PDF-2 = Quality + Value

Transcription

1

2 Dear Customer, We invite you to review this brochure and case studies that showcase the benefits of using a quality database for your phase analyses. The PDF-2 database, featuring 265,127 entries, is designed for rapid and accurate phase identification. It combines the power of both powder diffraction and crystal structure reference data from the ICDD, ICSD*, and NIST* database sources. As you will discover in the following pages, you can be confident with PDF-2. It is designed to get you the right answer with the most cost effective license. PDF databases use a Sybase relational database platform integrated with JAVA for point and click data mining, rapid searches, and sorts for all materials and materials systems. All ICDD databases use an integrated editorial quality review for all data sets. This is the only crystallographic database that evaluates the quality and provides the results of the assessment to the user in the comment section of each reference. PDF-2 = Quality + Value *Our international crystallographic database organizations collaborations include: Fachinformationszentrum Karlsruhe (FIZ), Karlsruhe, Germany Inorganic Crystal Structure Database (ICSD) National Institute of Standards and Technology (NIST), Gaithersburg, Maryland ICDD, the ICDD logo, and PDF are registered in the U.S. Patent and Trademark Office. Powder Diffraction File is a trademark of JCPDS International Centre for Diffraction Data JCPDS International Centre for Diffraction Data

3 How good is your database? We know that any database can get you an answer, but is it the right answer? Be confident with PDF-2. It is designed to get you the right answer with the most cost effective license. QUALITY + VALUE = PDF-2 PDF-2 is a database designed for rapid and accurate phase identification. It combines the power of both powder diffraction and crystal structure reference data from the ICDD, ICSD, and NIST database sources. This combination results in a database that is globally sourced with 265,127 entries from 2,441 journals and 325,903 references. The data are reviewed, edited and standardized prior to PDF-2 publication. The standardization process is especially important due to the large number of sources used for the database. This database contains extensive collections of inorganic, mineral, and metals and alloys data. There are select collections of organic and polymer data in the database that are comprised primarily of commercial materials with 10,875 of the organic entries originating from ICDD s Grant-in-Aid program. This is part of the design of the database where materials are added to help you solve your problems. In our product testing, this collection of organics outperforms many other larger organic databases because of the focus on common and industrially important materials. Many of the ICDD powder diffraction sources being from individual contributions and grants are unique to the PDF databases. EXCELLENT VALUE, COST EFFECTIVE AND HIGH PERFORMANCE The PDF-2 database is licensed for five years, with one free option to extend the license for an additional five years. Over the 10 year license period, the database effectively costs hundreds of dollars a year. The PDF-2 license is more cost effective than most commercial single crystal databases that have higher annual fees and have less content than PDF-2. For industrial and government users, the cost differential is thousands of dollars over the lifetime of the product. The longer the database is used, the more cost effective it becomes. Of course, the user always has the option to renew their license to the current PDF-2 release using the low renewal pricing. KEY FEATURES OF PDF-2 RELEASE 2013 DATA SOURCES Powder Data (ICDD) 110,224 Single Crystal Data (ICSD, NIST) 154,903 FUNDAMENTALS Total Number of Datasets 265,127 Number with Cell Parameters 233,795 Number with l/l c 167,539 Number of Editorial Comments 1,301,250 BIBLIOGRAPHIC DATA SUBFILE DISTRIBUTION Number of References 325,903 Number of Journals 2,441 Number of Authors 102,056 Number of Titles 156,959 Inorganic 233,700 Organic 37,816 Mineral 32,153 Metal and Alloys 89,189 POWDER DATA AND SINGLE CRYSTAL DATA You get the best of both worlds by combining a powder database with a single crystal database into a single product! Single crystal data and experimental powder data can be very complimentary in powder diffraction analyses. Experimental powder patterns are very useful for materials that are difficult to crystallize or are noncrystalline. Some materials that are difficult to crystallize include many polymers, pigments, clays and zeolites. Subfile or Subclass Powder Single Crystal Total Polymers 1, ,272 Zeolites 1,565 1,763 3,328 Pigments ,034 Minerals 11,615 20,538 32,153 As shown above, the number of entries from these two primary sources of data is very complimentary and enables the user to have extensive materials coverage

