TEM Investigation and FBB Model Explanation to the Phase Relationships between(1)

合集下载

课堂版How to write a body part

课堂版How to write a body part
To describe what was done by referring to existing methods in the literature.
Key
7 This method obtains a precipitate through the addition of BaCl2.2H2O; the resulting precipitate can be washed and stored easily.
6 Samples 10–18 were prepared in our laboratory using a revised version of the precipitation method established by the ISF Institute in Germany.6 7 This method obtains a precipitate through the addition of BaCl2.2H2O; the resulting precipitate can be washed and stored easily. 8 The samples were subsequently shipped to ISF for analysis by accelerator mass spectrometry (AMS). 9 All tubing used was stainless steel, and although two samples were at risk of CFC contamination as a result of brief contact with plastic, variation among samples was negligible.
Your Task: Building a Model

Bosch Access Professional Edition 2.0说明书

Bosch Access Professional Edition 2.0说明书

The introduction of access control in a company is often done in several small steps. At first only a few central areas are monitored. As system needs expands, access control may become part of an integrated security system. Access Professional Edition 2.0 (Access PE) allows you to start small. Your investment is always safeguarded, because it supports the same controllers as Bosch’s large scale project based systems. If you need to expand your system at a later date, the door leading to an integrated management system including AMC access control is wide open.System OverviewAccess PE provides self contained access control for small and medium-sized companies. Its installation is extremely simple and fast. There is no need to install and administer a database system. The system is easy to use and enables a person with minimal training to create badges and administer access rights in a very short time. Specific event logging and reporting allows you to trace who was when and where on your company’s grounds.You can choose your readers and cards from the same bundle of devices, which are supported by Bosch’s medium to large scale BIS/ACE Access Engine systems, thereby safeguarding your investment for future extensions. Card personalization is included in Access PE, providing you with the means to design your badges, to perform image acquisition with USB-cameras and to use most commercially available Card Printers.In addition to given standardized door models it is possible to use extended I/O functionality to exert influence on diverse conditions while monitoring inputs or setting outputs.The Access PE can be extended with two modules. A Video System on the one side and an Offline Locking System [selected markets, only] on the other side.The Offline Locking System (OLS) is integrated into the Access PE – so a person can use the same card for both systems.Through integrated video functionality and using the wide range of Bosch IP devices an operator for example can do a video verification by comparing live streams with the database image of the person requesting for access. Additional surveillance of the back and front of the person saves your company for unauthorized access.Access Professional Edition 2.0▶Compact access control based on Bosch’s innovativeAMC controller family▶Integrated Video Verification▶Multi-user system with definable user profiles▶Attendance tableau▶Online swipe▶Easy upgrade to BIS/ACE through import of existingcardholder and device data▶Includes Badge Designer for Photo ID Badge Printing▶Integrated Offline Locking System (OLS) [selectedmarkets, only]2Access PE runs on a standard PC with a Windows operating system (2003, XP, or Vista). This can be a single workstation computer or a client-server system with a central server and remote workstations.NoteMicrosoft Windows XP Professional is required for any video integration.FunctionsPos.Description1Server with Access PE 2.0 software 2Workstations 3Printer4AMC2 – access controller 5Reader 6Door strikesThe Access PE system, along with AMC access controllers,offers the following features:•A wide range of pre-defined and self-explaining door models allow fast and easy hardware configuration through one-click selection of the door type, such as standard door, turnstile, elevator, with entrance / exit reader, and so on.•Real-time activation of reader and cardholder configurations in the access controllers.•Time zone support for AMC - allows installations across different time zones.•Time schedules for time-based access permission defined per weekday including the definition of extra days, holidays, etc..•Time schedules for automatic activation/deactivation of cardholder settings, such as access rules, pin codes and so on.•Time schedules for automatic activation/deactivation of system settings, such as setting door status to permanent open from 9 a.m. to 5 p.m.•Online swipe – verification via database image automatically provided through access request.•Three PIN code types can be used for persons:-Verification PIN with alarm code feature -Identification PIN – substitudes a card -IDS-PIN to arm special areas•Choose between the following access modes -Card only -PIN only -PIN or card•Temporary activation/blocking of cardholders, either manually or time controlled.•Definable input fields for cardholder information.•Anti-Passback•Card personalization for importing cardholder images and creating your individual corporate badge designs printable on standard card printers.•Up to three cards can be assigned to a person.•Creation of an acknowledgment of receipt together with the print of the picture and the badge design.•Creation of logical areas, which could be single rooms,groups of rooms, whole floors or parking lots, where access control points could be assigned to.•Full archival and restoral of system data.•Using the Bosch controller family AMC digital and monitored I/Os for additional control and monitoring functions or easy intrusion features.•Supported languages:-English -German -Dutch -Polish-Simplified Chinese -Russian -Spanish-Portuguese (Brazil)•Detailed event log for recording access events.•Integrated filter- and reporting features.•Export into standard CSV-format for further processing.•Comprehensive online help3VideoPos.DescriptionAdditional t o t he p ositions i n t he f irst s ystem o verview t he v ideo c omponents are included now.7Cameras 8DVR or encoderEntrances from special interest can be equipped with additional video devices providing different options. The integrated video functionality in Access PE allows to assign up to five cameras to a door, each to be used as an ID or surveillance camera. With video verification the security level is raised by comparing the person's database image with a live stream while the environment can be observed for tail-gating or possible threats.Any violation, such as door open time exceeds, will provide alarm images for further investigation. Storage of those alarm images and retrieval of alarm archives using Boschvideo storage systems are supported as well.Additional functionalities are:•Alarm verification – providing live images caused through typical alarms around a door.•Video playback from archives reporting on accessrequests or alarm situation with minimum search time.•Live video can be selected from single camera or 2x2matrix (quad mode).Offline Locking System (OLS) [selected markets, only]The OLS uses some components of the Online System. In the picture above the part with the grey background shows the devices which are used for both systems. On the left hand side there are devices of the Online-System and on the right devices of the OLS.Pos.Description 1Workstation2Server with configuration applications and the database 3Access control reader with write module 4Card – can be used for both systems 5AMC2 4R4 – access controller 6Access control reader7Dialog reader to read and write cards 8OLS terminalWith the OLS it is possible to integrate a large quantity of rooms which are far away or not so security relevant in the access control system. All data are stored in the samedatabase and the persons need only one card for both – the Online- and the Offline-System.4Special reader with write-module (3), which are usually used for access control in the Online System, renew data and validity dates on the cards.Note Writing OLS data onto cards can only be used with an AMC2 4R4. Systems with Wiegand-interface-controller (AMC 4W) have to beexpanded with at least one AMC2 4R4 and thecorresponded reader.Parts Included•Access Professional Edition Software•Card Personalization Software•Quick start guide•Documentation CDTechnical SpecificationsMax. number of active cardholders10,000Cards/person up to 3PIN codes 4 to 8 digitsPIN code types Verification PINIdentification PINArming PINDoor PINAccess modes Card onlyPIN onlyPIN or cardMax. number of time profiles255Max. number of access authorizations255Max. number of area/time authorizations255Max. number of access authorizations groups255Max. number of readers128Max. number of remote workstations16Max. number of I/O extension boards per AMC1VideoMax. number of cameras128 Identification camera1/entrance Surveillance cameras up to 4/entranceBack surveillance2/entranceFront surveillance2/entranceAlarm and logbook camera1/entranceOffline Locking System (OLS) [selected markets, only]Max. number of entrances1,000Time models15Periods/ time model4Special days10Ordering InformationAccess PE - Basic License1 Client, 16 Readers, 2,000 CardsASL-APE2P-BASEAccess PE - Extended License2 Clients, 64 Readers, 10,000 CardsASL-APE2P-BEXTAccess PE - Reader Expansion LicenseAdditional 16 readers – highest limit per sys-tem: 128 readersASL-APE2P-RDRAccess PE - Client Expansion LicenseOne additional client – highest limit per sys-tem: 16 clientsASL-APE2P-CLIAccess PE - Video Activation LicenseEnables video with 16 channelsASL-APE2P-VIDBAccess PE - Video Expansion LicenseAdditional 16 channels – highest limit per sys-tem: 128 channelsASL-APE2P-VIDEAccess PE - OLS Activation LicenseEnables Offline Locking System (OLS) with 16doorsASL-APE2P-OLSBAccess PE - OLS Expansion LicenseOne additional door for the Offline LockingSystem (OLS) – highest limit per system:1,000 doorsASL-APE2P-OLSEAmericas:Bosch Security Systems, Inc. 130 Perinton Parkway Fairport, New York, 14450, USA Phone: +1 800 289 0096 Fax: +1 585 223 9180***********************.com Europe, Middle East, Africa:Bosch Security Systems B.V.P.O. Box 800025600 JB Eindhoven, The NetherlandsPhone: + 31 40 2577 284Fax: +31 40 2577 330******************************Asia-Pacific:Robert Bosch (SEA) Pte Ltd, Security Systems11 Bishan Street 21Singapore 573943Phone: +65 6258 5511Fax: +65 6571 2698*****************************Represented by© Bosch Security Systems Inc. 2010 | Data subject to change without notice T6197545611 | Cur: en-US, V9, 4 Jul 2010。

