In situ probes into the structural changes and active state evolution of a highly selective iron-based CO2 reduction photocatalyst
Bibliographic record
Abstract
Harnessing solar energy for CO 2 conversion to fuels presents a sustainable alternative to fossil fuels. However, finding an economical, stable, non-toxic nanomaterial catalyst poses a significant challenge. Understanding the catalyst's active state is vital for optimal performance due to potential structural changes during reactions. Herein, we employ various in situ characterizations to detail δ-FeOOH's structural evolution during hydrogen activation, identifying its active phase while catalyzing the heterogeneous reduction of CO 2 by H 2 . Using in situ environmental transmission electron microscopy, δ-FeOOH is first dehydrated to α-Fe 2 O 3 , then reduced to Fe 3 O 4 , and finally to α-Fe. Other in situ characterizations revealed that the active state of the catalyst (Fe-350-H 2 ) is a mixture of Fe 3 O 4 and α-Fe. A detailed investigation into the photocatalytic CO 2 reduction using batch, flow, and LED reactors unveiled that the Fe-350-H 2 catalyst exhibits superior activity and selectivity in activating the reverse water gas shift reaction compared with similar iron-based catalysts.
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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".