Bibliographic record
Abstract
Rechargeable lithium-ion batteries (LIBs), commercially pioneered by SONY 33 years ago, have emerged as the preferred power source for portable electric devices, electric vehicles (EVs), and LIBs-based grid storage systems.This preference is attributed to their exceptional characteristics, including high electromotive force, lightweight design, and impressive energy density.LIBs are now even being explored for potential use in electric flight applications.Over several decades, significant progress has been made in developing mature electrode materials and cell architectures, such as olivine LiFePO 4 , layered oxides, Li-rich Mnbased materials, and graphite anodes.Generally, there remains an urgent need to continuously reduce costs, enhance safety measures, and increase energy density associated with LIBs.This demand is particularly crucial in the EVs market, where lower costs and greater energy density are required to extend the travel distance.Thus far, Li(Ni,Mn,Co)O 2 (NMC) and Li(Ni,Mn,Co)O 2 (NCA) compounds have been extensively studied and identified as promising cathode materials.However, the major challenges for large-scale applications are safety concerns arising from structural and thermal instability at high states-of-charge and the availability of metal resources.Another potential high-energy cathode, the Li-rich Mn-based cath-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".