TEM+investigations+on+layered+ternary+ceramics

TEM+investigations+on+layered+ternary+ceramics

•ReviewTEM Investigations on Layered Ternary CeramicsZhijun LIN1,2),Meishuan LI1)and Yanchun ZHOU1)†1)Shenyang National Laboratory for Materials Science,Institute of Metal Research,Chinese Academy of Sciences,Shenyang110016,China2)Graduate School of Chinese Academy of Sciences,Beijing100039,China[Manuscript received October23,2006]Layered ternary ceramics represent a new class of solids that combine the merits of both metals and ceramics.These unique properties are strongly related to their layered crystal structures and microstructures.The com-bination of atomic-resolution Z-contrast scanning transmission electron microscopy(STEM)and transmission electron microscopy(TEM),selected area electron diffraction(SAED),convergent beam electron diffraction (CBED)represents a powerful method to link microstructures of materials to macroscopic properties,al-lowing layered ternary ceramics to be investigated in an unprecedented detail.Microstructural information obtained using TEM is useful in understanding the formation mechanism,layered stacking characteristics,and defect structures for layered ternary ceramics down to atomic-scale level;and thus provides insight into under-standing the“Processing-Structure-Property”relationship of layered ternary ceramics.Transmission electron microscopic characterizations of layered ternary ceramics in Ti-Si-C,Ti-Al-C,Cr-Al-C,Zr-Al-C,Ta-Al-C and Ti-Al-N systems are reviewed.KEY WORDS:Layered ternary ceramics;MAX phase;TEM;STEM;Interfacial structure1.IntroductionIn1960s,Nowotny and co-workers[1–4]madefirst attempts on the exploration of a large group of car-bides and nitrides.They embarked on the investiga-tions of ternary carbides and nitrides including the H¨a gg or H–phases.A large number of ternary com-pounds were identified during that decade.Thirty years later,Barsoum[5]published a detailed review on a new class of solids called M n+1AX n phases(M is an early transition metal;A is an A-group element (mostly IIIA and IVA)and X is either C and/or N, n=1,2,or3).The MAX phases are described as thermodynamically stable nanolaminates,which are elastically stiff,electrically and thermally conductive, readily machinable,relatively soft,damage tolerant and thermal-shock resistant.Over the past decade, exciting progresses have been made in the synthesis, microstructure,property,and design of M n+1AX n phases.In the present review,we extend the do-main of M n+1AX n phases to layered ternary ceramics and highlight the microstructural characterizations of layered ternary ceramics using various transmission electron microscopy(TEM)techniques.Concrete ex-amples rather than generalized descriptions will be given in this paper.We hope that this approach will help the readers understand both the basic TEM con-cepts and ideas on investigations of layered ternary ceramics.This paper is structured as follows:the first section contains a brief introduction of layered ternary ceramics,together with a concise description of several TEM techniques including selected area electron diffraction(SAED),convergent beam elec-tron diffraction(CBED),high-resolution transmis-sion electron microscopy(HRTEM),scanning trans-mission electron microscopy(STEM),and electron energy loss spectroscopy(EELS).Section two deals with atomic-scale characterizations of layered ternary †Prof.,Ph.D.,to whom correspondence should be addressed, E-mail:yczhou@.ceramics using SAED,CBED,HRTEM,and STEM. The third section summarizes investigations on the formation mechanism of layered ternary ceramics mainly by SAED,HRTEM,and Z-contrast STEM. Finally,conclusions on microstructural characteriza-tions of layered ternary ceramics using TEM tech-niques are drawn and some future trends in thisfield are highlighted in the last section.1.1Introduction of layered ternary ceramicsLayered ternary ceramics represent an unusual family of compounds that combine both the mer-its of metals and ceramics.These ceramics cover M n+1AX n phases[5],layered ternary carbides in the T(T=Sc,Zr,Hf,U)-Al-C[6–20],and A(A=Al,Sc, Lu,Gd)-B-C[21–28]systems.The crystal structures of the M n+1AX n phases(M2AX,M3AX2,M4AX3), T2Al3C4,and A3BC3are displayed in Fig.1[1–5,7,10,25]. The crystal structures of M n+1AX n phases consist of two fundamental units,i.e.,the nonstoichiometric MX x unit and A atomic plane.The most pronounced difference between the crystal structures shown in Figs.1(a–c)is the number of M-layers within every two A-layers,i.e.,there are two,three,and four M-layers within every two A-layers in M2AX,M3AX2, M4AX3,respectively.Ternary T-Al-C compounds can be viewed as the intergrown structures consisting of two kinds of layers.One is the(T-C)m(m=1,2or 3)slab in the NaCl-type structure and the other con-sists of Al3C2unit in an arrangement similar to that of the binary aluminum carbide Al4C3[6,8].A-B-C com-pounds,on the other hand,generally contain a linear C-B-C unit,which plays an important role in deter-mining their mechanical performance[21–25].Owing to the unique crystal structures,layered ternary ceram-ics display advantageous attributes such as high mod-ulus,excellent thermal-shock and high-temperature oxidation resistance,and damage tolerance.Ti3SiC2[29–69],Ti3AlC2[67–82],and Ti2AlC[80–100] are representative layered ternary ceramics,which have been extensively investigated.To conserve space,Fig.1Crystal structures for:(a)M2AX,(b)M3AX2,(c)M4AX3,(d)T2Al3C4,(e)A3BC3[1–5,7,10,25]Table1Typical physical,mechanical,and chemical properties for Ti3AlC2,Ti2AlC,and TiC Compound Crystal Lattice Density Vickers Coefficient ofstructure parameters/nm/(g/cm3)hardness/GPa thermal expansion/K−1Ti3AlC2[5,71–73]Hexagonal a=0.3075, 4.25∼3.59.0×10−6(P63/mmc)c=1.8578Ti2AlC[5,83–86]Hexagonal a=0.3040, 4.11 2.8–48.2×10−6(P63/mmc)c=1.3600TiC[101]Cubic(F m¯3m)a=0.4332 4.9428.5–33.57.9×10−6 Compound Electrical Young s Shear Compressive Bending Oxidation Machinability conductivity modulus modulus strength strength resistance in air/Ω−1·m−1/GPa/GPa/MPa/MPaTi3AlC2[5,71–73]2.9×106,3.5×106297124764375Excellent up to1300◦C Excellent Ti2AlC[5,83–86] 4.4×106270116540275Excellent TiC[101] 1.4×106310–439751–1309220–690Severe(T≥1100◦C)PoorFig.2(a)Brightfield TEM image of the oxide scales formed on a Ti3AlC2substrate.The inner oxide scale is an Al2O3scale while the outer scale is a discontinuous TiO2layer,(b)and(c)composite SAED patterns taken at the Ti3AlC2/Al2O3interfaces.“α”and“H”denoteα-Al2O3and hexagonal Ti3AlC2[77],respectivelyonly a brief description of the research work on the Ti-Al-C system will be introduced here.Jeitschko et al.[2]identified Ti2AlC in the1960s while Pietzka and Schuster[70]first reported Ti3AlC2in1994.By incorporating Al into TiC,layered ternary Ti3AlC2 and Ti2AlC ceramics can be prepared.Table1 summarizes some fundamental properties of Ti3AlC2, Ti2AlC,and TiC[5,71–73,83–86,101].The most salient features for layered ternary Ti-Al-C compounds over binary TiC compound are the machinability,damage tolerance,and high-temperature oxidation resistance. Difficult machinability hinders the extensive applica-tions of traditional ceramics such as SiC and Al2O3. Ternary Ti-Al-C ceramics,on the other hand,can be easily machined using high-speed tool bits with-out lubrication or cooling.The easy machinability and damage tolerance originate from their low shear strength[91].The oxidation resistance of TiAl is not satisfying because no protective Al2O3scale forms during high-temperature oxidation process[102–105].However,it is interesting and surprising that,with much lower Al content,protectively continuous Al2O3scales form on Ti3AlC2and Ti2AlC surfaces during high tem-perature oxidation in air,which were identified us-ing scanning electron microscopes[73,86,90].A typical cross-sectional TEM image of a Ti3AlC2specimen ox-idized at1200◦C for2h is shown in Fig.2(a)[77].The scale is approximately3µm in thickness.The com-pacted scales are well bonded to the substrate.SAED analyses(not shown)indicate that the outermost ox-ide layer is discontinuous TiO2while the inner ox-ide layer is an intact Al2O3layer,which is in good agreement with previous SEM results[73,86,90].More-over,Ti3AlC2substrate and the Al2O3oxide scale satisfy the following two sets of orientation relation-ships(ORs)[77]:(0001)Ti3AlC2//(0001)Al2O3and[11¯20]Ti3AlC2//[¯12¯10]Al2O3(OR−1)(1)(0001)Ti3AlC2//(0001)Al2O3and[11¯20]Ti3AlC2//[1¯100]Al2O3(OR−2)(2) In addition,the measured adhesive strength of Ti3AlC2/Al2O3interface exceeds85MPa[82],which is generally higher than those between other ox-ide scales and corresponding underlying alloys substrates[106–108].The strong adhesion contributes to the good thermal cyclic stability of the oxide scales.It is reasonable that the compacted Al2O3 scale can efficiently protect the Ti3AlC2substrate and accounts for the excellent high-temperature oxidation resistance[73].Conversely,Barsoum et al.[80]claimed that the oxidation resistance of Ti3AlC2and Ti2AlC was not good because a mixed and non-protective ox-ide scale formed during the oxidation process.Their results should be interpreted with care.In the later work,Sundberg et al.[90]reported that the oxida-tion resistance of Ti2AlC was excellent and claimed that the results were consistent with those reported by Zhou and Wang[86].Wefind that the presence of impurities,such as TiC and TiAl,greatly dete-riorates the oxidation resistance of ternary Ti-Al-C ceramics.So microstructure,especially phase compo-sitions should be examined prior to oxidation tests. The exceptional properties of bulk Ti-Al-C ceramics stimulated and promoted the research work on thin films[81].Moreover,inspired by the success in Ti-Al-C system,attempts have been made to explore new material systems with desired properties in the Ti-Si(Al)-C[67–69],Ti-Al-N[109–114],Cr-Al-C[115–120],Ta-Al-C[121–125],Zr-Al-C[10–12,18–20],and Al-B-C[25–26] systems.1.2Brief introduction of several TEM techniquesBefore delving in the details of microstructural characterizations of layered ternary ceramics,it is instructive to have a basic understanding of several TEM techniques.TEM is one of the most power-ful and widely used analytical tools for characteriz-ing microstructures of materials.TEM greatly puts forward our understanding of materials by complet-ing the processing-structure-property links down to atomistic levels.It is now even possible to tailor the microstructure(and mesostructure)of materials to achieve specific sets of properties;the extraordi-nary abilities of modern TEM instruments provid-ing almost all of the structural,phase,and crystallo-graphic information allow us to accomplish this feat.A very complete and easily understanding textbook on TEM,written by Williams and Carter[126],cov-ersfields ranging from specimen preparation,imaging, and diffraction to spectroscopy.Spence[127]reviewed thefield of atomic-resolution transmission electron microscopy and its application to materials science.A recent sensible and tasteful review on STEM is avail-able in the publication by Varela et al.[128].1.2.1Selected-area electron diffraction and convergent-beam electron diffraction Diffraction patterns are the basis of all image formation in TEM, as well as all crystallographic analyses and defect characterizations.The SAED method for selecting an area is to place an aperture in thefirst image plane below the objective lens and then a pattern of sharp spots(or rings)is observed in the back fo-cal plane of the objective lens.In contrast,in the CBED technique,an incident convergent beam(in the range of0.1-0.5◦)is focused on the specimen and gives rise to a pattern with disks of intensity.The CBED pattern of disk contains a wealth of contrast detail,which is not presented in the sharp spots of SAED pattern.Besides the capability of more pre-cisely determining the lattice parameters of materials than the SAED method,CBED is a powerful tool to determine the point group,space group,and crys-tal system of various materials because the presence of inversion center,glide plane and screw axis can be experimentally distinguished[129].Literature[126] gave an overview of these two diffraction techniques. In the second section,a case on the determination of space group of ternary Zr-Al-C carbides by using SAED and CBED techniques will be presented.1.2.2High-resolution transmission electron mi-croscopy HRTEM is thefirst TEM technique, which is capable of atomic resolution observations. This method has been proved to be indispensable in determining microstructures of materials.Phase-contrast HRTEM uses a coherent imaging technique, and suffers from the fact that data cannot be directlyinverted to retrieve structural information.HRTEMimages are generally not directly interpretable due to the complex nature of the coherent imaging process. In addition,the ability to extract compositional infor-mation from HRTEM images is limited.That is,the major disadvantage of HRTEM is that the obtained image does not necessarily relate to the structure in a simple manner.Image contrast is a complicated function of focus and specimen thickness,even in an aberration-corrected TEM,which means that there is no unique image that gives the best view of the structure.Thus,it is particularly difficult to inves-tigate samples or defects with an unknown structure or composition,which are of course the most interest-ing.One of the most notable advancements over the past decade is the development of quantitative meth-ods to interpret phase-contrast HRTEM images such that atomic coordinates at defects can be quantita-tively determined[130,131].The most widely used indi-rect method for correlating the image intensity in an HRTEM image with the projected crystal structure (especially for defect structures such as interfaces) is the multislice image simulation procedure,which takes into account dynamical scattering effects.De-spite recent advances in quantitative HRTEM utiliz-ing large series of defocused images and/or extensive computation(e.g.see literature[126]),any composi-tional information obtained using HRTEM will always be indirect.However,HRTEM remains a valuable complement to the associated TEM-based analytical techniques.The second major electron imaging ad-vancement is high-angle annular darkfield(HAADF) STEM,in which incoherent atomic-scale images can be formed with a chemical sensitivity.1.2.3High angle annular darkfield or Z-contrast imaging There has been considerable interest in atomic resolution Z-contrast imaging(or HAADF imaging)over the last few years,particularly in its applications to interfaces[132]and other extended defects[133–135].Z-contrast imaging uses a small,con-vergent probe(<0.2nm in diameter)of afield emis-sion gun(FEG)in an STEM system and an annu-lar detector with a large inner radius,as shown in Fig.3.Z-contrast imaging removes coherent effects, and thus the imaging is incoherent.Consequently,the images are directly interpretable and are not subject to contrast reversals due to focus and/or thickness changes as that in HRTEM.This technique is there-fore gaining increasing popularity.Figure3presents another important aspect of the STEM-based tech-nique,EELS,which can be performed simultaneously at a high spatial resolution,thus allowing direct cor-relation between structure and chemistry at the sub-nanometer or even atomic length scale,as discussed further in detail.Because the detected signal is in-coherent using this technique,the intensity in a Z-contrast image,I(R),is given by the convolution of the incident probe intensity function,|P(R)|2,with the crystal object function,O(R):I(R)=|P(R)|2⊗O(R)(3) Even under dynamical diffraction conditions,it has been shown that s-type Bloch states,which are tightly bound to individual atomic