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Panchuk V, Petrov Y, Semenov V, Kirsanov D. Quantification of elements in spent nuclear fuel using intrinsic radioactivity for sample excitation and chemometric data processing. Anal Chim Acta 2023; 1239:340694. [PMID: 36628762 DOI: 10.1016/j.aca.2022.340694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/18/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022]
Abstract
Quantitative analysis of spent nuclear fuel (SNF) is a very challenging task. High radioactivity, complex chemical composition and personnel safety requirements severely limit the number of analytical tools suitable for this problem. There is an urgent need for the methods that would provide for remote on-line quantification of elements in spent nuclear fuel and its reprocessing technological solutions. Here we propose a novel approach based on the registration of X-ray fluorescence radiation from SNF samples induced by fission products radioactivity. In this case the X-ray excitation conditions will obviously vary from sample to sample; moreover the resulting spectra will be a complex superposition of numerous signals from soft gamma emitters and X-ray fluorescence of various nature. These complex spectra can be effectively treated with chemometric data processing for quantification of particular elements. We have demonstrated the validity of this approach for direct analysis of U, Zr and Mo in SNF raffinate.
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Affiliation(s)
- Vitaly Panchuk
- Institute of Chemistry, St. Petersburg University, St. Petersburg, Russia; Institute for Analytical Instrumentation RAS, St. Petersburg, Russia
| | - Yuriy Petrov
- Khlopin Radium Institute, St. Petersburg, Russia
| | - Valentin Semenov
- Institute of Chemistry, St. Petersburg University, St. Petersburg, Russia; Institute for Analytical Instrumentation RAS, St. Petersburg, Russia
| | - Dmitry Kirsanov
- Institute of Chemistry, St. Petersburg University, St. Petersburg, Russia.
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2
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Maurice AA, Theisen J, Rai V, Olivier F, El Maangar A, Duhamet J, Zemb T, Gabriel JP. First online X‐ray fluorescence characterization of liquid‐liquid extraction in microfluidics. NANO SELECT 2021. [DOI: 10.1002/nano.202100133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Ange A. Maurice
- SCARCE Laboratory Energy Research Institute @ NTU (ERI@N) Nanyang Technology University Singapore
| | - Johannes Theisen
- ICSM CEA CNRS ENSCM Université de Montpellier Marcoule France
- CEA IRIG INAC MEM Université Grenoble Alpes Grenoble France
| | - Varun Rai
- SCARCE Laboratory Energy Research Institute @ NTU (ERI@N) Nanyang Technology University Singapore
| | - Fabien Olivier
- SCARCE Laboratory Energy Research Institute @ NTU (ERI@N) Nanyang Technology University Singapore
- CEA CNRS NIMBE LICSEN Université Paris‐Saclay Gif‐sur‐Yvette France
| | | | - Jean Duhamet
- CEA DES ISEC DMRC Université de Montpellier Marcoule France
| | - Thomas Zemb
- ICSM CEA CNRS ENSCM Université de Montpellier Marcoule France
| | - Jean‐Christophe P. Gabriel
- SCARCE Laboratory Energy Research Institute @ NTU (ERI@N) Nanyang Technology University Singapore
- CEA IRIG INAC MEM Université Grenoble Alpes Grenoble France
- CEA CNRS NIMBE LICSEN Université Paris‐Saclay Gif‐sur‐Yvette France
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3
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A Novel Multi-Ionophore Approach for Potentiometric Analysis of Lanthanide Mixtures. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9020023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This work aims to discuss quantification of rare earth metals in a complex mixture using the novel multi-ionophore approach based on potentiometric sensor arrays. Three compounds previously tested as extracting agents in reprocessing of spent nuclear fuel were applied as ionophores in polyvinyl chloride (PVC)-plasticized membranes of potentiometric sensors. Seven types of sensors containing these ionophores were prepared and assembled into a sensor array. The multi-ionophore array performance was evaluated in the analysis of Ln3+ mixtures and compared to that of conventional monoionophore sensors. It was demonstrated that a multi-ionophore array can yield RMSEP (root mean-squared error of prediction) values not exceeding 0.15 logC for quantification of individual lanthanides in binary mixtures in a concentration range 5 to 3 pLn3+.
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Surkova A, Bogomolov A, Legin A, Kirsanov D. Calibration Transfer for LED-Based Optical Multisensor Systems. ACS Sens 2020; 5:2587-2595. [PMID: 32691588 DOI: 10.1021/acssensors.0c01018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Multivariate calibration transfer is widely used to expand the applicability of the existing regression model to new analytical devices of the same or similar type. The present research proves the feasibility of calibration model transfer between a full-scale laboratory spectrometer and an optical multisensor system based on only four light-emitting diodes with different wavelengths. The model transfer between two multisensor systems of this kind has also been studied. Both possibilities were successfully performed without any significant loss of precision using a designed set of training and transfer samples. Direct standardization and slope and bias correction protocols for model transfer were tested and compared. The best model transfer between two optical multisensor systems was obtained using direct standardization.
