Positive Electrode Materials for Li-Ion and Li-Batteries
Bibliographic record
Abstract
Positive electrodes for Li-ion and lithium batteries (also termed “cathodes”) have been under intense scrutiny since the advent of the Li-ion cell in 1991. This is especially true in the past decade. Early on, carbonaceous materials dominated the negative electrode and hence most of the possible improvements in the cell were anticipated at the positive terminal; on the other hand, major developments in negative electrode materials made in the last portion of the decade with the introduction of nanocomposite Sn/C/Co alloys and Si−C composites have demanded higher capacity positive electrodes to match. Much of this was driven by the consumer market for small portable electronic devices. More recently, there has been a growing interest in developing Li−sulfur and Li−air batteries that have the potential for vastly increased capacity and energy density, which is needed to power large-scale systems. These require even more complex assemblies at the positive electrode in order to achieve good properties. This review provides an overview of the major developments in the area of positive electrode materials in both Li-ion and Li batteries in the past decade, and particularly in the past few years. Highlighted are concepts in solid-state chemistry and nanostructured materials that conceptually have provided new opportunities for materials scientists for tailored design that can be extended to many different electrode materials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".