columns,are pre-dominantly responsible for the image intensityunder Fig.3Schematic illustration of simultaneous Z-contrast and EELS in an STEM system[135]zone-axis imaging conditions[136].Consequently,the object function is localized on atomic columns.It should also be noted that the Rutherford scattering cross-sections to high angles are much greater for ele-ments with higher atomic number(Z),so that the intensity of a Z-contrast image scales with atomic number,produces peaks of intensity at the cation sites while yields almost no intensity at low-Z col-umn positions[137].Moreover,in the limit of Ruther-ford scattering,this goes as Z2,so the contrast in a Z-contrast image will also vary approximately as Z2. The exact contrast details depend on the experimen-tal set up and are affected by factors such as electron channeling or detector size.But over a fairly large ranges of defocus or sample thicknesses,the image will nonetheless represent the sample structure in a straightforward way.1.2.4X-ray energy dispersive spectroscopy (XEDS)XEDS is one of the most convenient and effective microscopically analytical techniques. Acquisition of the characteristic X-rays emitted by materials when illuminated by the electron beam provides a direct,quantitative measure of chemical compositions.XEDS has been the most extensively used technique for measuring segregation,starting with the pioneering studies of Doig and Flewitt[138], who demonstrated that monolayer-level segregation was detectable in thin foils.Today,with an FEG-STEM,the spatial resolution can be less than2nm and segregation can be quantified with a sensitiv-ity approaching0.01monolayer[139].A common ap-proach is to acquire a segregation profile by stepping the electron probe along a line perpendicular to theFig.4XEDS line-scanning plot of Ti across two Al2O3 grains with corresponding HAADF image shownin the inset[82]segregation area.Figure4shows an example of a plot of Ti segregation to the grain boundary within an Al2O3oxide scale,where the enhanced Ti level (about3-5nm in thickness)at the grain boundary is clearly observed with increasing signals across the boundary[82].Recent optimization of instruments has allowed the acquisition of compositional maps at a high spatial resolution(<2nm)and high sensitivity (<0.1monolayer)[140].Due to the fact that XEDS only detects∼1%X-ray,the signal is quite weak,es-pecially for the light elements.Secondly,the energy resolution of XEDS is about130eV,which is rela-tively high,and the light elements have serious over-lapping spectra.The XEDS detector is located as close as possi-ble to specimen.Regular holders have the additional disadvantage that the holder area around the speci-men is“deep”,which hinders the acquisition of signal. Thus,it is very difficult to exclude spurious X-rays, which are generated outside the area of interest,be-cause of the proximity of the detector.In order to reduce these X-rays,a proper low-background speci-men holder is strongly recommended.The situation is essential especially when quantitative analyses are carried out.1.2.5Electron energy loss spectroscopy (EELS)EELS is another TEM technique for microanalysis of materials.This method is signifi-cantly more appropriate for analyses of light elements (Z<11),as well as many transition metal and rare earth elements.A larger proportion of the inner-shell excitations can be recorded by an EELS spec-trometer than an XEDS detector.Most often the core-loss region of the EEL spectrum,corresponding to inner shell ionizations,is analyzed to obtain an insight into the chemical composition and electronic structure of materials[141–143].Compositional pro-files across the planar boundary can be performed to identify the presence of reaction phases and to determine the chemical width of an interface.Typ-ically,the compositional accuracy is in the order of5-10wt pct.The spatial resolution can be sub-nanometer when performed with a focused electron probe in an FEG-TEM.In fact,in an STEM system under channeling conditions,spectra can be obtained at an atomic resolution directly from the interface plane and specific atomic columns[144,145].Thus,it is particularly useful because EELS can provide ele-mental and electronic structure information down to the atomic length scale within an STEM.The ioniza-tion edge onsets and EELS near-edgefine structures (ELNES),which include information about the un-occupied density of states,are sensitive to the local coordination and electronic structure.The ELNES for many elements exhibits a shape characteristic of the valence and bond strength of atom.Detailed descriptions on this technique can be referenced to literature[128,146,147].2.Atomic Scale Microstructures of LayeredTernary CeramicsAll the specimens for TEM investigations in this paper were prepared using conventional methods,i.e., by slicing,gluing the specimen face to face(for cross-sectional specimens only),grinding,dimpling,andfi-nally ion milling to electron transparent.TEM ob-servations were performed using a200kV JEM-2010 TEM(JEOL,Japan)and a300kV Tecnai G2F30 TEM(FEI company,Netherland),which is equipped with an energy dispersive spectroscopic system,a high angle annular darkfield detector in an STEM system, and a post column EELS system.2.1Determination of space group using SAED andCBEDElectron diffraction is the basis of all TEM ing SAED analysis,Arunajatesan and Carim[32]firstly investigated the symmetry of Ti3SiC2.Morgiel et al.[35]investigated the mi-crostructures of Ti3SiC2-based ceramics.Rounded TiC and angular SiC inclusions,as well as TiSi2phase were identified.Farber et al.[51]determined the Burg-ers vector of dislocations in Ti3SiC2in tilting exper-iments.Thereafter,SAED plays an increasing role in determining the crystal structure,lattice param-eters,defect structure,and crystallographic orien-tations of layered ternary compounds.The CBED method,however,is less employed due to the difficul-ties in analyzing the experimental results.All crystal structures can be specified by a set of Bravais lattice vector( R)and a set of vectors describ-ing the positions of the basis atoms( r j)[148].In prac-tice,however,the number of basis atoms can be quite large and simply listing them is cumbersome.Space group is a simple and efficient way to overcome these difficulties.By acknowledging the symmetry of the atomic configuration,it is usually possible to distill the description of the basis down to a small number of parameters.Consequently,crystal structure data are always presented with reference to the space group or underlying symmetry of the structure.It is,there-fore,important to understand the space group of a crystal.X-ray diffraction(XRD)is usually used to deter-mine the space group of materials.However,XRD results may be misinterpreted when dealing with ma-terials containing impurities or defects because the spot size of an X-ray diffractometer is relatively large (typically at micrometer scale).At this point,TEM displays its advantage on microstructual domain anal-ysis since the spot size of a convergent electron beam can easily reach nanometer scale.Thefirst attemptFig.5SAED patterns of hexagonal ZrAl 3C 5with the electron beam parallel to the directions of [0001](a),[1¯210](b)and [1¯100](c),respectively,an SAED pattern with the orientation being positioned between [1¯210]and [1¯100](d),a CBED pattern showing the symmetry of a 6-fold rotation about the [0001]axis and also the symmetry of the mirror reflection across two independent planes (e),a CBED pattern showing the existence of a mirror plane on (0001)(f)[10]to investigate the space group of layered ternary ce-ramics using TEM was made by Arunajatesan and Carim [32].They reported the point group of Ti 3SiC 2as 6/mmm .Ma et al.[76]systematically investigated microstructures of Ti 3AlC 2and determined the space group of this compound as P63/mmc by combining SAED and CBED.They also illustrated the determi-nation of an impurity,TiAl 3,using SAED and CBED.Following the scheme of Ma et al.[76],the present authors clarified the space group of Zr 2Al 3C 4and Zr 3Al 3C 5.Using XRD analysis,the space group of Zr 2Al 3C 4was reported to be P 31c (C 43v )by Schuster andNowotny [7],P 63/mmc by Parth´e and Chabot [13],and P 63mc by Fukuda et al.[8].Similarly,Zr 3Al 3C 5was determined to have P 63/mmc [7,14]and P 63mc symmetry [6].Therefore,it is necessary to clarify the symmetry of both carbides.Figure 5(a–c)are SAED patterns which were indexed as [0001],[1¯210],and [1¯100]zone axes,respectively,of the hexagonal Zr 3Al 3C 5[10].From these patterns of low-indices ba-sic zone axes,the lattice parameters are derived as a =0.33nm and c =2.76nm,which are consistent with those determined from powder X-ray analyses [6,7,14].These SAED patterns are also informative for learn-ing extinction rules.It is noted that all reflections in the [1¯210]pattern appear,but the (000l )(l =odd)reflections in the [1¯100]pattern are absent,imply-ing the existence of a c glide plane.The appearance of the {000l }(l =odd)reflections in the [1¯210]pat-tern can be attributed to double diffraction [76,96,149].Figure 5(d)is an SAED pattern whose orientation ispositioned between [1¯210]and [1¯100].As shown in Fig.5(d),the (000l )reflections with l =odd are also absent,which indicates that there is a 63screw axis along the [0001]axis [76,96].Figure 5(e)is a CBED pattern,which was acquired from a Zr 3Al 3C 5grain along the [0001]zone axis.A 6-fold axis of rotational symmetry as well as two independent and mutually perpendicular mirror planes (each of them is repro-duced every 60◦by the action of the 6-fold axis)is observed.These two types of mirror planes are par-allel to the 6-fold axis.The symmetry is determined to be 6mm .In addition,the symmetry shown in the SAED [1¯210]pattern (Fig.5(b))is 2mm .A 6mm symmetry and a 2mm symmetry indicate a unique 6/mmm point group for Zr 3Al 3C 5.Figure 5(f)is a CBED pattern showing the existence of a mirror plane on (0001).A 6mm symmetry and a mirror plane on (0001)further confirm the 6/mmm point group for Zr 3Al 3C 5.A combination of the information derived from these SAED and CBED patterns indicats that Zr 3Al 3C 5has a space group P 63/mmc ,which is in good correspondence with the results of Parth´e andChabot [13].The symmetry of Zr 2Al 3C 4was also in-vestigated using SAED and CBED.The lattice pa-rameters obtained are a =0.33nm and c =2.24nm,which agree well with previously reported data [7,14].Zr 2Al 3C 4was also determined to have a point group 6/mmm and a space group P 63/mmc ,which is in consistent with that reported by Mikhalenko et al.[14].The SAED and CBED patterns of Zr 2Al 3C 4areFig.6HRTEM image of Ti2AlC with the electron beam parallel to the[1¯210]direction[96]similar to those of Zr3Al3C5,and therefore,are not shown for brevity.More recently,wefind that the reason for the dis-crepancy in the space group determined using XRD might originate from the impurities and defects es-pecially stacking faults in the as-prepared layered ternary ceramics.In other words,XRD analyses col-lect the reflections from a relatively large area;both the impurities and defects in the specimens contribute to the XRD profile.Consequently,the results may be misinterpreted because some reflections are overlap-ping.On the other hand,TEM analysis can concen-trate on an individual grain without defects(single-crystal).Thus,the TEM results are more accu-rate at this point.The presently determined space group for Zr3Al3C5is also supported byfirst princi-ples calculations[12].2.2HRTEM investigations of layered ternary ceramicsHRTEM is a conventional and useful method in exploring the microstructural features of materials. This technique enables the investigations of ceram-ics with an atomic scale resolution.The most recent major discovery was the observation and synthesis of bucky-tubes,which resulted entirely from HRTEM observation[150].As far as layered ternary ceramics are concerned,HRTEM is a useful tool to character-ize both layered stacking characteristics and interfa-cial microstructures.The layered stacking characteristics of most lay-ered ternary ceramics(hexagonal structure)can be observed in an HRTEM image when viewed along the[1¯210]direction.Experimental HRTEM image of Ti2AlC,with the electron beam parallel to this zone axis,is displayed in Fig.6[96].As mentioned above, HRTEM imaging is sensitive to experimental param-eters.So,image simulations of the Ti2AlC crystal structure were performed using the Cerius2software (Molecular Simulation Inc.,USA)in a wide range of defocus and thickness permutations.Simulations show that there is one-to-one correspondence between the Ti and Al atomic columns and the bright spots in the image.This allows the interpretation of stacking sequence through image contrast.The image fringes with a periodicity of1.36nm along[0001]direction can be seen.The C atoms cannot be resolved in our microscopes.Taking Ti and Al into account,the im-ages can be described as a layered stacking with the sequence of ABABAB(the underlined letters denote the Al layers and the remained letters represent Ti layers)along[0001]direction.This type of stacking sequence for Ti and Al atoms is consistent with pre-vious results,which were derived from powder XRD analyses[1].There are cases that bright spots in HRTEM im-ages do not correspond to atomic columns.For ex-ample,Farber et al.[52]observed different stacking characteristics for Ti3SiC2in an HRTEM image and concluded that polymorphic transformation occurred during the ion-milling process.On the other hand, Yu et al.[42]reported that bright spots in HRTEM images did not correspond to the atomic columns. By combining image simulations and HRTEM imag-ing,they also identified two polymorphs,i.e.,α-and β-Ti3SiC2and claimed that theα-phase was more stable.The subsequent theoretical investigations us-ingfirst principles calculations are noteworthy[41]. The phase transition path fromα-toβ-Ti3SiC2was clearly illustrated,and the lattice dynamic of these two polymorphs was investigated.Theoretical calcu-lations revealed thatα-Ti3SiC2is more stable than theβ-phase.HRTEM imaging is also effective in under-standing the interfacial structure of layered ternary ing this method,the interfacial structures of Ti3SiC2/TiC[39,44–46,58],Ti5Si3/TiC[44], Ti3AlC2/Al2O3[77],Ti3AlC2/TiC[76,81], Ti2AlC/TiAl[96],and Ti2AlC/TiC[81,96]were investi-gated.The following shows an example of character-izing the Ta2AlC/TaC interface[124].XEDS anal-ysis shows that minor amount of cubic TaC was occasionally observed in Ta2AlC.In order to de-termine the crystallographic relationship between these two carbides,high-resolution imaging was conducted.Figure7(a)shows an HRTEM im-age of the interfacial structure between Ta2AlC and TaC[124].It is seen that TaC forms a coher-ent interface with Ta2AlC and the crystallographic orientation relationship between TaC and Ta2AlC can be described as:(111)TaC//(0001)Ta2AlC and [1¯10]TaC//[1¯210]Ta2AlC.In order to better un-derstand the structural relationship between TaC and Ta2AlC,an interfacial structural model based on the observed orientation relationship is pro-posed and shown in Fig.7(b).The Ta-C units of Ta2AlC are locally the same as those of TaC, which ensures a coherent interfacial structure be-tween TaC and bining the orien-tation relationships between binary cubic car-bides and layered ternary carbides identified in the Ti-Si-C and Ti-Al-C systems[44–46,76,81,89,96], a generalized orientation relationship between binary cubic MX carbides/nitrides and lay-ered ternary M x A y X z-phases carbides/nitrides is proposed as:[1¯10]MX//[1¯210]M x A y X z and (111)MX//(0001)M x A y X z(where M is an early transition metal element;A is a IIIA and IV group element;X is carbon or nitrogen,and x,y,z are。

