Material Identification – the Good, Bad and Ugly

T.G. Fawcett, J. R. Blanton

International Centre for Diffraction Data,

Newtown Square, PA, USA 19073

Instructors

ICDD, Executive Director 2001-2017

The Dow Chemical Company1979-2001

ICDD, Manager Engineering and Design

• Decades of analyzing problems by diffraction methods

• Design databases forMaterial identification

• Develop software forMaterial identification

• Develop and create brochures,video’s and pubications

Outline

Introduction 15 min

What’s good, bad and ugly – and why ?

Good (Oyster Shell, Bauxite) 15 min

Automated Methods – How to handle good data 30 min

Bad (Portland Cement) 15 min

Break

Very Bad (Drill Core, Metal Die) 10 min

Methods for better material identification – How handle ugly data 20 min

Ugly (Allegra, Pristiq, Roman Coins) 20 min

Tools for better material identification – and how they work ? 15 min

Applic-o-meter

Theory Application

n

Week long instruction – XRD I

• Theory• Applications• Hands-on-workshops

Resources

at www.icdd.com

Tutorials and videos

Technical Bulletins

softwareapplications

Publications

Powder DiffractionAdv in X-ray Analysis

Introduction to material identification

Phase Identification is called a fingerprint technique

Match the experimentwith a reference

Unknown specimenCompared to a reference

8

X-ray Diffraction - Bragg’s Law

θθd

When this extra distance is equal to one wavelength, the x-rays scattered from the 2nd plane are in phase with x-rays scattered from the 1st plane (as are those from any successive plane). This “constructive interference” produces a

diffraction peak maximum at this angle. This is the basis of Bragg’s Law: λ= 2d·sinθ

X-rays scattered from the second plane travel the extra distance depicted by the yellow lines. Simple geometry shows this distance = 2d·sinθ

Incident coherent X-ray beam

d = interplanar spacing of parallel planes of atoms

XRPD Pattern for NaCl

(1 1 1) d = 3.256 Å2θ = 27.37°

°

λ = 1.5406 Å (Cu Kα1)a = 5.6404 Å

Bragg’s law prescribes the 2θ angular position for each peak based on the interplanar distance for the planes from which it arises. 2θ = 2 asin(λ/2d)

(2 0 0) d = 2.820 Å2θ = 31.70°

(2 2 0) d = 1.994 Å2θ = 45.45°

(3 1 1) d = 1.701 Å2θ = 53.87°

(2 2 2) d = 1.628 Å2θ = 56.47°

Each material that has a unique arrangement of atoms will produce peaks that correspond to interplanar distances between atom planes. The intensity of these peaks are proportion to the number of contributing atoms and the type of atom.

If the atoms are in a crystalline periodic array, coherent diffraction will result based on Bragg’s law.

If the atoms aren’t in a periodic array, diffuse scattering will result based on Debye’s scattering theory that considers interactions between adjacent atoms. The scattering intensity and distribution be a function of number of atoms and atom type

Why It Works

• Each crystalline component phase of an unknown specimen produces its own X-ray powder diffraction (XRPD) pattern.

• These patterns arise from the crystal structures of the component phases and are, at least in principle, unique.

• The XRPD pattern for a multi-component mixture consists of the weighted sum of the individual XRPD patterns for each component in the mixture.

CaSO4•2H2O CaO Ca(OH)2 CaCO3

D-spacings and Intensities

Simple – Control wavelength and angleand you can accurately determine d-spacingsto a part per thousand (ppm with calibration)

Complex (very powerful) – Many variables typically leads to more uncertainty in thevalues. The structure factor F is also multi-variant.

Perfect specimen preparation, great data………wrong answers

• How do you know the answers are wrong• How to get the right answers

Most powerful material identification tool

ever created

What your brain knows that your computer doesn’t

Where the sample came from

How the sample was made (or was it dug up or purchased)

The samples composition

Additional analytical data (not diffraction) that is relevant to the analysis

Is the sample a powder, gel, a solid piece, tar, goo or gunk !