4 versus having just a single crystal database or a powder database. A comprehensive database will get you the right result. When you compare PDF-2 to other databases, especially relative to the materials that you analyze, you will discover that many databases claim large numbers, but have a limited field of chemistry and application. PERFORMANCE GETTING THE RIGHT ANSWER The successful performance of the PDF-2 database is due not only to having extensive content, but it is also due to its design and embedded software. As with any database product the intent is not to get an answer, but to get the correct answer! The design of the database is targeted at phase identification by comparison to reference powder patterns. The database housing is a relational database for facile data mining. Through the use of JAVA interfaces, data can be organized, sorted and displayed using a point and click of the mouse. The most effective data mining tools for material identification are: Subfile searches Elemental composition searches Long and strong line searches Status and quality mark searches Symmetry searches The status, quality mark and subfile system are unique to ICDD products. Data are reviewed and classified by quality, subfile content, and status. Status categories include primary, alternate and deleted. The primary designation is made by field experts and designates the best entry for a material of a specific composition. This allows the user to use the best data for material identification and then further target the identification by filters, such as elemental composition and subfile type. Patterns that are designated as deleted are the result of a better pattern being added to the database. The pattern is not removed from the database for legacy purposes. We are the only crystallographic database that defines its quality based on the accuracy of the powder pattern. A simple demonstration of the power of filters is shown in the table above. A strong line search was used as a filter. The results are shown for the number of entries with this line(s) in the full database, the Mineral subfile and a mineral containing iron. A small number of candidate entries (less than 10) can be achieved using relatively little information. As the accuracy of the experimental d-spacing improves, for example when 3.06 Å is replaced by Å, then the candidate phase selection also improves (fewer candidates). As shown in the enclosed case histories, such filtering can be used to easily identify trace and minor phases in your experimental data. This filtering was enabled by having long and strong line indexes in the database, converting all formula to atomic and weight percent composition, and classifying the database into 31 defined subfiles. Over 70 years of editing by experts ensures that the subfiles have comprehensive coverage. The strong line search filtering takes advantage of the subfile d-spacing distributions in the database. In the above case using 22,705 primary minerals, maximum d-spacings were plotted in 0.05 d-spacing increments. The maximum of the distribution occurs near the 3.06 Å d-spacing used in the table; however, the population significantly falls off at higher d-spacings. Small clusters at 7.2, 10 and 14.4 Å are common to families of layered silicates (kaolin, muscovite, clinochlore, vermiculite, etc.). The population distributions are significantly different for minerals versus metals versus pharmaceuticals, and each subfile and subclass has its own characteristics.

5 Perhaps the biggest advantage of using filters is that you eliminate candidate phases having the wrong chemistry, low precision, and/or poor quality. Any database can get you an answer, PDF-2 is designed to get you the right answer. All filtering is done by software embedded in the PDF-2 database; no additional software is required. Embedded Software Features in PDF-2 CHOICES 31 Subfiles Elemental Searches Nomenclature Searches Mineral and Zeolite Classifications Physical Property Searches Author, Crystal and Reduced Cell Searches ANX, Structure Type Searches - Pearson Symbol Code Bibliographic Searches databases includes crystal structures, molecular graphics, and digital patterns. Technical details about these products can be found in the technical bulletins at EDUCATION Our goal is to help you solve your material problems. In addition to the PDF-2 database, we also provide online publications and tutorials. Many tutorials focus on capabilities of the database, but there are also general tutorials that describe methods used to analyze drugs, polymers, and minerals. The tutorial page has links to free download publications, as well as instructional videos. The website also contains over 931 full publications for free download from Advances in X-ray Analysis. Our website, tutorial page and publication pages are there to help you! The PDF-2 database offers the highest value in the global marketplace. This value is due to its low annual cost amortized over the lifetime of the license, combined with its large content of powder and crystal structure reference data, and its high level of quality with embedded software for data mining. Quality and subfile filters combined with 52 different searches and 47 display fields enable you to target your results for more accurate identification. The database is not just a collection of numbers; it is a sophisticated data analysis tool. PDF-2 is not the right choice of database if you are working in an advanced scientific field where new materials are being continuously developed by the global community. This would include most electronics materials, pharmaceuticals, nanomaterials, and energy storage materials. If you need a database with comprehensive global materials coverage designed for both phase identification and quantitative analysis, we would recommend one of our PDF-4 databases (PDF-4+, PDF-4/Minerals, PDF-4/Organics). The PDF-4 family of databases features the data you need if you are doing Rietveld analysis or other pattern fitting techniques, performing a structure solution from powders, analyses of amorphous and noncrystalline materials, or determinations of crystallinity and crystallite size. Each of the

