Dressing Paraffin Wax/Boron Nitride Phase Change Composite with a Polyethylene “Underwear” for the Reliable Battery Safety Management
Bibliographic record
Abstract
Abstract Phase change material (PCM) can provide a battery system with a buffer platform to respond to thermal failure problems. However, current PCMs through compositing inorganics still suffer from insufficient thermal‐transport behavior and safety reliability against external force. Herein, a best‐of‐both‐worlds method is reported to allow the PCM out of this predicament. It is conducted by combining a traditional PCM (i.e., paraffin wax/boron nitride) with a spirally weaved polyethylene fiber fabric, just like the traditional PCM is wearing functional underwear. On the one hand, the spirally continuous thermal pathways of polyethylene fibers in the fabric collaborate with the boron nitride network in the PCM, enhancing the through‐plane and in‐plane thermal conductivity to 10.05 and 7.92 W m−1 K, respectively. On the other, strong polyethylene fibers allow the PCM to withstand a high puncture strength of 47.13 N and tensile strength of 18.45 MPa although above the phase transition temperature. After this typical PCM packs a triple Li‐ion battery system, the battery can be promised reliable safety management against both thermal and mechanical abuse. An obvious temperature drop of >10 °C is observed in the battery electrode during the cycling charging and discharging process.
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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".