A review on photocatalytic CO <sub>2</sub> reduction using perovskite oxide nanomaterials
Bibliographic record
Abstract
Abstract As the search for efficient catalysts for CO 2 photoreduction continues, nanostructured perovskite oxides have emerged as a class of high-performance photocatalytic materials. The perovskite oxide candidates for CO 2 photoreduction are primarily nanostructured forms of titanates, niobates, tantalates and cobaltates. These materials form the focus of this review article because they are much sought-after due to their nontoxic nature, adequate chemical stability, and tunable crystal structures, bandgaps and surface energies. As compared to conventional semiconductors and nanomaterial catalysts, nanostructured perovskite oxides also exhibit an extended optical-absorption edge, longer charge carrier lifetimes, and favorable band-alignment with respect to reduction potential of activated CO 2 and reduction products of the same. While CO 2 reduction product yields of several hundred μ mol −1 h −1 are observed with many types of perovskite oxide nanomaterials in stand-alone forms, yield of such quantities are not common with semiconductor nanomaterials of other types. In this review, we present current state-of-the-art synthesis methods to form perovskite oxide nanomaterials, and procedures to engineer their bandgaps. This review also presents a comprehensive summary and discussion on crystal structures, defect distribution, morphologies and electronic properties of the perovskite oxides, and correlation of these properties to CO 2 photoreduction performance. This review offers researchers key insights for developing advanced perovskite oxides in order to further improve the yields of CO 2 reduction products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".