6 Entries 320,000 PDF-2 Value Price 270, ,000 Entries 170,000 List Price $9, ,000 Academic Price $7,000 70,000 Eight consecutive years of maintaing pricing while increasing VALUE by the increase in the size and technical capability of the PDF-2 database. $5,000 20,000 $3, PDF-2 Release Year Entries List Price Academic Price

7 CASE STUDY MORROCAN MINERAL A mineral sample from Bou Becker, Morroco was commercially purchased for analysis. The sample was sold as azurite and had the distinctive brilliant, blue color often associated with that mineral. However, light microscopic examination showed that the sample also contained other colored materials embedded in a matrix rock. The sample was manually ground and crushed in an agate mortar and pestle. The ground material was placed in a cavity mount for analysis. The overall texture of the specimen was very rough, as there were clear hard particles that were difficult to hand grind. The grinding was stopped because the analyst did not want to destroy the crystallinity of the specimen and it was obvious that there were hard materials in the composition. The data were collected on a commercial diffractometer equipped with a germanium monochromator. The raw data (shown above) was noisy, but showed well defined crystalline peaks. The noise was attributed to the rough specimen surface. For this analysis we used ICDD s (optional) search-indexing software for PDF-2, SIeve. The graphics and output shown in this case study is from SIeve. SIeve displays the data, allows the user to process the data and generate an input list of d-spacings and intensities for the analysis. The list of calculated d-spacings and intensities are shown on the right in the display above. Prior to an analysis, a selection box appears (not shown) during data input and asks if the user would like to use a filter. Since the specimen was a mineral, the mineral subfile was selected. The program immediately identified quartz and azurite as the two major phases in the specimen. In the bottom middle panel (above) are the experimental data (Ex d, Ex I), the identified phase of quartz (P 1 d, P 1 I), the identified phase of Azurite (P 2 d, P 2 I), and the possible next phase Ba(Pb)SO 4 (P 3 d, P 3 I). The Ba(Pb)SO 4 phase is also shown in the candidate list in the top box, highlighted in black. The colored d-spacings in the top box show where there is a match between the candidate phase and the experimental data. The panel on the bottom left shows

