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HAL Id: cea-02439451

https://hal-cea.archives-ouvertes.fr/cea-02439451

Submitted on 26 Feb 2020

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Qualitative and quantitative analysis of materials by

LIBS in nuclear environment how multivariate methods

can help.

J-B. Sirven, M. El Rakwe, N. Coulon, D. l’Hermite, E. Vors

To cite this version:

J-B. Sirven, M. El Rakwe, N. Coulon, D. l’Hermite, E. Vors. Qualitative and quantitative analysis of materials by LIBS in nuclear environment how multivariate methods can help.. LIBS 2016, Sep 2015, Chamonix-Mont-Blanc, France. �cea-02439451�

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www.cea.fr

QUALITATIVE AND

QUANTITATIVE ANALYSIS

OF MATERIALS BY LIBS IN

NUCLEAR ENVIRONMENT:

HOW MULTIVARIATE

METHODS CAN HELP

J.-B. Sirven, M. El Rakwe, N. Coulon, D.

L’Hermite, E. Vors

CEA, Nuclear Energy Division, Department of Physical Chemistry F-91191 Gif sur Yvette, France

jean-baptiste.sirven@cea.fr

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NUCLEAR ENVIRONMENT

© CEA

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NUCLEAR ENVIRONMENT

| PAGE 3 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

© CEA

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NUCLEAR ENVIRONMENT

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NUCLEAR ENVIRONMENT

| PAGE 5 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

© CEA

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ELEMENTAL ANALYSIS IN

NUCLEAR ENVIRONMMENT

Samples containment In situ analysis No standards Complex matrices Maintenance is difficult High robustness required Resistance to radiation Radioactivity CONSTRAINTS INSTRUMENTATION ANALYSIS

Motivation for

chemometrics

Optimize a measurement Identify samples Improve quantitative analysis Nuclear materials

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1/3

EXPERIMENTAL DESIGNS TO

IMPROVE THE ROBUSTNESS

OF THE ANALYSIS

Samples containment In situ analysis No standards Complex matrices Maintenance is difficult High robustness required Resistance to radiation Radioactivity CONSTRAINTS INSTRUMENTATION ANALYSIS

Motivation for

chemometrics

Optimize a measurement Identify samples Improve quantitative analysis Nuclear materials

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OPTIMIZATION OF INSTRUMENTAL

PARAMETERS USING A DESIGN OF

EXPERIMENTS

Objective: To optimize experimental parameters in ordre to obtain the best

analytical performances, with a user point of view and a minimum of

experimental trials

sample

f

Φ

δ

la

ser

E

detection

system

t

d

4 responses:

Ablated volume Line ratio (plasma temperature indicator) Signal intensity Signal repeatability

 Design Of Experiment

5 factors:

pulse energy (E) beam diameter (Φ) focal length (f)

focal plane – sample distance (δ) gate delay (td) Physical responses Analytical responses

Maria El Rakwe PhD thesis

Taking into account that

The relation between factors and responses is non-linear

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OPTIMIZATION OF INSTRUMENTAL

PARAMETERS USING A DESIGN OF

EXPERIMENTS

Experiment:

266 nm Nd:YAG laser

Czerny-Turner monochromator, 300 mm, 2400 g/mm grating

ICCD

Sample:

SS59 standard: Fe 99.6 % ; Mn 0.12 %

| PAGE 9 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

Central composite DOE (32

experiments)

1. Modeling of relation between factors and responses

2. Simultaneous optimization of analytical responses

3. Check of experimental reproducibility

4. Experimental validation of the predictive ability of the model

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OPTIMIZATION OF INSTRUMENTAL

PARAMETERS USING A DESIGN OF

EXPERIMENTS

Experimental reproducibility

Inte

n

sity

(a

.

u

.)

