Highly Durable Nanoporous Cu<sub>2–<i>x</i></sub>S Films for Efficient Hydrogen Evolution Electrocatalysis under Mild pH Conditions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Copper-based hydrogen evolution electrocatalysts are promising materials to scale-up hydrogen production due to their reported high current densities; however, electrode durability remains a challenge. Here, we report a facile, cost-effective, and scalable synthetic route to produce Cu 2– x S electrocatalysts, exhibiting hydrogen evolution rates that increase for ∼1 month of operation. Our Cu 2– x S electrodes reach a state-of-the-art performance of ∼400 mA cm –2 at −1 V vs RHE under mild conditions (pH 8.6), with almost 100% Faradaic efficiency for hydrogen evolution. The rise in current density was found to scale with the electrode electrochemically active surface area. The increased performance of our Cu 2– x S electrodes correlates with a decrease in the Tafel slope, while analyses by X-ray photoemission spectroscopy, operando X-ray diffraction, and in situ spectroelectrochemistry cooperatively revealed the Cu-centered nature of the catalytically active species. These results allowed us to increase fundamental understanding of heterogeneous electrocatalyst transformation and consequent structure–activity relationship. This facile synthesis of highly durable and efficient Cu 2– x S electrocatalysts enables the development of competitive electrodes for hydrogen evolution under mild pH conditions.
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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 | 0.000 |
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".