市场调查方法(英文版)第十五章

市场调查方法(英文版)第十五章

15–14
EXHIBIT 15.2 Independent Samples t-Test Results
© 2007 Thomson/South-Western. All rights reserved.
15–15
What Is ANOVA?
• Analysis of Variance (ANOVA)
➢ An analysis involving the investigation of the effects of one treatment variable on an interval-scaled dependent variable
➢ A hypothesis-testing technique to determine whether statistically significant differences in means occur between two or more groups.
❖ Behavior, characteristics, beliefs, opinions, emotions, or attitudes
• Bivariate Tests of Differences
➢ Involve only two variables: a variable that acts like a dependent variable and a variable that acts as a classification variable.
❖ Differences in mean scores between groups or in comparing how two groups’ scores are distributed across possible response categories.

单壁碳纳米管储氢的统计理论模型

单壁碳纳米管储氢的统计理论模型

单壁碳纳米管储氢的统计理论模型张帆【摘要】Hydrogen energy is an environmentally friendly and renewable energy source. The development and application of hydrogen energy will bring great changes for the structure of energy sources from the long view. Carbon nanotubes were reported to be very promising materials for storing hydrogen form some research finding, which has been a hot spot in the applied research field of studying nano materials. Although many experimental results for hydrogen storage in carbon nanotubes were reported, corresponding theoretical investigation of adsorption mechanisms have almost not developed and it is difficult to find the theoretical equation of hydrogen storage quantity in particular. In this paper, statistical theory model on the basis of interaction between hydrogen molecules and carbon atoms was presented, and the formula of hydrogen storage quantity was obtained, which is almost in agree with the experiment value. The conclusion can provide theoretical reference for studying hydrogen storage in carbon nanotubes.%氢能是一种洁净的可再生的能源,从长远的观点看,氢能的发展与利用能够使能源结构发生重大变化。

Understanding individual human mobility patterns

Understanding individual human mobility patterns

a r X i v :0806.1256v 1 [p h y s i c s .s o c -p h ] 7 J u n 2008Understanding individual human mobility patternsMarta C.Gonz´a lez,1,2C´e sar A.Hidalgo,1and Albert-L´a szl´o Barab´a si 1,2,31Center for Complex Network Research and Department of Physics and Computer Science,University of Notre Dame,Notre Dame IN 46556.2Center for Complex Network Research and Department of Physics,Biology and Computer Science,Northeastern University,Boston MA 02115.3Center for Cancer Systems Biology,Dana Farber Cancer Institute,Boston,MA 02115.(Dated:June 7,2008)Despite their importance for urban planning [1],traffic forecasting [2],and the spread of biological [3,4,5]and mobile viruses [6],our understanding of the basic laws govern-ing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals.Here we study the trajectory of 100,000anonymized mobile phone users whose position is tracked for a six month period.We find that in contrast with the random trajectories predicted by the prevailing L´e vy flight and random walk models [7],human trajectories show a high degree of temporal and spatial regularity,each individual being characterized by a time independent characteristic length scale and a significant prob-ability to return to a few highly frequented locations.After correcting for differences in travel distances and the inherent anisotropy of each trajectory,the individual travel patterns collapse into a single spatial probability distribution,indicating that despite the diversity of their travel history,humans follow simple reproducible patterns.This inherent similarity in travel patterns could impact all phenomena driven by human mobility,from epidemic prevention to emergency response,urban planning and agent based modeling.Given the many unknown factors that influence a population’s mobility patterns,ranging from means of transportation to job and family imposed restrictions and priorities,human trajectories are often approximated with various random walk or diffusion models [7,8].Indeed,early mea-surements on albatrosses,bumblebees,deer and monkeys [9,10]and more recent ones on marine predators [11]suggested that animal trajectory is approximated by a L´e vy flight [12,13],a random walk whose step size ∆r follows a power-law distribution P (∆r )∼∆r −(1+β)with β<2.While the L´e vy statistics for some animals require further study [14],Brockmann et al.[7]generalized this finding to humans,documenting that the distribution of distances between consecutive sight-ings of nearly half-million bank notes is fat tailed.Given that money is carried by individuals, bank note dispersal is a proxy for human movement,suggesting that human trajectories are best modeled as a continuous time random walk with fat tailed displacements and waiting time dis-tributions[7].A particle following a L´e vyflight has a significant probability to travel very long distances in a single step[12,13],which appears to be consistent with human travel patterns:most of the time we travel only over short distances,between home and work,while occasionally we take longer trips.Each consecutive sightings of a bank note reflects the composite motion of two or more indi-viduals,who owned the bill between two reported sightings.Thus it is not clear if the observed distribution reflects the motion of individual users,or some hitero unknown convolution between population based heterogeneities and individual human trajectories.Contrary to bank notes,mo-bile phones are carried by the same individual during his/her daily routine,offering the best proxy to capture individual human trajectories[15,16,17,18,19].We used two data sets to explore the mobility pattern of individuals.Thefirst(D1)consists of the mobility patterns recorded over a six month period for100,000individuals selected randomly from a sample of over6million anonymized mobile phone users.Each time a user initiates or receives a call or SMS,the location of the tower routing the communication is recorded,allowing us to reconstruct the user’s time resolved trajectory(Figs.1a and b).The time between consecutive calls follows a bursty pattern[20](see Fig.S1in the SM),indicating that while most consecutive calls are placed soon after a previous call,occasionally there are long periods without any call activity.To make sure that the obtained results are not affected by the irregular call pattern,we also study a data set(D2)that captures the location of206mobile phone users,recorded every two hours for an entire week.In both datasets the spatial resolution is determined by the local density of the more than104mobile towers,registering movement only when the user moves between areas serviced by different towers.The average service area of each tower is approximately3km2 and over30%of the towers cover an area of1km2or less.To explore the statistical properties of the population’s mobility patterns we measured the dis-tance between user’s positions at consecutive calls,capturing16,264,308displacements for the D1and10,407displacements for the D2datasets.Wefind that the distribution of displacements over all users is well approximated by a truncated power-lawP(∆r)=(∆r+∆r0)−βexp(−∆r/κ),(1)withβ=1.75±0.15,∆r0=1.5km and cutoff valuesκ|D1=400km,andκ|D2=80km(Fig.1c,see the SM for statistical validation).Note that the observed scaling exponent is not far fromβB=1.59observed in Ref.[7]for bank note dispersal,suggesting that the two distributions may capture the same fundamental mechanism driving human mobility patterns.Equation(1)suggests that human motion follows a truncated L´e vyflight[7].Yet,the observed shape of P(∆r)could be explained by three distinct hypotheses:A.Each individual follows a L´e vy trajectory with jump size distribution given by(1).B.The observed distribution captures a population based heterogeneity,corresponding to the inherent differences between individuals.C.A population based heterogeneity coexists with individual L´e vy trajectories,hence(1)represents a convolution of hypothesis A and B.To distinguish between hypotheses A,B and C we calculated the radius of gyration for each user(see Methods),interpreted as the typical distance traveled by user a when observed up to time t(Fig.1b).Next,we determined the radius of gyration distribution P(r g)by calculating r g for all users in samples D1and D2,finding that they also can be approximated with a truncated power-lawP(r g)=(r g+r0g)−βr exp(−r g/κ),(2) with r0g=5.8km,βr=1.65±0.15andκ=350km(Fig.1d,see SM for statistical validation). L´e vyflights are characterized by a high degree of intrinsic heterogeneity,raising the possibility that(2)could emerge from an ensemble of identical agents,each following a L´e vy trajectory. Therefore,we determined P(r g)for an ensemble of agents following a Random Walk(RW), L´e vy-Flight(LF)or Truncated L´e vy-Flight(T LF)(Figure1d)[8,12,13].Wefind that an en-semble of L´e vy agents display a significant degree of heterogeneity in r g,yet is not sufficient to explain the truncated power law distribution P(r g)exhibited by the mobile phone users.Taken together,Figs.1c and d suggest that the difference in the range of typical mobility patterns of indi-viduals(r g)has a strong impact on the truncated L´e vy behavior seen in(1),ruling out hypothesis A.If individual trajectories are described by a LF or T LF,then the radius of gyration should increase in time as r g(t)∼t3/(2+β)[21,22]while for a RW r g(t)∼t1/2.That is,the longer we observe a user,the higher the chances that she/he will travel to areas not visited before.To check the validity of these predictions we measured the time dependence of the radius of gyration for users whose gyration radius would be considered small(r g(T)≤3km),medium(20<r g(T)≤30km)or large(r g(T)>100km)at the end of our observation period(T=6months).Theresults indicate that the time dependence of the average radius of gyration of mobile phone users is better approximated by a logarithmic increase,not only a manifestly slower dependence than the one predicted by a power law,but one that may appear similar to a saturation process(Fig.2a and Fig.S4).In Fig.2b,we have chosen users with similar asymptotic r g(T)after T=6months,and measured the jump size distribution P(∆r|r g)for each group.As the inset of Fig.2b shows,users with small r g travel mostly over small distances,whereas those with large r g tend to display a combination of many small and a few larger jump sizes.Once we rescale the distributions with r g(Fig.2b),wefind that the data collapses into a single curve,suggesting that a single jump size distribution characterizes all users,independent of their r g.This indicates that P(∆r|r g)∼r−αg F(∆r/r g),whereα≈1.2±0.1and F(x)is an r g independent function with asymptotic behavior F(x<1)∼x−αand rapidly decreasing for x≫1.Therefore the travel patterns of individual users may be approximated by a L´e vyflight up to a distance characterized by r g. Most important,however,is the fact that the individual trajectories are bounded beyond r g,thus large displacements which are the source of the distinct and anomalous nature of L´e vyflights, are statistically absent.To understand the relationship between the different exponents,we note that the measured probability distributions are related by P(∆r)= ∞0P(∆r|r g)P(r g)dr g,whichsuggests(see SM)that up to the leading order we haveβ=βr+α−1,consistent,within error bars, with the measured exponents.This indicates that the observed jump size distribution P(∆r)is in fact the convolution between the statistics of individual trajectories P(∆r g|r g)and the population heterogeneity P(r g),consistent with hypothesis C.To uncover the mechanism stabilizing r g we measured the return probability for each indi-vidual F pt(t)[22],defined as the probability that a user returns to the position where it was first observed after t hours(Fig.2c).For a two dimensional random walk F pt(t)should follow ∼1/(t ln(t)2)[22].In contrast,wefind that the return probability is characterized by several peaks at24h,48h,and72h,capturing a strong tendency of humans to return to locations they visited before,describing the recurrence and temporal periodicity inherent to human mobility[23,24].To explore if individuals return to the same location over and over,we ranked each location based on the number of times an individual was recorded in its vicinity,such that a location with L=3represents the third most visited location for the selected individual.Wefind that the