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Affiliation(s)
- Anastasiia Surkova
- Institute of Chemistry, St. Petersburg State University, Universitetskaya nab. 7-9, Mendeleev Center, 199034 St. Petersburg, Russia
- Samara State Technical University, Molodogvardeyskaya Street 244, 443100 Samara, Russia
| | - Andrey Bogomolov
- Samara State Technical University, Molodogvardeyskaya Street 244, 443100 Samara, Russia
- Endress+Hauser Liquid Analysis GmbH+Co. KG, Anthon-Huber-Strasse 20, 73430 Aalen, Germany
| | - Andrey Legin
- Institute of Chemistry, St. Petersburg State University, Universitetskaya nab. 7-9, Mendeleev Center, 199034 St. Petersburg, Russia
| | - Dmitry Kirsanov
- Institute of Chemistry, St. Petersburg State University, Universitetskaya nab. 7-9, Mendeleev Center, 199034 St. Petersburg, Russia
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Plutonium (IV) Quantification in Technologically Relevant Media Using Potentiometric Sensor Array. SENSORS 2020; 20:s20061604. [PMID: 32183104 PMCID: PMC7147468 DOI: 10.3390/s20061604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/03/2020] [Accepted: 03/10/2020] [Indexed: 01/03/2023]
Abstract
The quantification of plutonium in technological streams during spent nuclear fuel (SNF) reprocessing is an important practical task that has to be solved to ensure the safety of the process. Currently applied methods are tedious, time-consuming and can hardly be implemented in on-line mode. A fast and simple quantitative plutonium (IV) analysis using a potentiometric sensor array based on extracting agents is suggested in this study. The response of the set of specially designed PVC-plasticized membrane sensors can be related to plutonium content in solutions simulating real SNF-reprocessing media through multivariate regression modeling, providing 30% higher precision of plutonium quantification than optical spectroscopy.
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El Maangar A, Theisen J, Penisson C, Zemb T, Gabriel JCP. A microfluidic study of synergic liquid–liquid extraction of rare earth elements. Phys Chem Chem Phys 2020; 22:5449-5462. [DOI: 10.1039/c9cp06569e] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A membrane based liquid–liquid extraction microfluidic device coupled with X-ray fluorescence enables the first quantification of free energies of transfer dependence for a complex mixture of rare earth elements and iron using synergic extractants.
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Low-Input Crops as Lignocellulosic Feedstock for Second-Generation Biorefineries and the Potential of Chemometrics in Biomass Quality Control. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9112252] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Lignocellulose feedstock (LCF) provides a sustainable source of components to produce bioenergy, biofuel, and novel biomaterials. Besides hard and soft wood, so-called low-input plants such as Miscanthus are interesting crops to be investigated as potential feedstock for the second generation biorefinery. The status quo regarding the availability and composition of different plants, including grasses and fast-growing trees (i.e., Miscanthus, Paulownia), is reviewed here. The second focus of this review is the potential of multivariate data processing to be used for biomass analysis and quality control. Experimental data obtained by spectroscopic methods, such as nuclear magnetic resonance (NMR) and Fourier-transform infrared spectroscopy (FTIR), can be processed using computational techniques to characterize the 3D structure and energetic properties of the feedstock building blocks, including complex linkages. Here, we provide a brief summary of recently reported experimental data for structural analysis of LCF biomasses, and give our perspectives on the role of chemometrics in understanding and elucidating on LCF composition and lignin 3D structure.
