Capacity retention improvement of LiCoO2 cathodes via their laser-ablation-based nanodecoration by BaTiO3 nanoparticles
Bibliographic record
Abstract
We report on the pulsed-laser-deposition (PLD) based nanodecoration of LiCoO2 (LCO) with BaTiO3 (BTO) nanoparticles (NPs) aimed at increasing the density of dielectric-active material–electrolyte triple-phase interfaces (TPIs). The BTO-NPs were deposited onto LCO at different numbers of laser pulses (NLp) and two different schemes, namely, (i) BTO-NP deposition on the surface of the precast cathode (“2D-nanodecoration”) and (ii) BTO-NP decoration of LCO powder prior to its processing to form a working cathode (“3D-nanodecoration”). While the “2D-nanodecoration” mode was found to improve significantly the discharge capacity of the LCO cathodes (by ∼30 mAh/g for NLp ≥ 200), their capacity retention (CR) was modest. In contrast, the “3D-nanodecoration” scheme enabled not only the volumic nanodecoration of the LCO powder by BTO-NPs but also their subsequent annealing to improve their crystallinity. These 3D-nanodecorated LCO cathodes were found to exhibit significantly higher CR values. In particular, for NLp = 100 k, a CR (@10 °C) as high as 78% was achieved (∼47% higher than that of their sol–gel-processed cathode counterparts). Our results point out that three key ingredients (small BTO-NP size, high DTPI, and high dispersibility of NPs on LCO) should be combined to ensure a high CR of BTO-NP-decorated LCO cathodes.
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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".