Advanced Cellulosic Materials Toward High‐Performance Metal Ion Batteries
Bibliographic record
Abstract
Abstract Nanocellulose and its derivatives represent the most abundant biopolymers on Earth, offering a wide range of advantages, including versatility in preparation, customizable functional group incorporation, and compatibility with various materials. They open up new horizons in the development of various types of metal‐ion batteries (MIBs). This work concisely categorizes nanocellulose design strategies, including rational isolation, surface chemical enhancement, and effective physical treatments. Subsequently, an overview of recent advancements in utilizing nanocellulose and its derivatives to enhance the performance of MIBs, from lithium‐ion batteries (LIBs) to post lithium‐ion batteries (e.g., Na + , K +, Zn 2+ , Mg 2+ , Ca 2+ ) are provided. The pivotal roles of nanocellulose in electrode design, interface engineering, electrolyte modification, and binder optimization are highlighted. Lastly, the challenges and prospects of utilizing nanocellulose and its derivatives in MIBs are delved into. This work aims to comprehensively cover recent developments in nanocellulose surface modification strategies and illuminate their current applications in emerging MIBs with impressive energy and power densities.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 0.001 |
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".