Effect of Solution Chemistry on Solution Precursor Plasma Spray Deposition of LiFePO4
Bibliographic record
Abstract
Abstract Solution precursor plasma spray (SPPS) is a thermal spray process in which deposits are formed by injecting solutions with the appropriate chemistry directly into the plasma. The deposits consist of grains or particles as small as ~20nm, and may be very porous or nearly dense, depending on the solution and deposition parameters. Recently, the potential of SPPS to deposit fine particle, porous coatings suitable for use as electrochemical electrodes for fuel cells and gas sensors has been demonstrated. This paper describes the efforts to deposit LiFePO4 coatings which may be of interest for Li ion battery electrodes with SPPS. In this case, along with the porosity, surface area, and microstructure of the deposited coatings, crystal structure also plays an important role in determining the performance of the LiFePO4 electrodes. Solution precursors with different solution chemistries containing lithium, iron and phosphorus ions are injected into hydrocarbon plasma issuing from a DC-arc torch. The effects of solution chemistries on coating morphologies and crystal structure were investigated. The results indicate that the porosity and crystal structure of the coatings can be tailored by selecting different additives.
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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 | 0.001 |
| 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) | 0.002 | 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".