Photoelectrochemical CO<sub>2</sub> Reduction into Syngas with the Metal/Oxide Interface
Bibliographic record
Abstract
Photoelectrochemical (PEC) reduction of CO 2 with H 2 O not only provides an opportunity for reducing net CO 2 emissions but also produces value-added chemical feedstocks and fuels. Syngas, a mixture of CO and H 2, is a key feedstock for the production of methanol and other commodity hydrocarbons in industry. However, it is challenging to achieve efficient and stable PEC CO 2 reduction into syngas with controlled composition owing to the difficulties associated with the chemical inertness of CO 2 and complex reaction network of CO 2 conversion. Herein, by employing a metal/oxide interface to spontaneously activate CO 2 molecule and stabilize the key reaction intermediates, we report a benchmarking solar-to-syngas efficiency of 0.87% and a high turnover number of 24 800, as well as a desirable high stability of 10 h. Moreover, the CO/H 2 ratios in the composition can be tuned in a wide range between 4:1 and 1:6 with a total unity Faradaic efficiency. On the basis of experimental measurements and theoretical calculations, we present that the metal/oxide interface provides multifunctional catalytic sites with complementary chemical properties for CO 2 activation and conversion, leading to a unique pathway that is inaccessible with the individual components. The present approach opens new opportunities to rationally develop high-performance PEC systems for selective CO 2 reduction into valuable carbon-based chemicals and fuels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".