What does the sample look like

Color

Crystal habit

What do the data look like

Sharp peaks, very broad features

Peaks of the right intensity in the right location

Overlapping peaks – how crowded is your data

Peak shape and symmetry

What does the background look like –what is it telling you

How difficult it was to prepare to a powder

Soft

Hard

Brittle

Waxy/deforms

All photo’s taken on a cell phone

What are these data sets telling you ?

What are these data sets telling you ?All data sets taken under routinelaboratory conditions

85,000 counts !Very sharp peaks

Orientation

• Very crowded pattern • Data scan started at 20 degrees• Intense peak at high angles

Missing data & granularity

• Weak signal 800 counts• Unusual background

Absorption (Pb) and surface roughness

Where is the baseline ?

A mix of crystalline andnon-crystalline compounds

What are these data sets telling you ?

Orientation• Regrind the sample• Apply an orientation function to the data

Missing data & granularity

Start data collection at 5 degreesRegrind the sample

A mix of crystalline andnon-crystalline compounds

Use a pattern fitting method to identify crystalline and non crystalline materialsAbsorption (Pb) and surface roughness

• Improve counting statistics• - multiple scans, longer count times• Polish surface (if possible)• Use a different radiation (reduce absorption and

increase depth penetration)

What are the data sets telling you ?

In all of the previous data sets you can identify most of the materials in the specimen using the data provided– if you identify the problem and take it’s effects into consideration

Orientation can be mathematically corrected, granularity (large grains) cannot

Orientation and granularity effect intensities, d-spacing can still be used to identify the materials

In one case the surface roughness and absorption explain the unusual background. In the other case the unusual background is being caused by the material you need to identify – they need to be treated differently to get the right results.

Know your InstrumentThese data sets were all taken on the samedesktop diffractometer with ~ 0.02 step size and~1 hour scans. Hundreds of data sets collected on this instrument produce a maximum intensity from5,000-30,000 counts depending on the materialand number of phases.

Sample A – 80,000 counts, 1hr - too highSample B – 800 counts, 1hr - too lowSample C – 35,000 counts, 2 hrs, about right

A

B

C

Too sharp ? – What is the peak width of myInstrument standard (NIST SRM - Si, LaB6)

The Good

The Click of a Mouse gets you the answer you desire

EVA, HighScore, Jade, Match, PDXL, Sieve (PDF-2/PDF-4+)

Steps in the treatment of diffraction data

From “Introduction to X-ray Powder Diffractometry “ by Jenkins & Synder (John Wiley & Sons, Inc)

Oyster Calcium

Reference PDF 04-077-4388Calcium CarbonateMineral Calcite

X-ray Powder DiffractionIdentification of oyster shell

Experiment Reference

26

Note the match to 5 significantfigures in d-spacing

Why is this good data

14,000 cts peak Intensity – easily see 1% peaks

Peaks are well resolved with good peak shape

Background is very low, easily defined, and has large regions of low intensity

0.4, 1…….. 2, and 1 Intensity

Nisinovici et al 1987-1993

42,020Minerals

Where aren’t there peaks

Identification Process

33 experimental peaks found

Compared 383,535 References

61 Have at least 2 peaks that match the experiment

1 Material, calcite, matches 30 of 33 peaks accurately !

(Note other 3 peaks match quartz, SiO2 and dolomite)

Simple “Easy” Phase ID

This data sets has no instrumental or specimen errors

(i.e. displacement, transparency or molecular orientation)

Peaks are well separated and do not require deconvolution

Large areas with no signal eliminates many possible candidates

Synthetic Bauxite

Good Signal to Noise

Good resolution not much peak overlap

Very low background, easily defined

148 Peaks are identified

Identification Process

33 experimental peaks found149 experimental peaks found

Compared 383,535 References

61 have at least 2 peaks that match the experiment529 have at least 2 peaks that match the

experiment

1 Material, calcite, matches 30 of 33 accurately !7 Materials match all 149 peaks !

(Note other 3 peaks match quartz, SiO2)

All 148 peaks accounted for !

10 X scale

Justin

How can the good go wrong !

Don’t identify all the possible peaks

Don’t have the right reference patterns

Synthetic BauxiteIUCr RR sample

Most automated systems do not find 137 peaks in automated mode !

Perfect sample – perfect data – DON’T GET THE RIGHT ANSWER !

