Atomic Layer Deposition of Ultrathin Al<sub>2</sub>O<sub>3</sub> Layer on Carbon Nanotube Anodes for Potassium Ion Batteries with High Initial Coulombic Efficiency and Electrochemical Performance
Bibliographic record
Abstract
Abstract Potassium ion batteries (PIBs) have emerged as one of the most attractive alternatives to lithium‐ion batteries (LIBs) to satisfy the burgeoning energy demand. The continuous growth of solid electrolyte interphase (SEI) and consumption of potassium resources cause poor initial Coulombic efficiency (ICE), unstable cycling performance, and even safety issues. Herein, the fabrication of an artificial SEI of an ultrathin Al2O3 layer by atomic layer deposition (ALD) on a multi‐walled carbon nanotubes (MWCNTs) anode for high‐performance PIBs is reported. The SEI resistance and the charge transfer resistance in the MWCNTs electrode are significantly reduced according to electrochemical impedance spectroscopy (EIS) and galvanostatic intermittent titration (GITT) measurements. The MWCNT electrode with a 2‐nm Al2O3 layer exhibits a much higher ICE of up to 85.9% than that of the uncoated one (20.8 %). It also maintains a remarkably high reversible capacity of 284 mA h g−1 at 100 mA g−1 after 100 cycles. In particular, the anode delivers superior rate capability of 558, 303, 251, 203, and 154 mAh g−1 at a current density of 50, 100, 200, 400, and 800 mA g−1, respectively. This work offers an effective approach for fabricating PIBs with high ICE and electrochemical 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) | 0.000 | 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".