Synergistic Effect of Hexagonal Boron Nitride-Coated Separators and Multi-Walled Carbon Nanotube Anodes for Thermally Stable Lithium-Ion Batteries
Bibliographic record
Abstract
In this work, we report the development of separators coated with hexagonal boron nitride (hBN) to improve the thermal stability of Li-ion batteries (LIBs). Aiming to achieve a synergistic effect of separators and anodes on thermal stability and electrochemical performance, multiwalled carbon nanotubes (MWCNTs) were prepared via plasma-enhanced chemical vapor deposition (PECVD) method and used as potential anode materials for LIBs. The grown MWCNTs were well characterized by using various techniques which confirmed the formation of MWCNTs. The prepared MWCNTs showed a crystalline structure and smooth surface with a diameter of ~9–12 nm and a length of ~10 μm, respectively. Raman spectra showed the characteristic peaks of MWCNTs and BN, and the sharpness of the peaks showed the highly crystalline nature of the grown MWCNTs. The electrochemical studies were performed on the fabricated coin cell with a MWCNT anode using a pristine and BN-coated separators. The results show that the cell with the BN-coated separator in a conventional organic carbonate-based electrolyte and MWCNTs as the anode resulted in a discharge capacity (at 65 °C) of ~567 mAhg−1 at a current density of 100 mAg−1 for the first cycle, and delivered a capacity of ~471 mAhg−1 for 200 cycles. The columbic efficiency was found to be higher (~84%), which showed excellent reversible charge–discharge behavior as compared with the pristine separator (69%) after 200 cycles. The improved thermal performance of the LIBs with the BN-coated separator and MWCNT anode might be due to the greater homogeneous thermal distribution resulting from the BN coating, and the additional electron pathway provided by the MWCNTs. Thus, the fabricated cell showed promising results in achieving the stable operation of the LIBs even at higher temperatures, which will open a pathway to solve the practical concerns over the use of LIBs at higher temperatures without compromising the 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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".