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Mistry A, Johnson ID, Cabana J, Ingram BJ, Srinivasan V. How machine learning can extend electroanalytical measurements beyond analytical interpretation. Phys Chem Chem Phys 2024; 26:2153-2167. [PMID: 38131627 DOI: 10.1039/d3cp04628a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Electroanalytical measurements are routinely used to estimate material properties exhibiting current and voltage signatures. Analysis of such measurements relies on analytical expressions of material properties to describe the experiments. The need for analytical expressions limits the experiments that can be used to measure properties as well as the properties that can be estimated from a given experiment. Such analytical relations are essentially solutions of the physics-based differential equations (with properties as coefficients) describing the material behavior under certain specific conditions. In recent years, a new machine learning-based approach has been gaining popularity wherein the differential equations are numerically solved to interpret the electroanalytical experiments in terms of corresponding material properties. Since the physics-based differential equations are solved, one can additionally estimate underlying fields, e.g., concentration profile, using such an approach. To exemplify the characteristics of such a machine learning assisted interpretation of electroanalytical measurements, we use data from the Hebb-Wagner test on a magnesium spinel intercalation host. As compared to the traditional analytical expression-based interpretation, the emerging approach decreases experimental efforts to characterize relevant material properties as well as provides field information that was previously inaccessible.
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Affiliation(s)
- Aashutosh Mistry
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, USA.
- Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Ian D Johnson
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, USA.
- Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Jordi Cabana
- Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, USA
- Department of Chemistry, University of Illinois at Chicago, Chicago, Illinois 60607, USA
| | - Brian J Ingram
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, USA.
- Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Venkat Srinivasan
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, USA.
- Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, USA
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Modak SV, Shen W, Singh S, Herrera D, Oudeif F, Goldsmith BR, Huan X, Kwabi DG. Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques. Nat Commun 2023; 14:3602. [PMID: 37328467 DOI: 10.1038/s41467-023-39257-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/30/2023] [Indexed: 06/18/2023] Open
Abstract
Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.
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Affiliation(s)
- Sanat Vibhas Modak
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Wanggang Shen
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Siddhant Singh
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Dylan Herrera
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Fairooz Oudeif
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Bryan R Goldsmith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xun Huan
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - David G Kwabi
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
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Leveraging graphical models to enhance in situ analyte identification via multiple voltammetric techniques. J Electroanal Chem (Lausanne) 2023. [DOI: 10.1016/j.jelechem.2023.117299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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Felicia WXL, Rovina K, ‘Aqilah NMN, Vonnie JM, Yin KW, Huda N. Assessing Meat Freshness via Nanotechnology Biosensors: Is the World Prepared for Lightning-Fast Pace Methods? BIOSENSORS 2023; 13:217. [PMID: 36831985 PMCID: PMC9954215 DOI: 10.3390/bios13020217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/18/2023]
Abstract
In the rapidly evolving field of food science, nanotechnology-based biosensors are one of the most intriguing techniques for tracking meat freshness. Purine derivatives, especially hypoxanthine and xanthine, are important signs of food going bad, especially in meat and meat products. This article compares the analytical performance parameters of traditional biosensor techniques and nanotechnology-based biosensor techniques that can be used to find purine derivatives in meat samples. In the introduction, we discussed the significance of purine metabolisms as analytes in the field of food science. Traditional methods of analysis and biosensors based on nanotechnology were also briefly explained. A comprehensive section of conventional and nanotechnology-based biosensing techniques is covered in detail, along with their analytical performance parameters (selectivity, sensitivity, linearity, and detection limit) in meat samples. Furthermore, the comparison of the methods above was thoroughly explained. In the last part, the pros and cons of the methods and the future of the nanotechnology-based biosensors that have been created are discussed.
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Affiliation(s)
- Wen Xia Ling Felicia
- Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
| | - Kobun Rovina
- Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
| | - Nasir Md Nur ‘Aqilah
- Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
| | - Joseph Merillyn Vonnie
- Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
| | - Koh Wee Yin
- Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
| | - Nurul Huda
- Faculty of Sustainable Agriculture, Universiti Malaysia Sabah, Locked Bag No. 3, Sandakan 90509, Sabah, Malaysia
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Sokolkov SV. Evolution of the analytical signal in electrochemistry from electrocapillary curve to a digital electrochemical pattern of a multicomponent sample. ELECTROCHEMICAL SCIENCE ADVANCES 2022. [DOI: 10.1002/elsa.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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