A Low-Cost Instrument for Dry Particle Fusion Coating of Advanced Electrode Material Particles at the Laboratory Scale
Bibliographic record
Abstract
Surface coating is an approach used to improve capacity retention of electrode materials for lithium-ion batteries. Dry particle fusion is a relatively new approach for applying coatings on particles. In this work, we introduce a low-cost dry particle fusion instrument that was constructed in house. The operation and performance of the machine is demonstrated by dry particle fusion coating of alumina on Ni(OH)2 and alumina and LiFePO4 on LiNi0.8Co0.15Al0.05O2, respectively. The hammer temperature vs time during dry particle fusion is used to monitor the process. Particle size distribution results demonstrate that the original core particles are not fractured by the coating process. SEM images show the morphology of particles before and after dry particle fusion coating and cross-sectional SEM/EDS images show the uniformity of the coating. Coin cell testing results show that dry particle fusion with suitable coating materials, at the laboratory scale using this instrument, is effective in improving capacity retention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".