A nickel gallium oxide chlorophyll mimic for green methanol synthesis
Bibliographic record
Abstract
Light-driven methanol synthesis from CO2 provides a sustainable fuel source and approach to carbon neutralization. Mimicking natural photosynthesis could improve gas-solid photocatalytic efficiency, but it remains highly challenging due to the absence of well-organized mass and charge transfer networks in artificial materials. Herein, we report a chlorophyll-mimicking, nano-pigment nickel gallium oxide, which facilitates discrete light/dark reactions and proton-mediated charge transfer for efficient photocatalytic hydrogenation of CO2 to methanol. This nano-pigment features surface frustrated Lewis pairs, enabling heterolytic hydrogen splitting into H- and H+. The H- acts analogously to nicotinamide adenine dinucleotide phosphate in natural photosynthesis, with Ni(II)/Ni(III) and OH(-I) respectively serving as conduits for ion transport of H- and H+ to the Ni site, where they subsequently react with CO2, mimicking natural carbon fixation. This approach establishes a chlorophyll-mimetic structure for photocatalytic stepwise CO2 hydrogenation, achieving 3.0% quantum efficiency, 3.20 mmol·h-1·g-1 methanol activity, and 79.6% selectivity. Here the authors report NiGa2O4–x(OH)y for light-driven CO2 hydrogenation to methanol. The surface Lewis acid–base pairs and -OH groups act as conduits for H- /H+ transport to active sites, enhancing photocatalytic methanol production.
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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".