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Panchuk V, Kirsanov D, Oleneva E, Semenov V, Legin A. Calibration transfer between different analytical methods. Talanta 2017; 170:457-463. [DOI: 10.1016/j.talanta.2017.04.039] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 04/14/2017] [Accepted: 04/16/2017] [Indexed: 11/25/2022]
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Khaydukova M, Panchuk V, Kirsanov D, Legin A. Multivariate Calibration Transfer between two Potentiometric Multisensor Systems. ELECTROANAL 2017. [DOI: 10.1002/elan.201700190] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Maria Khaydukova
- Saint Petersburg State University; Institute of Chemistry, Mendeleev Center; Universitetskaya nab. 7-9 199034 Saint Petersburg Russia
- Laboratory of artificial sensory systems; ITMO University; St. Petersburg Russia
| | - Vitaly Panchuk
- Saint Petersburg State University; Institute of Chemistry, Mendeleev Center; Universitetskaya nab. 7-9 199034 Saint Petersburg Russia
- Laboratory of artificial sensory systems; ITMO University; St. Petersburg Russia
| | - Dmitry Kirsanov
- Saint Petersburg State University; Institute of Chemistry, Mendeleev Center; Universitetskaya nab. 7-9 199034 Saint Petersburg Russia
- Laboratory of artificial sensory systems; ITMO University; St. Petersburg Russia
| | - Andrey Legin
- Saint Petersburg State University; Institute of Chemistry, Mendeleev Center; Universitetskaya nab. 7-9 199034 Saint Petersburg Russia
- Laboratory of artificial sensory systems; ITMO University; St. Petersburg Russia
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Bogomolov A. Diagonal designs for a multi-component calibration experiment. Anal Chim Acta 2016; 951:46-57. [PMID: 27998485 DOI: 10.1016/j.aca.2016.11.038] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 10/17/2016] [Accepted: 11/16/2016] [Indexed: 10/20/2022]
Abstract
Modern spectroscopic and sensor technologies combined with multivariate modelling are increasingly used for the quantitative analysis of complex mixtures. Their performance depends directly on the data design chosen for model training and validation. A well-balanced calibration experiment with the fewest samples possible presents additional challenges when several mixture components (factors) need to be calibrated on the same dataset and subsequently quantified from the same multivariate measurement. This practically important problem stays poorly addressed by the theory of experimental design. This theoretical work systematically formulates the requirements to an optimal calibration/validation dataset and introduces a new family of calibration designs, where the samples are placed along the diagonals of an experimental space that is a hypercube. Such placement is appropriate due to reasonable assumptions about the linear nature of analytical response. Suggested filling schemes allow economical diagonal designs with intrinsic validation to be built for multiple factors presented in as many levels as the number of samples. The most important practical cases of two and three factors are considered in detail, and generalization to higher dimensions is outlined. Diagonal designs of any complexity can be generated using a simple geometrical scheme or with a supplied script.
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Affiliation(s)
- Andrey Bogomolov
- Samara State Technical University, Molodogvardeyskaya Street 244, 443100 Samara, Russia; Global Modelling, Rembrandtstraße 1, 73433 Aalen, Germany.
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Debus B, Kirsanov DO, Panchuk VV, Semenov VG, Legin A. Three-point multivariate calibration models by correlation constrained MCR-ALS: A feasibility study for quantitative analysis of complex mixtures. Talanta 2016; 163:39-47. [PMID: 27886768 DOI: 10.1016/j.talanta.2016.10.081] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 10/20/2016] [Accepted: 10/23/2016] [Indexed: 11/26/2022]
Abstract
When it comes to address quantitative analysis in complex mixtures, Partial Least Squares (PLS) is often referred to as a standard first-order multivariate calibration method. The set of samples used to build the PLS regression model should ideally be large and representative to produce reliable predictions. In practice, however, the large number of calibration samples is not always affordable and the choice of these samples should be handled with care as it can significantly affect the accuracy of the predictive model. Correlation constrained multivariate curve resolution (CC-MCR) is an alternative regression method for first-order datasets where, unlike PLS, calibration and prediction stages are performed iteratively and optimized under constraints until the decomposition meets the convergence criterion. Both calibration and test samples are fitted into a unique bilinear model so that the number of calibration samples is no longer a critical issue. In this paper we demonstrate that under certain conditions CC-MCR models can provide for reasonable predictions in quantitative analysis of complex mixtures even when only three calibration samples are employed. The latter are defined as samples having the minimum, the maximum and the average concentration, providing for a simple and rapid strategy to build reliable calibration model. The feasibility of three-point multivariate calibration approach was assessed with several case studies featuring mixtures of different analytes in presence of interfering species. Satisfactory predictions with relative errors in the range 3-15% were achieved and good agreement with classical PLS models built from a larger set of calibration samples was observed.
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Affiliation(s)
- B Debus
- Institute of Chemistry, St. Petersburg State University, St. Petersburg 199034, Russia.
| | - D O Kirsanov
- Institute of Chemistry, St. Petersburg State University, St. Petersburg 199034, Russia; Laboratory of Artificial Sensory Systems, ITMO University, St. Petersburg 197101, Russia.
| | - V V Panchuk
- Institute of Chemistry, St. Petersburg State University, St. Petersburg 199034, Russia; Laboratory of Artificial Sensory Systems, ITMO University, St. Petersburg 197101, Russia
| | - V G Semenov
- Institute of Chemistry, St. Petersburg State University, St. Petersburg 199034, Russia
| | - A Legin
- Institute of Chemistry, St. Petersburg State University, St. Petersburg 199034, Russia; Laboratory of Artificial Sensory Systems, ITMO University, St. Petersburg 197101, Russia
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