Rational Design of a LiVPO<sub>4</sub>F@C Microsphere Cathode Enabled by a One-Step Hydrothermal Reaction for Lithium-Ion Batteries
Bibliographic record
Abstract
LiVPO 4 F is an attractive cathode material due to its high operating voltage, high energy density, and stable structure, but its application is still hindered by low electrical conductivity and complicated synthesis processes. Here, the microsphere LiVPO 4 F@C cathode material with a high discharge plateau and stable structures is synthesized by a simple hydrothermal reaction. The formation mechanisms of the microsphere LiVPO 4 F are clarified. Surfactant hexadecyl trimethylammonium bromide is self-assembled into a spherical micelle matrix during the hydrothermal reaction and formed the microsphere LiVPO 4 F@C particles through multilayer adsorption. Meanwhile, the effects of pH value and the content of LiF on the morphology, structure, and electrochemical performance of the LiVPO 4 F@C cathode material are investigated. When the value of pH is close to neutral, the fluorine can be effectively deposited to form spherical LiVPO 4 F. With the addition of LiF at a ratio of 1.4, the prepared LiVPO 4 F@C exhibits better crystallinity and uniform spherical morphology, with excellent rate capability and cycling stability and a capacity retention of 93.60% at 5C. This work provides a simple and effective preparation solution for exploring a high-performance LiVPO 4 F@C cathode material for lithium-ion batteries.
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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".