probability offinding a user at a location with a given rank L is well approximated by P(L)∼1/L, independent of the number of locations visited by the user(Fig.2d).Therefore people devote mostof their time to a few locations,while spending their remaining time in5to50places,visited with diminished regularity.Therefore,the observed logarithmic saturation of r g(t)is rooted in the high degree of regularity in their daily travel patterns,captured by the high return probabilities(Fig.2b) to a few highly frequented locations(Fig.2d).An important quantity for modeling human mobility patterns is the probabilityΦa(x,y)tofind an individual a in a given position(x,y).As it is evident from Fig.1b,individuals live and travel in different regions,yet each user can be assigned to a well defined area,defined by home and workplace,where she or he can be found most of the time.We can compare the trajectories of different users by diagonalizing each trajectory’s inertia tensor,providing the probability offinding a user in a given position(see Fig.3a)in the user’s intrinsic reference frame(see SM for the details).A striking feature ofΦ(x,y)is its prominent spatial anisotropy in this intrinsic reference frame(note the different scales in Fig3a),and wefind that the larger an individual’s r g the more pronounced is this anisotropy.To quantify this effect we defined the anisotropy ratio S≡σy/σx, whereσx andσy represent the standard deviation of the trajectory measured in the user’s intrinsic reference frame(see SM).Wefind that S decreases monotonically with r g(Fig.3c),being well approximated with S∼r−ηg,forη≈0.12.Given the small value of the scaling exponent,other functional forms may offer an equally goodfit,thus mechanistic models are required to identify if this represents a true scaling law,or only a reasonable approximation to the data.To compare the trajectories of different users we remove the individual anisotropies,rescal-ing each user trajectory with its respectiveσx andσy.The rescaled˜Φ(x/σx,y/σy)distribution (Fig.3b)is similar for groups of users with considerably different r g,i.e.,after the anisotropy and the r g dependence is removed all individuals appear to follow the same universal˜Φ(˜x,˜y)prob-ability distribution.This is particularly evident in Fig.3d,where we show the cross section of ˜Φ(x/σ,0)for the three groups of users,finding that apart from the noise in the data the curves xare indistinguishable.Taken together,our results suggest that the L´e vy statistics observed in bank note measurements capture a convolution of the population heterogeneity(2)and the motion of individual users.Indi-viduals display significant regularity,as they return to a few highly frequented locations,like home or work.This regularity does not apply to the bank notes:a bill always follows the trajectory of its current owner,i.e.dollar bills diffuse,but humans do not.The fact that individual trajectories are characterized by the same r g-independent two dimen-sional probability distribution˜Φ(x/σx,y/σy)suggests that key statistical characteristics of indi-vidual trajectories are largely indistinguishable after rescaling.Therefore,our results establish the basic ingredients of realistic agent based models,requiring us to place users in number propor-tional with the population density of a given region and assign each user an r g taken from the observed P(r g)ing the predicted anisotropic rescaling,combined with the density function˜Φ(x,y),whose shape is provided as Table1in the SM,we can obtain the likelihood offinding a user in any location.Given the known correlations between spatial proximity and social links,our results could help quantify the role of space in network development and evolu-tion[25,26,27,28,29]and improve our understanding of diffusion processes[8,30].We thank D.Brockmann,T.Geisel,J.Park,S.Redner,Z.Toroczkai and P.Wang for discus-sions and comments on the manuscript.This work was supported by the James S.McDonnell Foundation21st Century Initiative in Studying Complex Systems,the National Science Founda-tion within the DDDAS(CNS-0540348),ITR(DMR-0426737)and IIS-0513650programs,and the U.S.Office of Naval Research Award N00014-07-C.Data analysis was performed on the Notre Dame Biocomplexity Cluster supported in part by NSF MRI Grant No.DBI-0420980.C.A.Hi-dalgo acknowledges support from the Kellogg Institute at Notre Dame.Supplementary Information is linked to the online version of the paper at /nature.Author Information Correspondence and requests for materials should be addressed to A.-L.B.(e-mail:alb@)[1]Horner,M.W.&O’Kelly,M.E.S Embedding economies of scale concepts for hub networks design.Journal of Transportation Geography9,255-265(2001).[2]Kitamura,R.,Chen,C.,Pendyala,R.M.&Narayaran,R.Micro-simulation of daily activity-travelpatterns for travel demand forecasting.Transportation27,25-51(2000).[3]Colizza,V.,Barrat,A.,Barth´e l´e my,M.,Valleron,A.-J.&Vespignani,A.Modeling the WorldwideSpread of Pandemic Influenza:Baseline Case and Containment Interventions.PLoS Medicine4,095-0110(2007).[4]Eubank,S.,Guclu,H.,Kumar,V.S.A.,Marathe,M.V.,Srinivasan,A.,Toroczkai,Z.&Wang,N.Controlling Epidemics in Realistic Urban Social Networks.Nature429,180(2004).[5]Hufnagel,L.,Brockmann,D.&Geisel,T.Forecast and control of epidemics in a globalized world.Proceedings of the National Academy of Sciences of the United States of America101,15124-15129 (2004).[6]Kleinberg,J.The wireless epidemic.Nature449,287-288(2007).[7] D.Brockmann,D.,Hufnagel,L.&Geisel,T.The scaling laws of human travel.Nature439,462-465(2006).[8]Havlin,S.&ben-Avraham,D.Diffusion in Disordered Media.Advances in Physics51,187-292(2002).[9]Viswanathan,G.M.,Afanasyev,V.,Buldyrev,S.V.,Murphy,E.J.,Prince,P.A.&Stanley,H.E.L´e vyFlight Search Patterns of Wandering Albatrosses.Nature381,413-415(1996).[10]Ramos-Fernandez,G.,Mateos,J.L.,Miramontes,O.,Cocho,G.,Larralde,H.&Ayala-Orozco,B.,L´e vy walk patterns in the foraging movements of spider monkeys(Ateles geoffroyi).Behavioral ecol-ogy and Sociobiology55,223-230(2004).[11]Sims D.W.et al.Scaling laws of marine predator search behaviour.Nature451,1098-1102(2008).[12]Klafter,J.,Shlesinger,M.F.&Zumofen,G.Beyond Brownian Motion.Physics Today49,33-39(1996).[13]Mantegna,R.N.&Stanley,H.E.Stochastic Process with Ultraslow Convergence to a Gaussian:TheTruncated L´e vy Flight.Physical Review Letters73,2946-2949(1994).[14]Edwards,A.M.,Phillips,R.A.,Watkins,N.W.,Freeman,M.P.,Murphy,E.J.,Afanasyev,V.,Buldyrev,S.V.,da Luz,M.G.E.,Raposo,E.P.,Stanley,H.E.&Viswanathan,G.M.Revisiting L´e vyflightsearch patterns of wandering albatrosses,bumblebees and deer.Nature449,1044-1049(2007). [15]Sohn,T.,Varshavsky,A.,LaMarca,A.,Chen,M.Y.,Choudhury,T.,Smith,I.,Consolvo,S.,High-tower,J.,Griswold,W.G.&de Lara,E.Lecture Notes in Computer Sciences:Proc.8th International Conference UbiComp2006.(Springer,Berlin,2006).[16]Onnela,J.-P.,Saram¨a ki,J.,Hyv¨o nen,J.,Szab´o,G.,Lazer,D.,Kaski,K.,Kert´e sz,K.&Barab´a si A.L.Structure and tie strengths in mobile communication networks.Proceedings of the National Academy of Sciences of the United States of America104,7332-7336(2007).[17]Gonz´a lez,M.C.&Barab´a si,plex networks:From data to models.Nature Physics3,224-225(2007).[18]Palla,G.,Barab´a si,A.-L.&Vicsek,T.Quantifying social group evolution.Nature446,664-667(2007).[19]Hidalgo C.A.&Rodriguez-Sickert C.The dynamics of a mobile phone network.Physica A387,3017-30224.[20]Barab´a si,A.-L.The origin of bursts and heavy tails in human dynamics.Nature435,207-211(2005).[21]Hughes,B.D.Random Walks and Random Environments.(Oxford University Press,USA,1995).[22]Redner,S.A Guide to First-Passage Processes.(Cambridge University Press,UK,2001).[23]Schlich,R.&Axhausen,K.W.Habitual travel behaviour:Evidence from a six-week travel diary.Transportation30,13-36(2003).[24]Eagle,N.&Pentland,A.Eigenbehaviours:Identifying Structure in Routine.submitted to BehavioralEcology and Sociobiology(2007).[25]Yook,S.-H.,Jeong,H.&Barab´a si A.L.Modeling the Internet’s large-scale topology.Proceedings ofthe Nat’l Academy of Sciences99,13382-13386(2002).[26]Caldarelli,G.Scale-Free Networks:Complex Webs in Nature and Technology.(Oxford UniversityPress,USA,2007).[27]Dorogovtsev,S.N.&Mendes,J.F.F.Evolution of Networks:From Biological Nets to the Internet andWWW.(Oxford University Press,USA,2003).[28]Song C.M.,Havlin S.&Makse H.A.Self-similarity of complex networks.Nature433,392-395(2005).[29]Gonz´a lez,M.C.,Lind,P.G.&Herrmann,H.J.A system of mobile agents to model social networks.Physical Review Letters96,088702(2006).[30]Cecconi,F.,Marsili,M.,Banavar,J.R.&Maritan,A.Diffusion,peer pressure,and tailed distributions.Physical Review Letters89,088102(2002).FIG.1:Basic human mobility patterns.a,Week-long trajectory of40mobile phone users indicate that most individuals travel only over short distances,but a few regularly move over hundreds of kilometers. Panel b,displays the detailed trajectory of a single user.The different phone towers are shown as green dots,and the V oronoi lattice in grey marks the approximate reception area of each tower.The dataset studied by us records only the identity of the closest tower to a mobile user,thus we can not identify the position of a user within a V oronoi cell.The trajectory of the user shown in b is constructed from186 two hourly reports,during which the user visited a total of12different locations(tower vicinities).Among these,the user is found96and67occasions in the two most preferred locations,the frequency of visits for each location being shown as a vertical bar.The circle represents the radius of gyration centered in the trajectory’s center of mass.c,Probability density function P(∆r)of travel distances obtained for the two studied datasets D1and D2.The solid line indicates a truncated power law whose parameters are provided in the text(see Eq.1).d,The distribution P(r g)of the radius of gyration measured for the users, where r g(T)was measured after T=6months of observation.The solid line represent a similar truncated power lawfit(see Eq.2).The dotted,dashed and dot-dashed curves show P(r g)obtained from the standard null models(RW,LF and T LF),where for the T LF we used the same step size distribution as the onemeasured for the mobile phone users.FIG.2:The bounded nature of human trajectories.a,Radius of gyration, r g(t) vs time for mobile phone users separated in three groups according to theirfinal r g(T),where T=6months.The black curves correspond to the analytical predictions for the random walk models,increasing in time as r g(t) |LF,T LF∼t3/2+β(solid),and r g(t) |RW∼t0.5(dotted).The dashed curves corresponding to a logarithmicfit of the form A+B ln(t),where A and B depend on r g.b,Probability density function of individual travel distances P(∆r|r g)for users with r g=4,10,40,100and200km.As the inset shows,each group displays a quite different P(∆r|r g)distribution.After rescaling the distance and the distribution with r g(main panel),the different curves collapse.The solid line(power law)is shown as a guide to the eye.c,Return probability distribution,F pt(t).The prominent peaks capture the tendency of humans to regularly return to the locations they visited before,in contrast with the smooth asymptotic behavior∼1/(t ln(t)2)(solid line)predicted for random walks.d,A Zipf plot showing the frequency of visiting different locations.The symbols correspond to users that have been observed to visit n L=5,10,30,and50different locations.Denoting with(L)the rank of the location listed in the order of the visit frequency,the data is well approximated by R(L)∼L−1. The inset is the same plot in linear scale,illustrating that40%of the time individuals are found at theirfirsttwo preferred locations.FIG.3:The shape of human trajectories.a,The probability density functionΦ(x,y)offinding a mobile phone user in a location(x,y)in the user’s intrinsic reference frame(see SM for details).The three plots, from left to right,were generated for10,000users with:r g≤3,20<r g≤30and r g>100km.The trajectories become more anisotropic as r g increases.b,After scaling each position withσx andσy theresulting˜Φ(x/σx,y/σy)has approximately the same shape for each group.c,The change in the shape of Φ(x,y)can be quantified calculating the isotropy ratio S≡σy/σx as a function of r g,which decreases as S∼r−0.12(solid line).Error bars represent the standard error.d,˜Φ(x/σx,0)representing the x-axis cross gsection of the rescaled distribution˜Φ(x/σx,y/σy)shown in b.。