Automated Analyses

Software A = 44 peaks all peaks ht.> 3% 4 phases Software B = 105 peaks all peaks ht.> 1% 7 phasesSoftware C = 69 peaks all peaks area > 7% 6 phasesSoftware D = 98 peaks all peaks areas > 2% 5 phases*

ICDD has found in phase identification round robin testing that the number of peaks in a pattern had to be specified, prior to testing, so that the tests were not biased by the analysts/software ability to identify peaks !

* Additional phases are top candidates

Material Identification Round Robin Testing by ICDD and IUCr

If specimens were provided the testing was biased relative to the analysts ability to prepare a finely ground random powder.

If data sets were provided, the results were biased by the analysts ability to identify peaks

If data sets were provided, results were effected by the ability of the analysts to use appropriate reference standards (both quality and coverage)

Human error dominates round robin results !

The Bad

The Bad

Is there a baseline ?

Extensive peak overlap

What’s a peak, a shoulder, just noise ?

Poor signal to noise – why with 1-2 hour scans ?

Bad and Ugly Attributes

USP Method <941> X-ray Powder Diffraction

A Bad Case –Portland Cement

Overlappingpeaks

Portland Cement

Portand Cement

8 phases, ~ 100-200 peaks

Many, many shoulders

“Automated” phase ID gets 2-5 phases

Databases make a difference as not all databases have high

quality references or coverage for all phases

Most databases do not have a cement subfile – requiring more user expertise (what makes sense)

Portland CementNo filters

CementsA B3 X5Ca3 Si O5

Mineral Filter

Major phasesC3S, C2S, Anhydrite, BrownmilleriteOffset Plot

EVA, HighScore, Jade, Match, PDXL, Sieve (PDF-2/PDF-4+)

Portlandite (4.92)

Black = references added and scaled

2nd C2S phase

Peak asymmetry and overlapExplained by C3S and a second C3S phases

9 phases !

HaturiteAllite

Portland Cement

191 Peaks identified

5 phases > 5 weight %4 phases 1-3 weight %

Portland Cement

Technique

10 hour scan* – 105,000 counts

Small step size 0.02 or less

Large sample in a cavity mount

Well ground, random powder

Method

Look for shoulders, small peaks

Analyze peak assymmetry

Use subfiles to reduce candidates (use known cement phases and chemistry)

Use summation plots, difference plots and other graphics tools

Use databases with the appropriate references !

• Difficult problems require best methodsto improve signal to noise and resolution

7 Phase Bauxite

9 Phase Portland Cement

Perfect specimen preparation, great data………wrong answers

• How do you know the answers are wrongUse plots, graphs, subfilesIs the chemistry appropriate, does the answer look rightDo your answers match the appearance of the sample

• How to get the right answersUse good technique, right methods (subfiles and filters)Check a summation plot – do all peaks and intensities matchCheck peak profiles – does your answer explain shoulders and

asymmetryUse the right database having the appropriate references

More bad cases -

Too many phases – bad peak overlap

Die - Sample courtesy of Jannaz Tavadi, ArcelorMittal, Chicago

Core Drill Sample – courtesy of Prof. Christie Rowe, McGill University & USGS

Missing Data - Collected

Identified CaF2

The low angle data confirmed pseudo wollastonite and eliminated several other possible phases

Combined simulated pattern (black) contains

• CaF2

• Pseudo-Wollastonite

• Dolomite

• Wollastonite

• Quartz

2017-0639

Now we add Cuspidene but still several unidentified peaks with significant peaks at 4.19 and 2.66

A (100) orientation on the cuspidinehelps the intensity match better than shown but doesn’t account for missing peaks.

Casting Die

Could identify seven phases

Low angle data clarified cuspidene identification and ruled out other phases

Couldn’t resolve all customer questions (low concentration phases)

Still have granularity issues

Need more data on well ground samples – may need to separate phases either physically or through thermal analysis to identify all low concentration phases – or get a high resolution data set.

