Superassembled Red Phosphorus Nanorod–Reduced Graphene Oxide Microflowers as High‐Performance Lithium‐Ion Battery Anodes
Bibliographic record
Abstract
Lithium‐ion battery (LIB) anodes using red phosphorus materials are promising with the advantages of high capacity, low price, and abundant reserves. However, the huge volume expansion (≈300%) of red phosphorus during the charge and discharge process significantly limits their application. Herein, superassembled red phosphorus nanorod/reduced graphene oxide microflower (RPN/rGF) composites are reported. The RPNs can accommodate huge volume expansion, shorten lithium‐ion transmission distances, and provide more conductive contacts, and the rGF serves as an electron pathway and buffers the RPN volume expansion. Experimental and finite element simulations prove the fixation of PC bonds in the RPN/rGF composite, thereby demonstrating a high capacity (1760 mA h g−1 at 0.3 C), remarkable rate capability (1073 mA h g−1 at 3 C), and great cyclability (1380 mA h g−1 at 0.3 C over 300 cycle). This work could shed light on the future development of red phosphorus composite materials for commercially viable lithium‐ion batteries.
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 | 0.000 |
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".