EFECTIS发布新版建筑构件检测标准

EFECTIS发布新版建筑构件检测标准

为0.995,其中:Ts 为60 m in 后电热膜与棉被接触处的温 度,7\为环境温度。

可以认为,在汗蒸房、足浴房等环境温 度较高的场所,电热膜具有的引燃能力也较强。

进行火灾 原因调查时,可以利用该模型计算方法,模拟火场条件,初 步计算并判断电热膜引燃其周围可燃物的可能性。

4总结(1) 电热膜的引燃能力随着功率的增加而变强,当覆盖 物为棉被,试样功率为160 W 时,电热膜与棉被接触处最高 温度高于一般的可燃物的燃点;小于100 W 时,引燃能力较 差。

毛巾作为覆盖物时,所能达到的最高温度比棉被低 201左右。

(2)电热膜表面覆盖物的厚度越大,电热膜与覆盖物接触处温度越高,可燃物越容易被引燃。

毛巾作为覆盖物时, 若其厚度较低,其危险性比棉被低,而厚度较高时,与棉被 相当。

(3) 覆盖物所含水分会降低电热膜的引燃能力,含水率越高,电热膜的引燃能力越差。

(4)建立了模拟电热膜、覆盖物传热的物理模型,通过 与实验结果对比,验证了该模型的准确性。

参考文献:[1] 张洪识,治俭.低温辐射电热膜行业及市场发展研究概况[c ]//全国红外加热发展研讨会论文集,2015.[2] 周晓佳.汗蒸类场所电气火灾特点及预防对策分析[J].建筑安全,2017,32(6):61-63.[3] 赵荣华.低温辐射电热膜供暖系统作:住宅建筑中的利用[J ].科技与企业,2014,(14):191.[4] 胡明葸.碘钨灯引燃能力的实验研究与ANSYS 软件分析[J ].消防技术与产品信息,2017,(10):23_27.[5] 金静,张金专.碘钨灯引燃能力实验研究[J].消防科学与技术,2016,35(4):587-590.[6] 马迪,张金专.电暖器对毛毯和报纸的引燃能力研究[J ].武膂学院学报,2016,35(5):717—720.[7] 任松发,姜英.红外线式电暖器热辐射引起火灾的研究[J ].武膂学院学报,2000,16(1):40-43.[8] 杜宝相.一起电热膜汗蒸房火灾的认定[J].消防科学与技术,2014,33(1):114-116.[9] 马瑛.汗蒸养生房的电气防火[J].消防技术与产品信息,2013,(3):12-13.[10] 孙宁•.汗蒸房电热膜及电气线路故障起火原W 认定[J ].消防科学与技术,2014,33(8):975-977.[11] 张斌,鲁志宝,陈克,等.电热膜火灾危险性分析[.I].消防科学》彳技术,2018,37(6):851-854.[12] LU P B,CHENG F ,()U Y Y\,et al.A flexible and transparentthin film heater based on a carbon fiber/heat-resistant cellulose composite[J].Composites Science and Tecnology ,2017,153:1—6.[13] BORISOVA N V ,SLADKC)V () M ,ARTEMI*:NK() A A.Developingan electric heater design based on carbon fibres[J].Fibre Chemistry , 2007,39( 1 ):45-48.[14] CH1EN T H.TZO U I) Y, HU 八NG Z S ,e V u/.lligh performance326infrared heatersusing carbon fiber filamentsdecorated with alumi­na layer by microwave-assisted methodJJ].Journal of the Taiwan Institute of Chemical Engineers, 2016,59:521—525.[15]徐凯.基于Matlab 导热问题的数值模拟[J].上海丁.程大学学报,2016,30(4):354-358.Study on the ignition ability of metal-basedelectrothermal filmWANG Ji1, YANG Zan2, RAN Xue-qing3(1. China People's Police University, Hebei Langfang065000, China; 2. Harbin Fire and Rescue D ivision, H eilongji­ang Harbin 150000, China; 3. Zhangjiakou Fire Rescue Divi­sion, Hebei Zhangjiakou 075000, China)Abstract: In order to study the ignition ability o f m etal-based electrotherm al film , cotton quilt and towel are selected as com­bustible cover m aterials. The tem perature changes o f various po­sitions o f the cover material w ere studied experim entally, under the conditions o f different film power, covering thickness andm oisture content. A model was established. The tem perature of the place electrotherm al film connecting the cover at different am bient tem peratures w ere calculated by the m odel.The results o f this paper can provide som e reference data for fire preven­tion and fire investigation o f electrotherm al film fire.Key words: m etal- based electrotherm al film; ignition ability; flam m able cover; model calculation作者简介:王霁(1980—),女,河北石家庄人,中国 人民警察大学讲师,博士,主要从事消防材料学方面的研 究,河北省廊坊市安次区西昌路220号,065000。

外文翻译注塑模的单浇口优化

外文翻译注塑模的单浇口优化

Single gate optimization for plastic injection moldAbstract:Abstract: This paper deals with a methodology for single gate location optimization for plastic injection mold. The objective of the gate optimization is to minimize the warpage of injection molded parts, because warpage is a crucial quality issue for most injection molded parts while it is influenced greatly by the gate location. Feature warpage is defined as the ratio of maximum displacement on the feature surface to the projected length of the feature surface to describe part warpage. The optimization is combined with the numerical simulation technology to find the optimal gate location, in which the simulated annealing algorithm is used to search for the optimum. Finally, an example is discussed in the paper and it can be concluded that the proposed method is effective.Key words: Injection mold, Gate location, Optimization, Feature warpage.INTRODUCTIONPlastic injection molding is a widely used, com- plex but highly efficient technique for producing a large variety of plastic products, particularly those with high production requirement, tight tolerance, and complex shapes. The quality of injection molded parts is a function of plastic material, part geometry, mold structure and process conditions. The most important part of an injection mold basically is the following three sets of components: cavities, gates and runners, and cooling system.Lam and Seow (2000) and Jin and Lam (2002) achieved cavity balancing by varying the wall thick- ness of the part. A balance filling process within the cavity gives an evenly distributed pressure and tem- perature which can drastically reduce the warpage of the part. But the cavity balancing is only one of the important influencing factors of part qualities. Espe- cially, the part has its functional requirements, and its thicknesses should not be varied usually.From the pointview of the injection mold design, a gate is characterized by its size and location, and the runner system by the size and layout. The gate size and runner layout are usually determined as constants. Relatively, gate locations and runner sizes are more flexible, which can be varied to influence the quality of the part. As a result, they are often the design pa- rameters for optimization.Lee and Kim (1996a) optimized the sizes of runners and gates to balance runner system for mul- tiple injection cavities. The runner balancing was described as the differences of entrance pressures for a multi-cavity mold with identical cavities, and as differences of pressures at theend of the melt flow path in each cavity for a family mold with different cavity volumes and geometries. The methodology has shown uniform pressure distributions among the cavities during the entire molding cycle of multiple cavities mold.Zhai et al.(2005a) presented the two gate loca- tion optimization of one molding cavity by an effi- cient search method based on pressure gradient (PGSS), and subsequently positioned weld lines to the desired locations by varying runner sizes for multi-gate parts (Zhai et al., 2006). As large-volume part, multiple gates are needed to shorten the maxi- mum flow path, with a corresponding decrease in injection pressure. The method is promising for de- sign of gates and runners for a single cavity with multiple gates.Many of injection molded parts are produced with one gate, whether in single cavity mold or in multiple cavities mold. Therefore, the gate location of a single gate is the most common design parameter for optimization. A shape analysis approach was pre- sented by Courbebaisse and Garcia (2002), by which the best gate location of injection molding was esti- mated. Subsequently, they developed this methodol- ogy further and applied it to single gate location op- timization of an L shape example (Courbebaisse,2005). It is easy to use and not time-consuming, while it only serves the turning of simple flat parts with uniform thickness.Pandelidis and Zou (1990) presented the opti- mization of gate location, by indirect quality measures relevant to warpage and material degradation, which is represented as weighted sum of a temperature dif- ferential term, an over-pack term, and a frictional overheating term. Warpage is influenced by the above factors, but the relationship between them is not clear. Therefore, the optimization effect is restricted by the determination of the weighting factors.Lee and Kim (1996b) developed an automated selection method of gate location, in which a set of initial gate locations were proposed by a designer and then the optimal gate was located by the adjacent node evaluation method. The conclusion to a great extent depends much on the human design er’s in tuition, because the first step of the method is based on the desi gner’s proposition. So the result is to a large ex- tent limited to the designer’s experience.Lam and Jin (2001) developed a gate location optimization method based on the minimization of the Standard Deviation of Flow Path Length (SD[L]) and Standard Deviation of Filling Time (SD[T]) during the molding filling process. Subsequently, Shen et al.(2004a; 2004b) optimized the gate location design by minimizing the weighted sum of filling pressure, filling time difference between different flow paths, temperature difference, and over-pack percentage. Zhai et al.(2005b) investigated optimal gate location with evaluation criteria of injection pressure at the end of filling. These researchers presented the objec- tive functions asperformances of injection molding filling operation, which are correlated with product qualities. But the correlation between the perform- ances and qualities is very complicated and no clear relationship has been observed between them yet. It is also difficult to select appropriate weighting factors for each term.A new objective function is presented here to evaluate the warpage of injection molded parts to optimize gate location. To measure part quality di- rectly, this investigation defines feature warpage to evaluate part warpage, which is evaluated from the “flow plus warpage” simulation outputs of Moldflow Plastics Insight (MPI) software. The objective func- tion is minimized to achieve minimum deformation in gate location optimization. Simulated annealing al- gorithm is employed to search for the optimal gate location. An example is given to illustrate the effec- tivity of the proposed optimization procedure.QUALITY MEASURES: FEATURE WARPGEDefinition of feature warp ageTo apply optimization theory to the gate design, quality measures of the part must be specified in the first instance. The term “quality” may be referred to many product properties, such as mechanical, thermal, electrical, optical, ergonomical or geometrical prop- erties. There are two types of part quality measures: direct and indirect. A model that predicts the proper- ties from numerical simulation results would be characterized as a direct quality measure. In contrast, an indirect measure of part quality is correlated with target quality, but it cannot provide a direct estimate of that quality.For warpage, the indirect quality measures in related works are one of performances of injection molding flowing behavior or weighted sum of those. The performances are presented as filling time dif- ferential along different flow paths, temperature dif- ferential, over-pack percentage, and so on. It is ob- vious that warpage is influenced by these perform- ances, but the relationship between warpage and these performances is not clear and the determination of these weighting factors is rather difficult. Therefore, the optimization with the above objective function probably will not minimize part warpage even with perfect optimization technique. Sometimes, improper weighting factors will result in absolutely wrong re- sults.Some statistical quantities calculated from the nodal displacements were characterized as direct quality measures to achieve minimum deformation in related optimization studies. The statistical quantities are usually a maximum nodal displacement, an av- erage of top 10 percentile nodal displacements, and an overall average nodal displacement (Lee and Kim,1995; 1996b). These nodal displacements are easy to obtain from the simulation results, the statistical val- ues, to some extents, representing the deformation. But the statistical displacement cannot effectively describe the deformation of the injection molded parts.In industry, designers and manufacturers usually pay more attention to the degree of part warpage on some specific features than the whole deformation of the injection molded parts. In this study, feature warpage is defined to describe the deformation of the injection parts. The feature warpage is the ratio of the maximum displacement