Wavelength (nm)

̶ ̶ June 2015

(DOE experiments)

̶ ̶ June 2016

(validation experiments)

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OPTIMIZATION OF INSTRUMENTAL

PARAMETERS USING A DESIGN OF

EXPERIMENTS

Model validation

| PAGE 11 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

Responses

Predicted values

Experimental

values

Relative

deviation

Mn signal intensity

7260 ± 1740 7530 - 3.7%

Mn signal RSD

1 % (< 6 %) 3.5 % + 250%

Ablated volume (µm

3

)

(1.45 ± 0.16) 105 1.27 105 - 12%

Line ratio (plasma

temperature indicator)

1.7 ± 1.7 2.9 + 68%

The model is satisfactorily predictive  it can be used:

To determine the effect of the chosen experimental parameters on different

responses (e.g. SBR, LOD…)

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2/3

MATERIALS IDENTIFICATION

BY ONSITE LIBS SYSTEMS

Samples containment In situ analysis No standards Complex matrices Maintenance is difficult High robustness required Resistance to radiation Radioactivity CONSTRAINTS INSTRUMENTATION ANALYSIS

Motivation for

chemometrics

Optimize a measurement Identify samples Improve quantitative analysis Nuclear materials

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ONSITE IDENTIFICATION OF

MATERIALS

Motivation:

Inventory before nuclear decommissioning Control of contamination

Identification of wastes prior to treatment / sorting

| PAGE 13 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

Sampling characteristics :

Depth ~ 1 µm / laser shot Analyzed Area ~ (200 µm)² Ablated mass ~1 ng / laser shot

Spectrometer Laser

Probe

> 10 m

Safe area Contaminated area

Laser Spectrometer Probe Size 10cmx10cm Weight < 1Kg Optical fibers RICA robot developed at CEA for in situ measurements in nuclear environment Fibered

2 technologies:

Portable

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ONSITE IDENTIFICATION OF

MATERIALS

Examples of data treatment

Single-shot, low resolution

spectra (portable system)

Cu alloys

Zr alloys

Al alloys Ti alloys

alloys and metals Other

SS 316L PEBD

Concrete

Spectrum of an unknown sample

Spectra of material 1 Spectra of Spectra of material 2 5 nearest neighbors 200 nm 1000 nm

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ONSITE IDENTIFICATION OF

MATERIALS

| PAGE 15 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

50 spectra of concrete + 430 spectra of glass + 1260 spectra of alloys + 660 spectra of plastics

KNN

alloys glass concrete plastics

100 % 98 % 97 % 98 % steels + Fe-based + Ni-based Al-based 100 % 100 % Pure metals: Pb, Sn, W, Zn, Mo, V, Ag, Au,

Pt, Mn, Ga, Ta, Cr 100 % for each metal Cu-, Zr-, Ti-based alloys, BiTe, InPbZnSn 100 % for each alloy 1xxx series 66 % 2xxx series 89 % 2xxx series+Ti 89 % 4xxx series 100 % 6xxx series 100 % pure 100 % Ni-based 96 % Fe-based 100 % steel 99 % maraging 100 % stainless 86 % high Cr 97 % KNN KNN KNN KNN KNN other

PTFE, PVDF, Neop, SAN, Viton

100 % for each plastic

other PS 75 % KNN other ABS 70 % KNN other PP 60 % KNN other PE 60 % KNN other PA 60 % KNN other PC + PET 50 % KNN PUR 40 % PVC 60 % KNN EVA 20 % EPR 0 %

X % = rate of correct identification obtained at each step for the test set

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3/3

Samples containment In situ analysis No standards Complex matrices Maintenance is difficult High robustness required Resistance to radiation Radioactivity CONSTRAINTS INSTRUMENTATION ANALYSIS

Motivation for

chemometrics

Optimize a measurement Identify samples Improve quantitative analysis Nuclear materials

QUANTITATION APPROACHES

BASED ON THE EXPLOITATION

OF THE SPECTRAL AND

TEMPORAL DIMENSIONS OF

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CALIBRATION BY LIBS

Calibration of [Ni] in…

| PAGE 17 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

Gate delay = 1.5 µs

Gate width = 3 µs

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EFFECT OF TIME RESOLUTION ON

CALIBRATION LINES

Gate delay 1.5 µs Gate delay 9.5 µs

Gate width 0.5 µs Gate width 3 µs

Ni in…

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CALIBRATION USING

TIME-RESOLVED SPECTRA

What if we take into account the temporal dimension of the LIBS signal?

