Improvement of electrochemical and electrical properties of LiFePO <sub>4</sub> coated with citric acid
Bibliographic record
Abstract
Abstract LiFePO 4 was synthesized using hydrothermal method and coated with different amounts of citric acid as carbon source. The samples were characterized by X‐ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM), surface area measurement—Brunauer–Emmett–Teller (BET), discharge capability, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The results show that the quality and thickness of the carbon coating on the surface of LiFePO 4 particles are very important. The optimum carbon content (about 30 wt%) can lead to a more uniform carbon distribution. Electrochemical results show that the samples containing 20 wt%, 30 wt%, 40 wt%, and 50 wt% carbon deliver a discharge capacity of 105, 167, 151, and 112 mAh·g −1 , respectively, at the rate of 0.1C. The increase of carbon content leads to the decrease of discharge capacity of LiFePO 4 /C, owing to the fact that excess carbon delays the diffusion of Li + through the carbon layers during charge/discharge procedure. The LiFePO 4 /C with low carbon content exhibits poor electrochemical performance because of its low electrical conductivity. Therefore, the amount of carbon must be optimized in order to achieve excellent electrochemical performance of LiFePO 4 /C for its application in a lithium ion battery.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".