Tin Oxide with Controlled Morphology and Crystallinity by Atomic Layer Deposition onto Graphene Nanosheets for Enhanced Lithium Storage
Bibliographic record
Abstract
Abstract As one of the most promising negative electrode materials in lithium‐ion batteries (LIBs), SnO 2 experiences intense investigation due to its high specific capacity and energy density, relative to conventional graphite anodes. In this study, for the first time, atomic layer deposition (ALD) is used to deposit SnO 2 , containing both amorphous and crystalline phases, onto graphene nanosheets (GNS) as anodes for LIBs. The resultant SnO 2 ‐graphene nanocomposites exhibit a sandwich structure, and, when cycled against a lithium counter electrode, demonstrate a promising electrochemical performance. It is demonstrated that the introduction of GNS into the nanocomposites is beneficial for the anodes by increasing their electrical conductivity and releasing strain energy: thus, the nanocomposite electrode materials maintain a high electrical conductivity and flexibility. It is found that the amorphous SnO 2 ‐GNS is more effective than the crystalline SnO 2 ‐GNS in overcoming electrochemical and mechanical degradation; this observation is consistent with the intrinsically isotropic nature of the amorphous SnO 2 , which can mitigate the large volume changes associated with charge/discharge processes. It is observed that after 150 charge/discharge cycles, 793 mA h g −1 is achieved. Moreover, a higher coulombic efficiency is obtained for the amorphous SnO 2 ‐GNS composite anode. This study provides an approach to fabricate novel anode materials and clarifies the influence of SnO 2 phases on the electrochemical performance of LIBs.
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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".