Effect of Precursor Selection on the Photocatalytic Performance of Indium Oxide Nanomaterials for Gas-Phase CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Nonstoichiometric indium oxide nanoparticles, In2O3–x(OH)y, have been shown to function as active photocatalysts for gas-phase CO2 reduction under simulated solar irradiation. Herein we demonstrate that the choice of starting material has a strong effect on the photocatalytic activity of indium oxide nanoparticles. We examine three indium oxide materials prepared via the thermal decomposition of either indium(III) hydroxide or indium(III) nitrate and correlate their stability and photocatalytic activity to the number and type of defect present in the material. Further, we use 13CO2 isotope-tracing experiments to clearly identify the origins of the observed carbon-containing products. Significantly, we find that the oxidizing nature of the precursor anion has a substantial impact on the defect formation within the sample. This study demonstrates the importance of surface defects in designing an active heterogeneous photocatalyst and provides valuable insight into key parameters for the precursor design, selection, and performance optimization of materials for gas-phase CO2 reduction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".