In-situ restructuring of Ni-based metal organic frameworks for photocatalytic CO2 hydrogenation
Bibliographic record
Abstract
As the global quest for sustainable energy keeps rising, exploring novel efficient and practical photocatalysts remains a research and industrial urge. Particularly, metal organic frameworks were proven to contribute to various stages of the carbon cycle, from CO2 capture to its conversion. Herein, we report the photo-methanation activity of three isostructural, nickel-based metal organic frameworks incorporating additional niobium, iron, and aluminum sites, having demonstrated exceptional CO2 capture abilities from thin air in previous reports. The niobium version exhibits the highest performance, with a CO2 to CH4 conversion rate in the order of 750–7500 µmol*gcatalyst−1*h−1 between 180 °C and 240 °C, achieving 97% selectivity under light irradiation and atmospheric pressure. The in-depth characterization of this framework before and after catalysis reveals the occurrence of an in-situ restructuring process, whereas active surface species are formed under photocatalytic conditions, thus providing comprehensive structure-performance correlations for the development of efficient CO2 conversion photocatalysts. This study correlates the structure and the CO2 photohydrogenation performance of a series of isostructural Ni-based MOFs. Comparative characterization reveals the occurrence of an in situ restructuring, yielding different surface active sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".