2D SnSe Cathode Catalyst Featuring an Efficient Facet‐Dependent Selective Li<sub>2</sub>O<sub>2</sub> Growth/Decomposition for Li–Oxygen Batteries
Bibliographic record
Abstract
Abstract 2D materials are attracting much attention in the field of cathode catalysts for lithium–oxygen batteries (LOBs) due to their layered structure, unique electronic properties, and high stability. However, different stacking layer structures trigger different catalytic capabilities in LOBs. In this work, tin selenide nanosheets with a black phosphorus‐like 2D structure are synthesized and used as the cathode catalyst for LOBs. SnSe nanosheets with exposed stack (200) facets and stack edge facets exhibit superior specific capacity over 20 783 mAh g−1 and ultralong cycle stability over 380 cycles at 500 mA g−1 in LOBs. This demonstrates that the growth of discharge products is mainly concentrated on the 2D surface (200) facets, rather than the stack edge facets. Experimental and theoretical studies reveal that the confined adsorption of Li2O2 on the stack edge facets of SnSe, due to the 2D layer structure and the unique electron distribution, restricts the growth of discharge products. The 2D surface facets of SnSe benefit for the formation and stabilization of LiO2 intermediates, leading to the efficient formation/decomposition of discharge products. The findings provide in‐depth insight into the elusive electrocatalytic mechanism for 2D layer‐structures materials in LOBs.
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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".