Surface Treatments for Controlling Solid Electrolyte Interphase Formation on Sn/Graphene Composite Anodes for High-Performance Li-Ion Batteries
Bibliographic record
Abstract
Sn is a candidate anode material for high energy density Li-ion batteries, owing to its high specific capacity, low cost, and high electronic conductivity, but its practical applications are hindered by mechanical degradation induced by the large volume change during cycling. Graphene can be used as a buffer material for Sn volume expansion while also improving mechanical strength and electronic conductivity of the composite structure. We report here the synthesis of a composite of Sn nanoparticles and graphene through surface functionalization of graphene using diazonium grafting and subsequent Sn nanoparticle deposition. We further applied two types of surface treatments on the anode surface to improve the nucleation of the solid electrolyte interphase, which is formed due to the reduction of the electrolyte solution. These treatments include refunctionalizing the anode surface with graphene oxide sheets or sulfophenyl groups, which provide ample sites on the anode surface for the nucleation of the solid electrolyte interphase. These treatments result in the formation of a stable layer of solid electrolyte interphase, as evidenced from lower and stable charge transfer resistance at the anode interface during cycling. The anodes treated with layers of graphene oxide and sulfophenyl groups delivered reversible capacities which were 39% and 85% higher than the untreated anode. We related the enhanced electrochemical performance of the treated anodes to the formation of a stable solid electrolyte interphase layer.
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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) | 0.001 | 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".