Solar‐Triggered Engineered 2D‐Materials for Environmental Remediation: Status and Future Insights
Bibliographic record
Abstract
Abstract Modern‐day society requires advanced technologies based on renewable and sustainable energy resources to face the challenges regarding environmental remediation. Solar‐inspired photocatalytic applications for water purification, hydrogen and oxygen evolution, carbon dioxide reduction, nitrogen fixation, and removal of bacterial species seem to be unique solutions based on green and efficient technologies. Considering the unique electronic features and larger surface area, 2D photocatalysts have been broadly explored for the above‐mentioned applications in the past few years. However, their photocatalytic potential has not been optimized yet to the adequate level of practical and commercial applications. Among many strategies available, surface and interface engineering and the hybridization of different materials have revealed pronounced potential to boost the photocatalytic potential of 2D materials. This feature review recapitulates recent advancements in engineered materials that are 2D for various photocatalysis applications for environmental remediation. Various surface and interface engineering technologies are briefly discussed, like anion–cation vacancies, pits, distortions, associated vacancies, etc., along with rules and parameters. In addition, several hybridization approaches, like 0D/2D, 1D/2D, 2D/2D, and 3D/2D hybridization, etc., are also deeply investigated. Lastly, the application of these engineered 2D materials for various photocatalytic applications, challenges, and future perspectives is extensively explored.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".