Advanced Phosphorus‐Based Materials for Lithium/Sodium‐Ion Batteries: Recent Developments and Future Perspectives
Bibliographic record
Abstract
Abstract High‐performance and lost‐cost lithium‐ion and sodium‐ion batteries are highly desirable for a wide range of applications including portable electronic devices, transportation (e.g., electric vehicles, hybrid vehicles, etc.), and renewable energy storage systems. Great research efforts have been devoted to developing alternative anode materials with superior electrochemical properties since the anode materials used are closely related to the capacity and safety characteristics of the batteries. With the theoretical capacity of 2596 mA h g −1 , phosphorus is considered to be the highest capacity anode material for sodium‐ion batteries and one of the most attractive anode materials for lithium‐ion batteries. This work provides a comprehensive study on the most recent advancements in the rational design of phosphorus‐based anode materials for both lithium‐ion and sodium‐ion batteries. The currently available approaches to phosphorus‐based composites along with their merits and challenges are summarized and discussed. Furthermore, some present underpinning issues and future prospects for the further development of advanced phosphorus‐based materials for energy storage/conversion systems are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".