Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models
To improve the charge – discharge properties of an LiMn2O4 positive electrode active material for a lithium-ion battery, the effect of additive elements was investigated using high-throughput experiments and materials informatics techniques. First, the material libraries of LiMn1.4NixAyBzO4±δ (A, B ...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Science and Technology of Advanced Materials: Methods |
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| Online Access: | http://dx.doi.org/10.1080/27660400.2023.2260299 |
| _version_ | 1797641054806081536 |
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| author | Shin Tajima Mitsutaro Umehara Kensuke Takechi |
| author_facet | Shin Tajima Mitsutaro Umehara Kensuke Takechi |
| author_sort | Shin Tajima |
| collection | DOAJ |
| description | To improve the charge – discharge properties of an LiMn2O4 positive electrode active material for a lithium-ion battery, the effect of additive elements was investigated using high-throughput experiments and materials informatics techniques. First, the material libraries of LiMn1.4NixAyBzO4±δ (A, B = Mo, Ir, Bi, Eu, Zn, Y, Ce, and Ru, x + y + z = 0.6, x, y, z = 0, 0.2, 0.4, 0.6) were synthesized by the ink-jet technique, and the properties were estimated using X-ray diffraction and X-ray absorption near-edge structure (XANES) spectroscopy at SPring-8. Appropriate additives were searched for by machine learning models using composition-based explanatory and experimentally obtained objective variables without completing the lithium-ion battery cell. Next, LiMn2O4 specimens containing the additives were synthesized by the solid-state reaction method, and then the charge – discharge properties were verified using the sandwich-type electrochemical cell. Based on the results, LiMn1.6Ni0.2Ir0.1Mo0.1O4±δ, LiMn1.6Ni0.2Pd0.1W0.1O4±δ, LiMn1.6Ni0.2Ir0.1W0.1O4±δ, LiMn1.6Ni0.3W0.1O4±δ, and LiMn1.6Ni0.2Ru0.1W0.1O4±δ had approximately 10% larger current capacity and approximately 0.1 V higher average charge – discharge potential than LiMn2O4 without additives. The charge compensation of lithiation and delithiation could be caused by the valence change of Mn (Mn4+ ⇌ Mn3+) and Ni ions (Ni3+ ⇌ Ni2+), which was estimated by XANES spectroscopy. |
| first_indexed | 2024-03-11T13:39:01Z |
| format | Article |
| id | doaj.art-0c2ad5c4b35d40839a89c68e8b543544 |
| institution | Directory Open Access Journal |
| issn | 2766-0400 |
| language | English |
| last_indexed | 2024-03-11T13:39:01Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials: Methods |
| spelling | doaj.art-0c2ad5c4b35d40839a89c68e8b5435442023-11-02T13:48:31ZengTaylor & Francis GroupScience and Technology of Advanced Materials: Methods2766-04002023-12-013110.1080/27660400.2023.22602992260299Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning modelsShin Tajima0Mitsutaro Umehara1Kensuke TakechiToyota Central R&D Labs., IncToyota Central R&D Labs., IncTo improve the charge – discharge properties of an LiMn2O4 positive electrode active material for a lithium-ion battery, the effect of additive elements was investigated using high-throughput experiments and materials informatics techniques. First, the material libraries of LiMn1.4NixAyBzO4±δ (A, B = Mo, Ir, Bi, Eu, Zn, Y, Ce, and Ru, x + y + z = 0.6, x, y, z = 0, 0.2, 0.4, 0.6) were synthesized by the ink-jet technique, and the properties were estimated using X-ray diffraction and X-ray absorption near-edge structure (XANES) spectroscopy at SPring-8. Appropriate additives were searched for by machine learning models using composition-based explanatory and experimentally obtained objective variables without completing the lithium-ion battery cell. Next, LiMn2O4 specimens containing the additives were synthesized by the solid-state reaction method, and then the charge – discharge properties were verified using the sandwich-type electrochemical cell. Based on the results, LiMn1.6Ni0.2Ir0.1Mo0.1O4±δ, LiMn1.6Ni0.2Pd0.1W0.1O4±δ, LiMn1.6Ni0.2Ir0.1W0.1O4±δ, LiMn1.6Ni0.3W0.1O4±δ, and LiMn1.6Ni0.2Ru0.1W0.1O4±δ had approximately 10% larger current capacity and approximately 0.1 V higher average charge – discharge potential than LiMn2O4 without additives. The charge compensation of lithiation and delithiation could be caused by the valence change of Mn (Mn4+ ⇌ Mn3+) and Ni ions (Ni3+ ⇌ Ni2+), which was estimated by XANES spectroscopy.http://dx.doi.org/10.1080/27660400.2023.2260299high-throughput experimentsmaterials informaticsmachine learninglithium-ion batteryxanespositive electrode active materiallimn2o4chemical delithiationsupport vector regression |
| spellingShingle | Shin Tajima Mitsutaro Umehara Kensuke Takechi Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models Science and Technology of Advanced Materials: Methods high-throughput experiments materials informatics machine learning lithium-ion battery xanes positive electrode active material limn2o4 chemical delithiation support vector regression |
| title | Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models |
| title_full | Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models |
| title_fullStr | Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models |
| title_full_unstemmed | Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models |
| title_short | Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models |
| title_sort | charge discharge properties of limn2o4 group positive electrode active materials for lithium ion batteries using high throughput experimental screening and machine learning models |
| topic | high-throughput experiments materials informatics machine learning lithium-ion battery xanes positive electrode active material limn2o4 chemical delithiation support vector regression |
| url | http://dx.doi.org/10.1080/27660400.2023.2260299 |
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