Critical Aspects and Recent Advances in Structural Engineering of Photocatalysts for Sunlight‐Driven Photocatalytic Reduction of CO<sub>2</sub> into Fuels
Bibliographic record
Abstract
Abstract The catalytic conversion of CO 2 into valuable fuels is a compelling solution for tackling the global warming and fuel crisis. Light absorption and charge separation, as well as adsorption/activation of CO 2 on the photocatalyst surface, are essential steps for this process. This article reviews the CO 2 photoreduction mechanisms and critical aspects that greatly affect the photoreduction efficiency. Additionally, different materials for CO 2 photoreduction are provided, including d 0 and d 10 metal oxides/mixed oxides, sulfides, polymeric materials, and metal phosphides with visible response, metal‐organic frameworks, and layer double hydroxides. Furthermore, various structural engineering strategies and corresponding state‐of‐the‐art photocatalytic systems are reviewed and discussed, such as bandgap engineering, geometrical nanostructure engineering, and heterostructure engineering. Each strategy has advantages and disadvantages, requiring further adjustment to further improve the photocatalytic performance of the photocatalyst. Based on this review, it is greatly expected that efficiently artificial systems and the breakthrough technologies for CO 2 reduction will be successfully developed in the future to solve the energy shortage as well as the environmental problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".