Assymmetric Crystals and GranularityPharmaceutical Round Robin

Acetaminophen & Si

Mannitol & Si

Courtesy Andy Fitch, ESRF

GranularityGranularity

Scattering power falls as a function of angle (top right graph) so we shouldn’t have strongpeaks at high angles

Comparing Good to Bad

Good

33 experimental peaks found

Compared 383,535 References

61 Have at least 2 peaks that match the experiment

1 Material, calcite, matches 30 of 33 accurately !

Bad

101 experimental peaks found

~20 peaks as shoulders

Compared 383,535 References

8,491 match at least 2 peaks

705 match when strong unmatched lines considered

Best matches only match 6-8 peaks

HELP !

Experiment

Collect low angle data

Use better counting statistics

(collect longer each data point)

Regrind the sample

Compare to similar data

Separate the phases

Problem

What is the sample history

Do you know any composition

Is it a mineral, synthetic, cement, pigment etc. etc.

US Geological Survey Drill Core Samples -Palmdale, California

Courtesy of Prof. Christie RoweEarth and Planetary SciencesMcGill University, Montreal, Canada

Los AngelosAquaduct

Use Mineral classifications

Quartz, Feldspar, Zeolite, Chlorite

Areas of peak overlap

and peak symmetry

(NaCa)AlSi3O8(NaK)AlSi3O8Montmorillonite

Clinochlore

Summation Plot

7 Phase Bauxite

9 Phase Portland Cement

8 Phase Drill Core

7 Phase Metal Die

Garth

XRD Garth

The Ugly

The Ugly

Where is the baseline ? Is there a baseline ? Extensive peak overlap What’s a peak, a shoulder, just noise ? Poor signal to noise – why with 1-2 hour scans ? Amorphous materials and/or nanomaterial –

problems for background and peak finding algorithms problems

Ugly

Where is the baseline ?

The Ugly

Where is the baseline ?

Is there a baseline ?

Peak overlap is extensive

Poor signal to noise – why with 1-2 hour scans ?

Amorphous materials and/or nanomaterial give peak finding algorithms problems

Baseline – which one is right ?

Peak finding – Which one is right

Forces a fit to an incorrect answer

Did not use an ICDD database – appropriate materials not in this database

Why this data set is ugly !The data are from a mixture of crystalline (sharp peaks) and amorphous materials (broad peaks).Data analyses have difficulty finding both an appropriate background and peak positions. Users canset to algorithms to handle sharp or broad peak in most commercial software – but not both.

This requires user input to correctly identify/locate both sharp and broad peaks

Use a similarity index

that examines every point in a point by point comparison to a reference

Justin

Lots of peaksLots of peak overlap and shouldersUnstable baselineOverall weak intensity – poor signal to noisePeak overlap means that not all peaks at are theirexpected positions

The Ugly Case of Allegra

Whole tablet pattern

Fines concentrate Fenofexadine HCLSynchrotron

Separate out the componentsby particle size

Take multiple data sets

Fines concentrate Fenofexadine HCLLaboratory data

Fenofexadine HCLActive ingredient

Shell

• Anatase• Glucose

• Cellulose Iβ• Amorphous

cellulose

Anatase and Glucose

Experimental digital patters for Nanocrystalline and non-crystalline materials

Microcrystalline celluloseGelatinized StarchPovidone

Microcrystalline celluloseand Povidone

Microcrystalline cellulose

Starch

Allegra

D-MannitolD-Mannitol hydrateFexofenadine HClD-Mannitol (polymorph)

FinesFexofenadine HCL

ShellAnataseSucroseCellulose IβAmorphous CellulosePovidoneStarch

Whole TabletD-MannitolD-Mannitol HydrateD-Mannitol

Fines

Shell

Ground Tablet

10 phase Pharmaceutical Tablet- Allegra, Uses all Tools

6 Crystalline phases2 nanocrystalline phases2 amorphous phases Note the incredible number

of reference peaks

Best Methods

Technique

Case 2 - Really Ugly, Pristiq

Pharmaceuticals and Excipients Search All Phases

Pristiq – second specimen

Polymorph I

• Hydroxypropyl cellulose• rac-desvenlafaxine succinate monohydrate (I); Pristiq• Talc

Ugly Case 3 – Roman Coins

Not much signalTerrible backgroundVery broad peaks

Compare to similar data

Data collected on 24 Roman Coins

Ugly coins Good coins

14,000 counts1700 counts

Tin Oxide- Casserite

Lead Carbonate

Don’t see much Cu or Cu2Oin these bronze coin –dominant phases in the other Roman coins

Pb and Sn !

