Mild‐Temperature Solution‐Assisted Encapsulation of Phosphorus into ZIF‐8 Derived Porous Carbon as Lithium‐Ion Battery Anode
Bibliographic record
Abstract
Abstract The high theoretical capacity of red phosphorus (RP) makes it a promising anode material for lithium‐ion batteries. However, the large volume change of RP during charging/discharging imposes an adverse effect on the cyclability and the rate performance suffers from its low conductivity. Herein, a facile solution‐based strategy is exploited to incorporate phosphorus into the pores of zeolitic imidazole framework (ZIF‐8) derived carbon hosts under a mild temperature. With this method, the blocky RP is etched into the form of polyphosphides anions (PP, mainly P5−) so that it can easily diffuse into the pores of porous carbon hosts. Especially, the indelible crystalline surface phosphorus can be effectively avoided, which usually generates in the conventional vapor‐condensation encapsulation method. Moreover, highly‐conductive ZIF‐8 derived carbon hosts with any pore smaller than 3 nm are efficient for loading PP and these pores can alleviate the volume change well. Finally, the composite of phosphorus encapsulated into ZIF‐8 derived porous carbon exhibits a significantly improved electrochemical performance as lithium‐ion battery anode with a high capacity of 786 mAh g−1 after 100 cycles at 0.1 A g−1, a good stability within 700 cycles at 1 A g−1, and an excellent rate performance.
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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) | 0.000 | 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".