Protection of LiFePO4 against Moisture
Bibliographic record
Abstract
In this study, a carbon-coated LiFePO4 (LFP/C) powder was chemically grafted with trifluoromethylphenyl groups in order to increase its hydrophobicity and to protect it from moisture. The modification was carried out by the spontaneous reduction of in situ generated 4-trifluoromethylphenyl ions produced by the diazotization of 4-trifluoromethylaniline. X-ray photoelectron spectroscopy was used to analyze the surface organic species of the modified powder. The hydrophobic properties of the modified powder were investigated by carrying out its water contact angle measurements. The presence of the trifluoromethylphenyl groups on the carbon-coated LiFePO4 powder increased its stability in deionized water and reduced its iron dissolution in the electrolyte used for assembling the battery. The thermogravimetric and inductively coupled plasma atomic emission spectroscopy analyses revealed that 0.2–0.3 wt.% Li was deinserted during grafting and that the loading of the grafted molecules varied from 0.5 to 0.8 wt.% depending on the reaction conditions. Interestingly, the electrochemical performance of the modified LFP/C was not adversely affected by the presence of the trifluoromethylphenyl groups on the carbon surface. The chemical relithiation of the grafted samples was carried out using LiI as the reducing agent and the lithium source in order to obtain fully lithiated grafted powders.
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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) | 0.002 | 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".