1.

Does it improve analytical performances?

2.

Can we take advantage of it to correct matrix effects?

| PAGE 19 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

Wavelength wavelength (nm) [Cu]1 [Cu]2 [Cu]3 [Cu]5 [Cu]4 [Cu] S p ec tr a S1 S2 S3 S5 S4

?

multivariate [Cu] (%) P re dic ted [C u] (% ) Maria El Rakwe PhD thesis

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A PRACTICAL EXAMPLE

Dataset = calibration of [Cu] in Ni

a

1

a

2

a

3

a

4

Mostly Ni (+ Cr) Mostly Ti (+ Cr) Cu Continuum + most intense reversed Ni lines Wavelength [Cu] S p ec tr a

For each given delay and [Cu]

concentration:

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CALIBRATION LINE VS PLS

VS ICA-PLS

| PAGE 21 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

Calibration Trueness Precision LOD (ppm)

Cu line intensity 900 Cu line / background 400 PLS 350 ICA-PLS 180 Cu 327.4 nm line intensity PLS Cu 327.4 nm line / background ICA-PLS Delay 0.8 µs Width 1 µs Delay 0.8 µs Width 1 µs Delay 0.8 µs Width 1 µs Delay range 0.03-2 µs Width 1 µs

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MULTI-MATERIALS QUANTITATIVE

ANALYSIS

[Cu] (%at) Cu l ine intens ity

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CONCLUSION

Elemental analysis in nuclear environment:

Strong instrumental constraints Complex spectra

Few (sometimes no) standards available

Approaches based on Boltzmann modeling (CF-LIBS, Cσ, FP…) difficult to implement

Chemometrics is useful to overcome (to a certain extent) those difficulties

(but it is not magic… and requires careful validation…)

| PAGE 23 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

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DEN DANS DPC Commissariat à l’énergie atomique et aux énergies alternatives

Centre de Saclay| 91191 Gif-sur-Yvette Cedex T. +33 (0)1 69 08 43 71

Thank you for

your attention!

jean-baptiste.sirven@cea.fr

Acknowledgements:

Ayoub Aouam

Rémi Beaubaton

Alix Dehayem-Massop

Guillaume Gallou

Tiphanie Lelong

Gilles Moutiers

Laurie Pontreau

Sébastien Robino

Douglas N. Rutledge

Thomas Vercouter

| PAGE 24

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ABSTRACT

Elemental analysis in the nuclear field is essential in a number of situations, from the front-end of the fuel cycle to the waste management, either for process control, compliance checking, or waste characterization prior to conditioning. Yet, it is special for two main reasons.

First, due to their radioactivity, the samples are confined inside containments such as

controlled areas, glove boxes or hot cells. Those safety and security constraints inherent in nuclear environment strongly restrict the possibility of sampling, transportation and handling of materials. Therefore, onsite or even in situ analytical techniques, such as LIBS, are of high interest, as they enable to limit the workers exposure to radiation and contamination, as well as the production of waste and effluents generated by conventional laboratory analysis. One important consequence is that the instruments must be very robust and require as little

maintenance as possible.

Secondly, the matrices can be very complex due to the presence of many interfering elements including actinides and lanthanides. And a large variety of materials need to be characterized (fuel, alloys, glasses, concrete, solutions, etc.), both qualitatively and quantitatively. In this latter case, calibration samples are not always available and alternative approaches must be developed.

As shown by the abundant literature on the subject in other areas of analytical spectroscopy, multivariate methods are very helpful to cope with such issues. We will show how similar strategies can be implemented in LIBS, taking into account the peculiarities of the technique. Different applications of interest in the nuclear field will be reviewed: materials identification by onsite LIBS systems; use of experimental designs to improve the robustness of the analysis; innovative quantitation approaches based on the exploitation of the spectral and temporal dimensions of the LIBS signal; and isotopic analysis by an original method based on the self-absorption of intense emission lines.

| PAGE 25 Sirven et al. | Qualitative and quantitative analysis of materials by LIBS in nuclear environment: how multivariate methods can help

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