Vertically Oriented Arrays of ReS<sub>2</sub> Nanosheets for Electrochemical Energy Storage and Electrocatalysis
Bibliographic record
Abstract
Transition-metal dichalcogenide (TMD) nanolayers show potential as high-performance catalysts in energy conversion and storage devices. Synthetic TMDs produced by chemical-vapor deposition (CVD) methods tend to grow parallel to the growth substrate. Here, we show that with the right precursors and appropriate tuning of the CVD growth conditions, ReS2 nanosheets can be made to orient perpendicular to the growth substrate. This accomplishes two important objectives; first, it drastically increases the wetted or exposed surface area of the ReS2 sheets, and second, it exposes the sharp edges and corners of the ReS2 sheets. We show that these structural features of the vertically grown ReS2 sheets can be exploited to significantly improve their performance as polysulfide immobilizers and electrochemical catalysts in lithium-sulfur (Li-S) batteries and in hydrogen evolution reactions (HER). After 300 cycles, the specific capacity of the Li-S battery with vertical ReS2 catalyst is retained above 750 mA h g(-1), with only ∼0.063% capacity decay per cycle, much better than the baseline battery (without ReS2), which shows ∼0.184% capacity decay per cycle under the same test conditions. As a HER catalyst, the vertical ReS2 provides very small onset overpotential (<100 mV) and an exceptional exchange-current density (∼67.6 μA/cm(2)), which is vastly superior to the baseline electrode without ReS2.
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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) | 0.002 | 0.001 |
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".