Carbon in lithium-ion battery technology and beyond; Tribute to Kim Kinoshita
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".