8 the identified materials and pattern intensities attributed to each material. If one looks at the experimental d-spacing (Ex d) in the bottom middle panel, matched d-spacings are shown in blue and unidentified d-spacings are shown in black. With these three phases identified, 52 out of 64 peaks in the pattern are accounted for, as well as all relative intensities greater than 5 (normalized to 100). At this point the user can try to identify minor phases from the remaining unidentified peaks. Since only a few peaks remain (black), the user needs to lower the Goodness of Match (GOM) threshold to allow the program to identify materials with only a couple of peaks. This second level search results in the identification of fluorite, copper, and the layered silicate polylithonite. The identification of polylithonite is tenuous since it is based primarily on a peak at 9.90 Å and there are other layered silicates with this characteristic peak. Both fluorite and copper are relatively simple patterns with only a few peaks in the measurement range, so reducing the GOM to below 2,000 was a necessary step for identification. The six phase data match is shown above with the box on the left highlighted to show the identification and phase intensity. Five of the six phases have an I/Ic value, which could be used for a semi-quantitative analysis. After this analysis an XRF analysis confirmed the presence of the major elements corresponding to the compounds above, including the Pb for the Ba(Pb)SO 4. The mineral was examined under a black light and CaF 2 particles fluoresced. Microscopic examination showed thin veins of copper and the azurite adjacent to the copper, but also showed green and black crystals near the veins. Reexamination of the XRD pattern identified cuprite and malachite. Eight phases were identified in the material using these additional observations. A Rietveld refinement confirmed that all of the minor phases were in concentrations from 0.3 to 6 weight percent. This example demonstrates several features of PDF-2. Both powder diffraction reference data and single crystal reference data were used in the identification. Good quality raw data matched with good quality references resulted in eight phase identification. Use of the mineral subfile helped filter candidates and reduce false positive identification. Confirming data (XRF, light microscopy, fluorescence) supported the diffraction identification, allowing the user to be confident that they had the correct answer JCPDS International Centre for Diffraction Data

9 CASE STUDY PORTLAND CEMENT A sample of Portland Cement powder was micronized using an attrition mill. The resulting powder diffraction pattern from the specimen is shown below. The data were collected on a conventional laboratory diffractometer using Cu radiation. The data were imported into ICDD s search-indexing software for PDF-2, SIeve. Using SIeve, the analyst can remove background, strip alpha 2 and identify peak locations ultimately generating a d-spacing, intensity pair listing that is used for phase analysis. The software also has an option for data smoothing, which was not used in this case since the micronizing mill generated a fine powdered specimen with appropriate small grains and a smooth surface. Before analyzing the sample, we want to use a subfile search of Cement and Hydration Product for the analysis. This is done by selecting and searching in the Subfile/Subclass Search options in PDF-2 as shown on left. Subfile contents have been classified by field experts to contain materials found in the cement industry. Once SIeve is opened, the user is given a choice between default subsets and the previously selected Cement and Hydration Product subfile. Since many cement materials are also minerals, a Mineral subfile could be used. However, not all minerals are used in cements, so by choosing the Cement and Hydration Product subfile the user performs the following data reduction in PDF-2. Total PDF-2 Release2013 Minerals Cement and Hydration Products 265,127 material entries 32,153 material entries 1,320 material entries

10 This step significantly improves the accuracy of the phase identification by eliminating materials of inappropriate chemistry, which reduces the chance of false positive phases in the identification. The ability to select specific subfiles is also embodied in most of ICDD distributor s software. We recommend subfile selection, in all cases, for more accurate analyses independent on whether you use ICDD s software or alternative software. The above figure shows the main analysis screen of SIeve. The identified phases in order of Goodness of Merit (GOM) are displayed in the top left. At the top right are the individual d-spacing matches for that particular phase compared to the experimental data. If the d-spacings match, they are designated in red colors. The top phase by GOM is tricalcium silicate, a very common cement phase, and all eight peaks are identified in the specimen. The bottom left panel shows the selected identification, its intensity and I/I c value, while the bottom middle panel shows the d, I pairs for the experiment (Ex d, Ex I) and the d, I pairs for the identified material (P1 d, P1 I). This middle right box (see expanded view below) is a scroll down box that shows all d, I pairs in the experiment. The second phase matched is calcium carbonate, another common cement phase. It matches six of the more intense d-spacings, so it has a slightly lower GOM than tricalcium silicate, which matched eight d-spacings. Using our integrated search software, a user can sort any column by clicking the column header. A simple click of the mouse at the head of the listing (see left) provided a sorted list based on reference d-spacings. A drag and click of the mouse can also expand the box within the display. In the case of tricalcium silicate, one can see that this phase alone accounts for 33 of the 59 identified peaks in the experimental pattern, as shown in the left display.

