Biomass-derived nanostructured carbons and their composites as anode materials for lithium ion batteries
Bibliographic record
Abstract
Since ever-increasing energy demands stimulated intensive research activities on lithium-ion batteries (LIBs), biomass as an earth-abundant renewable energy source has played an intriguing and promising role in developing sustainable biomass-derived carbons and their composite materials for high-performance LIB anodes. Different from other materials (e.g., silicon, tin, metal oxides, etc.), biomass-derived carbons and their composite materials have been applied more and more to LIBs due to their advantages such as low cost, green and eco-friendly synthesis, easy accessibility, sustainable strategy, and improved battery performance, including capacity, cycling property, and stability/durability. This tutorial review focusing on biomass-derived carbons and their composites in the application of LIB anodes will act as a strategic guide to build a close connection between renewable materials and electrochemical energy storage devices. Also, this review provides a critical analysis and comparison of biomass-derived carbons and their composites for LIB anodes, coupled with an important insight into the remaining challenges and future directions in the field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".