Carbon in lithium-ion battery technology and beyond; Tribute to Kim Kinoshita
Why this work is in the frame
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Bibliographic record
Abstract
Carbon is essential for advancing battery materials in energy storage research. Its superior conductivity, chemical stability, and adaptability significantly enhance the performance of devices like lithium-ion batteries (LIBs). The rising need for sustainable energy solutions has heightened interest in Carbon's potential for electrochemical applications. Kim Kinoshita is a prominent scientist whose innovative research on carbon materials has substantially progressed lithium-ion battery technology, among other domains. Over many decades, his study has significantly influenced our comprehension of carbon electrode behavior in energy storage technologies. In the early 1980s, Kinoshita made foundational contributions to understanding carbon's function in electrochemical systems, establishing the basis for its extensive use in LIBs. His book Carbon: Electrochemical and Physicochemical Properties is a key reference in the field. Kinoshita's work on characterizing carbon materials for LIBs was crucial for improving anode performance and significantly advancing the understanding of lithium-ion intercalation in various carbon structures . His work on forming the solid electrolyte interphase on carbon electrodes provided great insight into battery life and safety. Beyond LIBs, Kinoshita explored using carbon material in supercapacitors , fuel cells, and metal-air batteries. His works on nanostructured carbons, including carbon nanotubes and graphene, developed novel paths for next-generation energy storage technology. Published over 200 peer-reviewed publications, the research work of Kinoshita bridges the gap between fundamental science and practical applications. This work highlights his contributions to electrochemical energy storage, particularly his research on carbon materials in LIBs. We also explore potential pathways for advancing rechargeable battery technology inspired by his innovative vision.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it