Boosting Photocatalytic Activity Using Carbon Nitride Based 2D/2D van der Waals Heterojunctions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The surging demand for energy and staggering pollutants in the environment have geared the scientific community to explore sustainable pathways that are economically feasible and environmentally compelling. In this context, harnessing solar energy using semiconductor materials to generate charge pairs to drive photoredox reactions has been envisioned as a futuristic approach. Numerous inorganic crystals with promising nanoregime properties investigated in the past decade have yet to demonstrate practical application due to limited photon absorption and sluggish charge separation kinetics. Two-dimensional semiconductors with tunable optical and electronic properties and quasi-resistance-free lateral charge transfer mechanisms have shown great promise in photocatalysis. Polymeric graphitic carbon nitride (g-C3N4) is among the most promising candidates due to fine-tuned band edges and the feasibility of optimizing the optical properties via materials genomics. Constructing a two-dimensional (2D)/2D van der Waals (vdW) heterojunction by allies of 2D carbon nitride sheets and other 2D semiconductors has demonstrated enhanced charge separation with improved visible photon absorption, and the performance is not restricted by the lattice matching of constituting materials. With the advent of new 2D semiconductors over the recent past, the 2D/2D heterojunction assemblies are gaining momentum to design high performance photocatalysts for numerous applications. This review aims to highlight recent advancements and key understanding in carbon nitride based 2D/2D heterojunctions and their applications in photocatalysis, including small molecules activation, conversion, and degradations. We conclude with a forward-looking perspective discussing the key challenges and opportunity areas for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it