Development of high performing UiO-67/BiFeO3 photocatalyst for selective CO2 conversion to methanol: Process optimization by RSM
Bibliographic record
Abstract
The continuous rise in atmospheric CO 2 levels due to industrialization has raised pressing environmental concerns, necessitating efficient carbon capture and utilization strategies. Among them, the photocatalytic reduction of CO 2 to methanol offers a sustainable approach that mitigates emissions while producing a valuable solar fuel. Metal-organic frameworks (MOFs) have emerged as promising photocatalysts due to their high surface area, tunable porosity, and dense active sites, though their wide bandgaps limit visible-light activity. In this study, a UiO-67/BiFeO 3 composite photocatalyst was synthesized via a wet chemical method, combining BiFeO 3 's narrow bandgap with the structural advantages of UiO-67. Photocatalytic tests under visible light showed a significant improvement in methanol production, with the composite achieving 93.99 μmol/g·h after 4 h outperforming pure UiO-67 (31.29 μmol/g·h) and BiFeO 3 (8.01 μmol/g·h). To optimize reaction conditions, Response Surface Methodology (RSM) and Box–Behnken design (BBD) were employed. The resulting model (R 2 = 0.9966) identified an optimal methanol production rate of 95.06 μmol/g·h with a desirability value of 1.000. These findings highlight the synergistic role of BiFeO 3 's light absorption and UiO-67's CO 2 adsorption, showcasing the potential of RSM in optimizing MOF-based photocatalytic systems for sustainable CO 2 conversion.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".