11 Once the major phases are identified they can be accepted by selecting the handshake icon at the top of the page (see the red circled icon above). Once selected, SIeve calculates a difference list of d, I pairs for further analysis. After two iterations the analysis has identified calcium trisilicate, calcium disilicate, calcite and gypsum; all known components of Portland Cement. Now the top candidate is a calcium iron oxide and the five phases account for 56 of the observed 59 peaks. Each phase is listed on the bottom right and compared to the experimental data. One can also see that now that we are near the end of the analysis as only a few peaks are matched as shown by the colored d-spacings. Red signifies a strong match and the color pink shows a weaker match as determined by the difference in the experimental and reference d-spacing. Since the majority of d-spacings have already been identified and assigned, the GOM figures that were several thousand (maximum scale 8,000 for the eight d-spacings) at the beginning of the analysis are now a few hundred since only 1 to 2 d-spacings are shown matching in the candidate lists. Close analysis of the d,i pair listing (bottom right) for the 56 identified d-spacings would show that every peak has one of the five phases as a contributor and most of the experimental peaks have contributions from several phases. The severe overlap of cement phases makes the analysis more difficult and emphasizes the importance of using filters, such as subfiles, to help with the identification. The largest unidentified peak is at Å. There are several possible cement candidates that could match this peak, but the overlap of the patterns would make further identification tenuous without additional supporting data, such as elemental analyses. Additional Notes: Cements analyses are often difficult due to the number of experimental phases with overlapping d-spacings. In the diffraction pattern, the region from 29 to 35 degrees two theta contains several strong overlapping peaks. SIeve has several tools that can help with difficult analyses. There is a choice of 3 matching algorithms Hanawalt, Fink and Long 8. The Hanawalt algorithm is the default algorithm and uses the most intense peaks in the pattern. Both Fink and Long 8 algorithms focus on the low angle peaks. In this case, the Long 8 algorithm would help identify gypsum and calcium iron oxide. There are options for weighing the match scheme, which helps identify simpler patterns such as lime (CaO), portlandite (Ca(OH) 2 ), and periclase (MgO). Finally, there is a pattern GOM, which identifies the material based on how well the reference pattern is matched. This reverses the process of the default GOM where matches are based on the fit to the experimental data. The pattern GOM, in this case, would help identify dicalcium silicate (larnite) and help differentiate it from other candidate cement phases that also have strong peaks between Å.

12 The region from 29 to 35 degrees two theta contains much of the pattern intensity and has intensity contributions from many phases. Possible low concentration phases, such as quartz, dolomite and lime would be difficult to resolve. In this case the user might want to perform a higher resolution analysis (i.e. smaller slits) and collect more total counts intensity data (longer collection times) to identify low concentration phases. The data shown above were from the same micronized sample, but different specimen where the instrument conditions were optimized for cement analysis. The intensity scale is 50X the previous analysis and the number of identified peaks went from 59 to 155. With the increased intensity and better resolution the phase identification was much easier and additional phases of anhydrite (CaSO 4 ), bassinite (CaSO H 2 O), quartz (SiO 2 ) and portlandite (Ca(OH) 2 ) were found. The calcium iron oxide was positively identified as brownmillerite. Overall, 151 d-spacings out of 155 were identified. The remaining unidentified peaks were of intensities less than 1% of the maximum peak. Conclusion Using PDF-2 along with a filter of cement and hydration products, we can identify five phases in a typical laboratory analysis of Portland cement, which are all the high concentration components in this mix. In an optimized analysis with improved signal to noise nine phases were identified. The use of subfiles search targets the appropriate chemistry and reduces the chance of false positive results. False positive results can be an issue in this type of analysis due to the number of overlapping peaks and multiple phases present in a typical cement. The ability to analyze nine phases shows the powerful combination of high quality data with a high quality reference database JCPDS International Centre for Diffraction Data

13