Enhanced Stable and High Voltage of Li/SOCl <sub>2</sub> Battery Catalyzed by FePc Particulates Fixed on Activated Carbon Substrates
Bibliographic record
Abstract
Efficient catalysts for the reduction reaction of SOCl 2 are desired for lithium/thionyl chloride batteries with high and stable voltage. In this work, iron phthalocyanine particulates fixed on activated carbon substrates (FePc/AC) are synthesized by an in situ solid approach under the temperatures lower than 300 °C. U–T curves, the value distance to the average U, dU/dT via mathematical differentiation and numerical analysis were employed to investigate to the catalytic reduction of SOCl 2 systematically. The U–T curve of the battery containing the FePc/AC displays almost perfect rectangle profile. The average voltage of the battery contains the FePc/AC is 3.00 V, 0.21 V higher than that without the catalysts. After discharge, the surface of the carbon cathode containing FePc/AC displays a well-constructed loose morphology composed of LiCl nanoparticles around 100 nm in diameter. The inside of carbon cathode shows a dense morphology with a mount of nano holes around 50 nm, indicating that the formed LiCl permeates into the interior of the carbon cathode, attributing to that the FePc/AC nanocomposite catalysts facilitate the reduction of SOCl 2 , leading the formation loose LiCl film in nano scale for electrolyte SOCl 2 and the formed LiCl diffusion.
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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".