Synthesizing Microporous Carbon from Soybean and Use It to Develop Cathode Material for High Performance Lithium-Selenium Batteries
Bibliographic record
Abstract
Selenium is considered as a promising cathode material for lithium-ion batteries due to its high electrical conductivity (10−3 S m−1) and volumetric capacity (3253 mA h cm−3). Though, feasibility of high-performance lithium-selenium (Li-Se) batteries depends on designing a cost-effective substrate for Se with desired porous structure. In this study, porous carbon was synthesized from soybean in a two-step carbonization/activation process and used as Se host to develop cathode for lithium-selenium (Li-Se) batteries. The activated carbon/selenium (C/Se) composites were prepared using a melt diffusion process at 260 ℃ inside an argon-filled autoclave. The cathode material consisted of C/Se composite, carbon black, and sodium alginate with a mass ratio of 8:1:1. The effect of activation temperature (500, 600, and 700 ℃) on the porous structure of activated carbon was investigated. It was revealed that both specific surface area and pore volume increased with activation temperature. Moreover, the carbon activated at 500 ℃ (C500) possessed mainly mesopores while the pore structure in the other carbon samples was microporous. The quality of Se impregnation in C/Se composites and their distinct electrochemical performance were correlated to the porous structure of the activated carbon. Using the carbon obtained at 600 ℃ (C600), the Li-Se coin cell exhibited a superior discharge capacity (664 mAh g-1 at 0.1C current density), rate capability, and long cycling stability at higher current densities. It was believed that the microporous feature of C600 along with high surface area and pore volume could favor effective confinement of Se, electrolyte wetting of the cathode, lithium-ion diffusion, and charge transfer, which resulted in better electrochemical performance. This work suggests the sustainable development of microporous carbon with a unique structure suitable for cathode material in Li-Se 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".