of the feature surface to the projected length of the feature surface (Fig.1):where γ is the feature warpage, h is the maximum displacement on the feature surface deviating from the reference platform, and L is the projected length of the feature surface on a reference direction paralleling the reference platform.For complicated features (only plane feature discussed here), the feature warpage is usually sepa- rated into two constituents on the reference plane, which are represented on a 2D coordinate system:where γx, γy are the constituent feature warpages in the X, Y direction, and L x, L y are the projected lengths of the feature surface on X, Y component.Evaluation of feature wa rpageAfter the determination of target feature com- bined with corresponding reference plane and pro- jection direction, the value of L can be calculated immediately from the part with the calculating method of analytic geometry (Fig.2). L is a constant for any part on the specified feature surface and pro- jected direction. But the evaluation of h is more com- plicated than that of L.Simulation of injection molding process is a common technique to forecast the quality of part de- sign, mold design and process settings. The results of warpage simulation are expressed as the nodal de- flections on X, Y, Z component (W x, W y, W z), and the nodal displacement W. W is the vector length of vector sum of W x·i, W y·j, and W z·k, where i, j, k are the unit vectors on X, Y, Z component. The h is the maximum displacement of the nodes on the feature surface, which is correlated with the normal orientation of the reference plane, and can be derived from the results of warpage simulation.To calculate h, the deflection of ith node is evaluated firstly as follows:where W i is the deflection in the normal direction of the reference plane of ith node; W ix, W iy, W iz are the deflections on X, Y, Z component of ith node; α,β,γ are the angles of normal vector of the reference; A and B are the terminal nodes of the feature to projectingdirection (Fig.2); WA and WB are the deflections of nodes A and B:where W Ax, W Ay, W Az are the deflections on X, Y, Zcomponent of node A; W Bx, W By and W Bz are the de- flections on X, Y, Z component of node B; ωiA and ωiB are the weighting factors of the terminal node deflections calculated as follows:where L iA is the projector distance between ith node and node A. Ultimately, h is the maximum of the absolute value of W i:In industry, the inspection of the warpage is carried out with the help of a feeler gauge, while the measured part should be placed on a reference plat- form. The value of h is the maximum numerical reading of the space between the measured part sur- face and the reference platform.GATE LOCATION OPTIMIZATION PROBLEM FORMATIONThe quality term “warpag e”means the perma- nent deformation of the part, which is not caused by an applied load. It is caused by differential shrinkage throughout the part, due to the imbalance of polymer flow, packing, cooling, and crystallization.The placement of a gate in an injection mold is one of the most important variables of the total mold design. The quality of the molded part is greatly af- fected by the gate location, because it influences the manner that the plastic flows into the mold cavity. Therefore, different gate locations introduce inho- mogeneity in orientation, density, pressure, and temperature distribution, accordingly introducing different value and distribution of warpage. Therefore, gate location is a valuable design variable to minimize the injection molded part warpage. Because the cor- relation between gate location and warpage distribu- tion is to a large extent independent of the melt and mold temperature, it is assumed that the moldingconditions are kept constant in this investigation. The injection molded part warpage is quantified by the feature warpage which was discussed in the previous section.The single gate location optimization can thus be formulated as follows:Minimize:Subject to:where γ is the feature warpage; p is the injection pressure at the gate position; p0 is the allowable in- jection pressure of injection molding machine or the allowable injection pressure specified by the designer or manufacturer; X is the coordinate vector of the candidate gate locations; X i is the node on the finite element mesh model of the part for injection molding process simulation; N is the total number of nodes.In the finite element mesh model of the part, every node is a possible candidate for a gate. There- fore, the total number of the possible gate location N p is a function of the total number of nodes N and the total number of gate locations to be optimized n:In this study, only the single-gate location problem is investigated.SIMULATED ANNEALING ALGORITHMThe simulated annealing algorithm is one of the most powerful and popular meta-heuristics to solve optimization problems because of the provision of good global solutions to real-world problems. The algorithm is based upon that of Metropolis et al. (1953), which was originally proposed as a means to find an equilibrium configuration of a collection of atoms at a given temperature. The connection be- tween this algorithm and mathematical minimization was first noted by Pincus (1970), but it was Kirkpatrick et al.(1983) who proposed that it formed the basis of an optimization technique for combina- tional (and other) problems.To apply the simulated annealing method to op timization problems, the objective function f is used as an energy function E. Instead of finding a low energy configuration, the problem becomes to seek an approximate global optimal solution. The configura- tions of the values of design variables are substituted for the energy configurations of the body, and the control parameter for the process is substituted for temperature. A random number generator is used as a way of generating new values for the design variables. It is obvious that this algorithm just takes the mini- mization problems into account. Hence, while per- forming a maximization problem the objective func- tion is multiplied by (−1) to obtain a capable form.The major advantage of simulated annealing algorithm over other methods is the ability to avoid being trapped at local minima. This algorithm em- ploys a random search, which not only accepts changes that decrease objective function f, but also accepts some changes that increase it. The latter are accepted with a probability pwhere ∆f is the increase of f, k is Boltzm an’s constant, and T is a control parameter which by analogy with the original application is known as the system “tem perature”irrespective of the objective function involved.In the case of gate location optimization, the implementation of this algorithm is illustrated in Fig.3, and this algorithm is detailed as follows:(1) SA algorithm starts from an initial gate loca- tion X old with an assigned value T k of the “tempera- ture”parameter T (the “temperature” counter k is initially set to zero). Proper control parameter c (0<c<1) in annealing process and Markov chain N generateare given.(2) SA algorithm generates a new gate location X new in the neighborhood of X old and the value of the objective function f(X) is calculated.(3) The new gate location will be accepted with probability determined by the acceptance functionFig.3 The flow chart of the simulated annealing algorithmAPPLICATION AND DISCUSSIONThe application to a complex industrial part is presented in this section to illustrate the proposed quality measure and optimization methodology. The part is provided by a manufacturer, as shown in Fig.4. In this part, the flatness of basal surface is the most important profileprecision requirement. Therefore, the feature warpage is discussed on basal surface, in which reference platform is specified as a horizontal plane attached to the basal surface, and the longitu- dinal direction is specified as projected reference direction. The parameter h is the maximum basal surface deflection on the normal direction, namely the vertical direction, and the parameter L is the projected length of the basal surface to the longitudinal direc- tion.Fig.4 Industrial part provided by the manufac tur e rThe material of the part is Nylon Zytel 101L (30% EGF, DuPont Engineering Polymer). The molding conditions in the simulation are listed in T able 1. Fig.5 shows the finite element mesh model ofthe part employed in the numerical simulation. It has1469 nodes and 2492 elements. The objective func- tion, namely feature warpage, is evaluated by Eqs.(1), (3)~(6). The h is evaluated from the results of “Flow+Warp” Analysis Sequence in MPI by Eq.(1), and the L is measured on the industrial part immediately, L=20.50 mm.MPI is the most extensive software for the in- jection molding simulation, which can recommend the best gate location based on balanced flow. Gate location analysis is an effective tool for gate location design besides empirical method. For this part, the gate location analysis of MPI recommends that the best gate location is near node N7459, as shown in Fig.5. The part warpage is simulated based on this recommended gate and thus the feature warpage is evaluated: γ=5.15%, which is a great value. In trial manufacturing, part warpage is visible on the sample work piece. This is unacceptable for the manufacturer.The great warpage on basal surface is caused bythe uneven orientation distribution of the glass fiber, as shown in Fig.6a. Fig.6a shows that the glass fiber orientation changes from negative direction to posi- tive direction because of the location of the gate, par- ticularly thegreatest change of the fiber orientation appears near the gate. The great diversification of fiber orientation caused by gate location introduces serious differential shrinkage. Accordingly, the fea- ture warpage is notable and the gate location must be optimized to reduce part warpageT o optimize the gate location, the simulated an- nealing searching discussed in the section “Simulated annealing algorithm” is applied to this part. The maximum number of iterations is chosen as 30 to ensure the precision of the optimization, and the maximum number of random trials allowed for each iteration is chosen as 10 to decrease the probability of null iteration without an iterative solution. Node N7379 (Fig.5) is found to be the optimum gate loca- tion.The feature warpage is evaluated from the war- page simulation results f(X)=γ=0.97%, which is less than that of the recommended gate by MPI. And the part warpage meets the manufacturer’s requirements in trial manufacturing. Fig.6b shows the fiber orien- tation in the simulation. It is seen that the optimal gate location results in the even glass fiber orientation, and thus introduces great reduction of shrinkage differ- ence on the vertical direction along the longitudinal direction. Accordingly, the feature warpage is re- duced.CONCLUSIONFeature warpage is defined to describe the war- page of injection molded parts and is evaluated based on the numerical simulation software MPI in this investigation. The feature warpage evaluation based on numerical simulation is combined with simulated annealing algorithm to optimize the single gate loca- tion for plastic injection mold. An industrial part is taken as an example to illustrate the proposed method. The method results in an optimal gate location, by which the part is satisfactory for the manufacturer. This method is also suitable to other optimization problems for warpage minimization, such as location optimization for multiple gates, runner system bal- ancing, and option of anisotropic materials.注塑模的单浇口优化摘要:本文论述了一种单浇口位置优化注塑模具的方法。

