Tin Oxide with Controlled Morphology and Crystallinity by Atomic Layer Deposition onto Graphene Nanosheets for Enhanced Lithium Storage
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it