Double sulfur vacancies by lithium tuning enhance CO2 electroreduction to n-propanol
Bibliographic record
Abstract
Abstract Electrochemical CO2 reduction can produce valuable products with high energy densities but the process is plagued by poor selectivities and low yields. Propanol represents a challenging product to obtain due to the complicated C3 forming mechanism that requires both stabilization of *C2 intermediates and subsequent C1–C2 coupling. Herein, density function theory calculations revealed that double sulfur vacancies formed on hexagonal copper sulfide can feature as efficient electrocatalytic centers for stabilizing both CO* and OCCO* dimer, and further CO–OCCO coupling to form C3 species, which cannot be realized on CuS with single or no sulfur vacancies. The double sulfur vacancies were then experimentally synthesized by an electrochemical lithium tuning strategy, during which the density of sulfur vacancies was well-tuned by the charge/discharge cycle number. The double sulfur vacancy-rich CuS catalyst exhibited a Faradaic efficiency toward n-propanol of 15.4 ± 1% at −1.05 V versus reversible hydrogen electrode in H-cells, and a high partial current density of 9.9 mA cm−2 at −0.85 V in flow-cells, comparable to the best reported electrochemical CO2 reduction toward n-propanol. Our work suggests an attractive approach to create anion vacancy pairs as catalytic centers for multi-carbon-products.
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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".