Mass attenuation coefficients

IF we have Ag, Sn or Pb – attenuation will be ~>4X that of copper when using Cu radiationbecause the x-ray absorption is effected by attenuation and density

IF we compare XRD (Cu) to XRF (Ag, Rh) data there is a 3-10 X Factor in sampling volume

If we calculate a half-depth of penetration, our data is coming from the top micron of the coinIn coins with high Ag, Sn and/or Pb. The surface features of the coin vary by ~50 microns

If we calculate a half-depth of penetration, our data is coming from the top micron of the coin

In coins with high Ag, Sn and/or Pb. The surface features of the coin vary by ~50 microns

Ugly Case 3

Instability in the background partially due to variable surfaceCorrosion products are on the surface – poorly crystallineNot much diffraction because not much depth penetration – greatlyreduced sampling volume using Cu radiation

Cu Ag Pb Sn Cl39 3.9 23.6 8.7 0.4

Si Ca P Al Fe10.4 7.3 2.0 1.7 1.0

XRF Analysis

XRD Analysis

• 37 % SiO2• 27 % CaAlFeSilicate• 20 % CaCO3• 6 % SnO2• 4 % PbCO3• 2 % Cu2O• 3 % Ag

This is a nano phase and identification is supported by XRF analysis

Best Methods

ComplimentaryTechniques

• Absorption by heavy metals limits diffraction to the top surface of the coins

• Uneven surface (coins images), small crystallite size both contribute to and uneven background

• Used XRD combined with XRF to determine phases and their relative concentrations

Justin

Data attributesGood Data

Good signal >10,000 cts

Low noise < 200 cts

Peaks are symmetrical

Areas with no peaks

All peaks are sharp (i.e. crystalline)

Bad Data Areas with overlapping

peaks

Lots of shoulders

High noise

Few or no areas without peaks

Assymmetric peak profiles

Ugly Data Where is the baseline ?

Do you have a baseline ?

Combinations of sharp and broad peaks ?

Amorphous or nanocrystalline phases in a complex matrix (many phases)

Peak profiles show asymmetry, shoulders etc

Peaks are at specified reference positions

Not all peaks at reference positionsdue to peak overlap and merging

Peaks not in proper position due tooverlap/merging or low crystallinity ornon-crystallinity – probably requireswhole pattern fitting methods

Solutions

Good Data but bad results

You don’t have the right database and quality reference materials

Did you find all the peaks –

LOOK AGAIN

With good data, a modern diffractometer and agood database – you shouldn’t have a problem !

Solutions

Bad Data

Increase your resolution reducing peak overlap (smaller slits, more monochromatic radiation, smaller steps)

Pay attention to peak asymmetry – an indicator of multiple overlapping phases

Make sure you identify all peaks including shoulders and asymmetry – this probably requires manual inspection and adjusting program parameters

Ugly Data

Try to separate the materials physically or thermally – take multiple data sets

Consider similarity indexes or other whole pattern recognition programs for non-crystalline materials.

Improve your statistics (longer count times) and resolution – do both if possible

Use subfiles or input known chemistry into your search match – eliminating inappropriate phase candidates

Take into consideration peak symmetry and breadth

Hard problems usually require complimentary analytical dataHint: If you search/match does not produce common

sense results – regrind the sample, prepare it again and runa second data set. The original data probably have a transparency,displacement, orientation or granularity problem.

Synthetic BauxiteIUCr RR sample

Easy

Gibbsite (Al(OH)3) 54.90 %

Hematite (Fe2O3) 10.00%

Boehmite (Al(OH3) 14.93%

Goethite 9.98%

Moderate

Quartz 5.16%

Kaolin 3.02%

Anatase 2.00%

Key to the analysis- How many peaks did you

find ?

If you find all 148 (or >110) – you will find all phases

If you find only the strongest peaksyou find 5 phases

Most automated systems do not find 148 peaks in default mode.

Data from threeVendors using CPD data