High‐performance photocatalysts for the selective oxidation of alcohols to carbonyl compounds
Bibliographic record
Abstract
Abstract With an increase in awareness about the need for green chemistry, there is a shift in focus towards identifying eco‐compatible technologies that can improve product yield and eliminate the use or generation of hazardous compounds. An immediate practical example of such an approach is the development of sustainable methods for alcohol oxidation as alternatives to the current processes that are energy intensive and rely on ecotoxic chemicals. In this regard, heterogeneous photocatalysis has been identified as a robust technique to catalyze reactions under benign conditions, which would otherwise require harsh synthesis routes. With the advent of materials sciences and nanotechnology, there has been a tremendous increase in the scope of applicability of photocatalysis in fine chemicals synthesis. Though an attractive choice, much of the fundamental information pertaining to catalyst activity, selectivity and reaction conditions for optimum conversion are still to be investigated for most of these systems. To this end, this review will encompass recent achievements in the selective photocatalytic oxidation of alcohols by harnessing solar radiation as a viable source of energy. The discussion will be arranged based on common types of photocatalysts reported in literature, namely metal oxides (eg, TiO2 and ZnO, Nb2O5), sulphides (eg, CdS, CuS, and Bi2S3), and carbonaceous photocatalysts (eg, g‐C3N4). Several such candidates for photocatalysts will be discussed critically with the aim of providing useful insight into developing selective photocatalysts that can oxidize alcohols via eco‐friendly pathways along with high yields.
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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".