Evidence of covalent synergy in silicon–sulfur–graphene yielding highly efficient and long-life lithium-ion batteries
Bibliographic record
Abstract
Silicon has the potential to revolutionize the energy storage capacities of lithium-ion batteries to meet the ever increasing power demands of next generation technologies. To avoid the operational stability problems of silicon-based anodes, we propose synergistic physicochemical alteration of electrode structures during their design. This capitalizes on covalent interaction of Si nanoparticles with sulfur-doped graphene and with cyclized polyacrylonitrile to provide a robust nanoarchitecture. This hierarchical structure stabilized the solid electrolyte interphase leading to superior reversible capacity of over 1,000 mAh g(-1) for 2,275 cycles at 2 A g(-1). Furthermore, the nanoarchitectured design lowered the contact of the electrolyte to the electrode leading to not only high coulombic efficiency of 99.9% but also maintaining high stability even with high electrode loading associated with 3.4 mAh cm(-2). The excellent performance combined with the simplistic, scalable and non-hazardous approach render the process as a very promising candidate for Li-ion battery technology.
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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".