  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

TEM Investigation and FBB Model Explanation to the Phase Relationships between Titanates and Titanium DioxidesHongwei Liu,Eric R.Waclawik,Zhanfeng Zheng,Dongjiang Yang,Xuebin Ke,Huaiyong Zhu,and Ray L.Frost*Discipline of Chemistry,Faculty of Science and Technology,Queensland Uni V ersity of Technology,Brisbane QLD 4001,AustraliaRecei V ed:April 23,2010;Re V ised Manuscript Recei V ed:May 31,2010Sodium and hydrogen titanates fibers and three TiO 2polymorphs (TiO 2(B),anatase,and rutile)were prepared from inorganic titanium compounds by hydrothermal reaction and calcination.The nature and morphologies of the nanofibers with layered structure were investigated by means of X-ray diffraction (XRD)and transmission electron microscopy (TEM).It was revealed that the phase transitions between titanates and titania (via proton titanate)could be interpreted systemically by the fundamental building blocks (FBB)model.The reaction also requires interconversion between some corner-linked and edge-linked pairs of TiO 6octahedrons.A generalized relationship of phase transitions between sodium/hydrogen titanates and titania by the wet-chemical process was proposed.A duplex pentagonal prism scheme was implied to summarize all of the phase relationships between titanates and titania.I.IntroductionMuch attention has been put into the design and fabrication of nanostructures based on metal oxides in the past decade because of their peculiar electronic and optic properties and their potential applications in technology.1-5There is great interest in the development of titanates and TiO 2-based solids with nanoscale dimensions and high morphological specificity 6,7such as nanofibers,8nanosheets,9and nanotubes 10because of their de-monstratedpotentialinsolarenergyconversion,11photocatalysis,12,13photovoltaic devices,14,15and as carrier for metallic nanopar-ticles.16Hydrothermal treatment of different TiO 2precursors in a highly alkaline medium is a powerful way to prepare nanotubes and nanowires,but the information about the structure of the synthesized nanostructures is still unclear despite intensive investigations.In contradiction with previous investigations,Sun et al.17report that as-synthesized nanotubes are titanates and can be described as Na x H 2-x Ti 3O 7.Thermal treatment of these materials leads to the formation of different titanates with the general formula Na 2Ti n O 2n +1.Recently,it was also mentioned that as-synthesized nanotubes or nanorods have an even more complex structure,Na x H 2-x Ti n O 2n +1·y H 2O.18-20Treatment of these nanoparticles with a HCl solution produced nanotubes and nanorods of H 2Ti 3O 7.Although the importance of continuing efforts to develop alternate approaches to the synthesis of the nanostructures has been realized,4the potential for controlled reactions of these nanostructures has not drawn significant attention.In fact,phase transitions at moderate temperature are strongly preferred for constructing inorganic structures on nanometer scale (nano-scale)21because the delicate nanostructures can easily be lost at high temperatures due to sintering.The present authors’recent work has described a reversible scheme of transitions between nanoscale titanium dioxides to and from nanoscale titanates.22We found for the first time that phase transitions from the titanate nanostructures to TiO 2polymorphs take place readilyin wet-chemical processes at temperatures close to ambient.Furthermore,the resultant TiO 2nanocrystals can react with concentrated NaOH solution,yielding hollow titanate nanotubes.In the present work,we report phase transitions from titanate fibers to hexatitanate,TiO 2(B),and anatase.The phase transitions are systematically explained by a fundamental building blocks (FBB)model,and a stereoschematic is implied on the basis of the above explanation.II.Experimental Section1.Sample Preparation.NaOH pellets and HNO 3(both are AR grade from Aldrich)and TiOSO 4·x H 2O (98%,from Fluka)were used in the synthesis.Titanate nanofibers in this study were prepared via a hydrothermal reaction between a concen-trated NaOH solution and an inorganic titanium salt.15Specif-ically,10.7g of TiOSO 4·H 2O was dissolved into 80mL of water and stirred until becoming clear.The resultant TiOSO 4solution was mixed with 100mL of 15M NaOH solution while stirring.The mixture (white suspension)was then transferred into a 200mL Teflon-lined stainless steel autoclave and kept at a temperature between ambient and 200°C for 48h to yield titanate precipitates via a hydrothermal reaction.The white precipitate in the autoclaved mixture was recovered by cen-trifugation and washed with deionized water four times by dispersing the wet cake into 100mL of water and recovering the solid by centrifugation.The sodium titanate product was labeled as T3.T3was calcinated at 500°C for 16h under air flow to obtain a new titanate T6.The sodium titanates T3and T6were then neutralized using 0.1M HCl solution and washed with water to remove most of the sodium ions.We dried the resultant hydrogen titanate (H-titanate;denoted as T3-H and T6-H,respectively)at 100°C for 16h and then dispersed this into a dilute (0.05M)HNO 3solution,at temperatures selected to form anatase (TA).On the other hand,the resultant hydrogen titanate (H-titanate)was carried out at 500°C in an atmosphere of air flow (20cm 3/min)to obtain TiO 2(B)(denoted as “TB”).Standard laboratory safety precautions,including the use of appropriate hoods,*Corresponding author.E-mail:r.frost@.au.J.Phys.Chem.C 2010,114,11430–114341143010.1021/jp103644x 2010American Chemical SocietyPublished on Web 06/14/2010vessels,and safety gloves,were required when handling the strong acids and bases.2.Sample Characterization.The microstructures and mor-phologies of products were investigated using transmission electron microscopy (TEM)and X-ray diffraction (XRD)techniques.TEM images were recorded on a Philips CM20TEM,employing an accelerating voltage of 200kV and double-tilt holder.High-resolution transmission electron microscopy (HRTEM)was carried out on a Philips Tecnai F20under an accelerating voltage of 200kV.XRD patterns of the sample powders were recorded using a Shimadzu XRD-6000diffrac-tometer,equipped with a graphite monochromator.Cu K R radiation (λ)0.15418nm)and a fixed power source (40kV and 40mA)were used.The samples were scanned at a rate of 1°(2θ)/min over a range of 2-80°,which covers the main characteristic diffraction peaks of the titanates,anatase,and TiO 2(B).X-ray photoelectron spectroscopy (XPS)spectra were recorded in an ESCALAB 250spectrometer,and Al K R radiation was used as the X-ray source.The C1s peak at 284.5eV was used as a reference for the calibration of the binding energy (BE)scale.III.Results and Discussion3.1.Phase Transformations from Titanate to Titania.The preparation procedure of the samples used here is shown in Scheme 1summarized from the viewpoint of phase transforma-tion.The phase transformations were investigated by X-ray diffraction (Figure 1).The directly obtained product (T3,Figure 1A)from hydro-thermal reaction is consistent mainly with tritanate phase (Na 2Ti 3O 7,monoclinic,S.G.P 21/m ,a )0.8566nm,b )0.3804nm,c )0.9133nm, )101.57°,ICSD #250000),althoughthere are some impurities,which is similar to the result given by Kolen’ko et al.23X-ray photoelectron spectroscopy (XPS)of the surface indicated that this washed product possessed a sodium content of ∼10wt %Na.The T3was converted to T6shown in Figure 1B (Na 2Ti 6O 13,monoclinic,S.G.C 2/m ,a )1.513nm,b )0.3745nm,c )0.9159nm, )99.30°,ICSD #23877)after calcining at 500°C.Proton titanate T6-H (H 2Ti 6O 13,Figure 1C)was obtained by ion-exchange of the Na with H from the respective sodium titanate T6.It is noted that the basal structure of proton titanate was not changed,although the ion-exchange process results in the decrease of the d -spacing in the layered structure.Calcining the proton titanate T6-H yielded TB shown in Figure 1D (TiO 2(B),monoclinic,S.G.C 2/m ,a )0.6524nm,b )0.3740nm,c )01.218nm, )107.05°,ICSD #41056),TA shown in Figure 1E (anatase,tetragonal,S.G.I 41/amd ,a )0.3782nm,c )0.9502nm,ICSD #63711),depending on the calcination temperature.It is noteworthy that the fibril morphology was heritage from T3for all of the above samples.All of the obtained titania phases can react with concentrated NaOH solution and yield trititanate.In addition,the phases involved here exhibit a complicated transition relationship,which has been summarized in Figure 2.All titanates on the green plane in Figure 2can be converted into titania directly by an acid-assisted wet-chemical process as reported earlier.163.2.TEM Observation.The phase transition process was also confirmed by transmission electron microscopy (TEM)and corresponding electron diffraction (ED)patterns,as shown in Figure 3.The lattice parameters for the samples were calculated from ED patterns and listed in Table 1.Figure 3A shows a typical low-magnification TEM image of as-prepared nanofibers of T3sample.The corresponding ED patterns (Figure 3B)indicate that it has a high crystallinity of the nanostructures,which are also confirmed by XRD.The ED patterns can be indexed as the monoclinic C 2/m space group,using the Na 2Ti 3O 7parameters.Figure 3C shows a typical low-magnification TEM image of as-prepared nanofibers of T6sample.The correspond-ing ED patterns (Figure 3D)indicate that this sample has also a high crystallinity of the nanostructures,which are confirmed by XRD.The ED patterns can be indexed as the monoclinic P 21space group,using the Na 2Ti 6O 13parameters.The zone axis is [001].Figure 3E shows a low-magnification image of proton titanate T6-H (H 2Ti 6O 13),a product of ion exchange of T6.The powder X-ray diffraction pattern of the sample reveals that the crystal structure is almost the same as that of T6because the positions of peaks of the sample are almost coincident with those of T6.It could be deduced that the structure of the sample is the sameSCHEME 1:Schematic Showing Phase Transformation Relationships between the Samples Used in This StudyaaT3(6)represents trititanate (hexatitanate).T3(6)-H represents proton trititanate(hexatitanate).Figure 1.Powder X-ray diffraction patterns of the titania-basednanofibers.Figure 2.A generalized phase transition between sodium-and hydrogen-titanates,anatase,TiO 2(B),and rutile by the wet-chemical process.Phase Relationships between Titanates and Titanium Dioxides J.Phys.Chem.C,Vol.114,No.26,201011431as T6based on the relationship of the crystal structure between T3and T3-H.The ED taken down [001]is shown in Figure 2F.The lattice parameters determined by ED are about 1.46nm for a ,0.382nm for b ,and 1.30for c ,which slightly depart from those of T6.Figure 3G and I show the TEM micrograph of TB (TiO 2(B))and TA (anatase)samples.The corresponding ED for TB sample (Figure 3H)down [001]can be indexed as the monoclinic C 2/m space group,using the TiO 2(B)parameters.The correspondingED down [67j 1]for TA is shown in Figure 3J,and can be indexed as the tetragonal I 41/amd space group,using the anatase parameters.3.3.FBB Model Explanation to Phase Transitions be-tween Titanates and Titania.There are many ways developed to describe the structures and the relationships between inorganic crystals.Veblen has discussed the utility of the poly somatic-series approach.24It cannot be simply applied to describe the TiO 2polymorphs,primarily because of the absence of slabs common to all polymorphs.In this work,the titanium oxide structures will be discussed on the approach based on the use of fundamental building blocks (FBB),25emphasized by the similarities between the structures.Banfield 26has used this model to represent the relationships among the TiO 2minerals.All of the structures except rutile related in this study can be constructed from a unit composed of four edge-sharing octa-hedrons,which are shown in Figure4.The difference between the corner-shared octahedrons and edge-shared octahedrons is marked with arrows.Thus,polyhedral representation of the structures of titanate and titania in this work can be described as Figure 5.It is shown in Figure 5the polyhedral representation of the structures of titanate and titania.All of the projections are viewed down [010]except rutile (down [001]).In Figure 5a,every three TiO 6octahedrons form a slab by edge-sharing,and the slabs connect with each other to form a layer of TiO 6octahedrons in corner-linked mode.Thus,upper-layer and lower-layer TiO 6octahedrons form layered-structural trititanate.If atomic sites between TiO 6slab layers are occupied by sodium ions,it is sodium titanate (Figure 5a).If they are taken by hydrogen ions,it is hydrogen trititanate (Figure 5b).If TiO 6layers in trititanate are connected in corner-linked mode,it will change into microporous structure.Thus,sodium hexati-tanate and hydrogen hexatitanate can be obtained as shown in Figure 5c and d.Given the absence of ions,if all TiO 6octahedrons are corner-linked,we can get rutile (Figure 5g).Similarly,if all octahedrons are edge-linked,we can get TiO 2(B)(Figure 5e)or anatase (Figure 5f).By applying the FBB model,the present authors have successfully explained the structural evolution in several transi-tion systems,such as the absorption of ammonium ions,27bivalent Ba and Sr cations 28,29by trititanate,and conversion of trititanate to hexatitanate.30A PBB schematic representing the transition of Na 2Ti 3O 7to TiO 2(B)is implied in Figure 6.There are four steps that are needed for converting this structural transition.Figure 6A shows an undistorted representation of the Na 2Ti 3O 7structure viewed down [010].The conversion from Na 2Ti 3O 7to TiO 2(B)requires replacement of Na cations with hydrogen ions,whichcorre-Figure 3.TEM microstructure of the samples used in this study.PanelsA,C,E,G,and I are T3,T6,T6-H,TB,and TA fibers,respectively.Panels B,D,F,H,and J are the electron diffraction patterns corresponding to the above samples in the same sequence.TABLE 1:Phase Compositions and Crystallographic Parameters for Nanofiberscrystal structureparameters (nm)reference phasecomposition space group a bc(deg)Ti3Na 2Ti n O 2n +1,n )3,4,9C 2/m 1.5130.3750.91699.3T6Na 2Ti 6O 13P21 1.560.378 1.22105Ti6-H a H 2Ti 6O 13P21 1.460.38 1.30105TB TiO 2(B)C 2/m 1.2160.3740.651107.29TAanataseI 41/amd0.37890.9537aCrystalline parameters for Ti6-H are measured in this study.11432J.Phys.Chem.C,Vol.114,No.26,2010Liu et al.sponds to step I.The reaction also requires interconversion between some corner-linked and edge-linked pairs of Ti -O 6octahedra,which have been illustrated in Figure 6b and c.The slab of TiO 6octahedron in trititanate is -3-3-3-;that is,every three edge-shared octahedrons connect mutually by corner-sharing,which will become -2-2-2-after conversion in step II (Figure 6c)and step III (Figure 6d).The structure of TiO 2(B)(Figure 6e)thus becomes reality by relative slipping of upper layer and lower layer (step III)followed by dehydration (step IV),as shown in the enlarged inset of Figure 6.IV.ConclusionThe phase transformations from sodium trititanate to hexati-tanate and from proton titanate to TiO 2(B)and anatase were investigated by XRD and TEM.It was revealed that the phase transitions and morphological derivation between titanates,anatase,and TiO 2(B)(via proton titanate)could be interpreted systemically by the fundamental building blocks model (FBB).A generalized relationship of phase transitions between sodiumand hydrogen titanates,anatase,TiO 2(B),and rutile by the wet-chemical process was then proposed.A duplex pentagonal prism scheme was then implied to summarize all of the relationships between titanates and titania including this work and previous work.It is the first time in which seven compounds of titanium have been made into one stereoscheme,showing the complex connections among them.Acknowledgment.The financial and infra-structure support of the Queensland University of Technology Inorganic Materials Research Program of the School of Physical and Chemical Sciences is gratefully acknowledged.The Australian Research Council (ARC)is thanked for funding the instrumentation.References and Notes(1)Li,D.;Xia,Y.Ad V .Mater.2004,16,1151–1170.(2)Limmer,S.J.;Cao,G.Ad V .Mater.2003,15,427–431.(3)Xia,Y.;Yang,P.;Sun,Y.;Wu,Y.;Mayers,B.;Gates,B.;Yin,Y.;Kim,F.;Yan,H.Ad V .Mater.2003,15,353–389.(4)Rao,C.N.R.;Deepak,F.L.;Gundiah,G.;Govindaraj,A.Prog.Solid State Chem.2003,31,5–147.(5)Patzke,G.R.;Krumeich,F.;Nesper,R.Angew.Chem.,Int.Ed.2002,41,2446–2461.(6)Hennings,D.;Klee,M.;Waser,R.Ad V .Mater.1991,3,334–340.(7)Newnham,R.E.MRS Bull.1997,22,20–33.(8)Gao,X.P.;Zhu,H.Y.;Pan,G.L.;Ye,S.H.;Lan,Y.;Wu,F.;Song,D.Y.J.Phys.Chem.B 2004,108,2886–2872.(9)Sukpirom,N.;Lerner,M.M.Chem.Mater.2001,13,2179–2185.(10)Kasuga,T.;Hiramatsu,M.;Hoson,A.;Sekino,T.;Niihara,ngmuir 1998,14,3160–3163.(11)Gra ¨tzel,M.Nature 2001,414,338–344.(12)Fujishima,A.;Hashimoto,K.;Watanabe,T.TiO 2Photocatalysis Fundamentals and Applications ;BKC,Inc.:Tokyo,1999.Figure 4.A scheme of functional buildingblocks.Figure 5.Polyhedral representation of the structures of titanate and titania.All of the projections are down [010]except rutile (down[001]).Figure 6.Diagram illustrating the stepwise conversion of Na 2Ti 3O 7(a)to TiO 2(B)(d).Parts (b)and (c)are the proposed intermediate.Phase Relationships between Titanates and Titanium Dioxides J.Phys.Chem.C,Vol.114,No.26,201011433(13)Linsebigler,A.L.;Lu,G.;Yates,J.T.Chem.Re V.1995,95,735–758.(14)Sugiura,T.;Yoshida,T.;Minoura,H.Electrochem.Solid-State Lett. 1998,1,175–177.(15)Tokudome,H.;Miyauchi,mun.2004,958–959.(16)Huang,J.G.;Kunitake,T.;Onoue,mun.2004, 1008–1009.(17)Sun,X.;Li,Y.Chem.-Eur.J.2003,9,2229–2238.(18)Armstrong,G.;Armstrong,A.R.;Canales,J.;Bruce,P.G.Chem. Commun.2005,2454–2456.(19)Armstrong,A.R.;Armstrong,G.;Canales,J.;Garsia,R.;Bruce, P.G.Ad V.Mater.2005,17,862–865.(20)Armstrong,A.R.;Armstrong,G.;Canales,J.;Bruce,P.G.Angew. Chem.,Int.Ed.2004,43,2286–2288.(21)Zhu,H.Y.;Gao,X.P.;Lan,Y.;Song,D.Y.;Xi,Y.X.;Zhao, J.C.J.Am.Chem.Soc.2004,126,8380–8381.(22)Zhu,H.Y.;Lan,Y.;Gao,X.P.;Ringer,S.P.;Zheng,Z.F.;Song,D.Y.;Zhao,J.C.J.Am.Chem.Soc.2005,127,6730–6736.(23)Kolen’ko,Y.V.;Kovnir,K.A.;Gavrilov,A.I.;Garshev,A.V.; Frantti,J.;Lebedev,O.I.;Churagulov,B.R.;Gustaaf Van Tendeloo,G.V.; Yoshimura,M.J.Phys.Chem.B2006,110,4030–4038.(24)Veblen,D.R.Am.Mineral.1991,76,801–826.(25)Moor,P.B.Am.Mineral.1986,71,540–546.(26)Banfield,J.F.;Veblen,D.R.Am.Mineral.1992,77,545–557.(27)Yang,D.J.;Zheng,Z.F.;Zhu,H.Y.;Liu,H.W.;Gao,X.P.Ad V. Mater.2008,20,2777–2781.(28)Liu,H.W.;Yang,D.J.;Waclawik,E.R.;Ke,X.B.;Zheng,Z.F.; Zhu,H.Y.;Frost,R.L.J.Raman Spectrosc.,in press.(29)Liu,H.W.;Zheng,Z.F.;Yang,D.J.;Waclawik,E.R.;Ke,X.B.; Zhu,H.Y.;Frost,R.L.J.Raman Spectrosc.,in press.(30)Yang,D.J.;Zheng,Z.F.;Yuan,Y.;Liu,H.W.;Waclawik,E.R.; Ke,X.B.;Xie,M.X.;Zhu,H.Y.Phys.Chem.Chem.Phys.2010,12, 1271–1277.JP103644X11434J.Phys.Chem.C,Vol.114,No.26,2010Liu et al.。

相关文档
最新文档