Radial Nano-Heterojunctions Consisting of CdS Nanorods Wrapped by 2D CN:PDI Polymer with Deep HOMO for Photo-Oxidative Water Splitting, Dye Degradation and Alcohol Oxidation
Bibliographic record
Abstract
Solar energy harvesting using semiconductor photocatalysis offers an enticing solution to two of the biggest societal challenges, energy scarcity and environmental pollution. After decades of effort, no photocatalyst exists which can simultaneously meet the demand for excellent absorption, high quantum efficiency and photochemical resilience/durability. While CdS is an excellent photocatalyst for hydrogen evolution, pollutant degradation and organic synthesis, photocorrosion of CdS leads to the deactivation of the catalyst. Surface passivation of CdS with 2D graphitic carbon nitrides (CN) such as g-C3N4 and C3N5 has been shown to mitigate the photocorrosion problem but the poor oxidizing power of photogenerated holes in CN limits the utility of this approach for photooxidation reactions. We report the synthesis of exfoliated 2D nanosheets of a modified carbon nitride constituted of tris-s-triazine (C6N7) linked pyromellitic dianhydride polydiimide (CN:PDI) with a deep oxidative highest occupied molecular orbital (HOMO) position, which ensures sufficient oxidizing power for photogenerated holes in CN. The heterojunction formed by the wrapping of mono-/few layered CN:PDI on CdS nanorods (CdS/CN:PDI) was determined to be an excellent photocatalyst for oxidation reactions including photoelectrochemical water splitting, dye decolorization and the photocatalytic conversion of benzyl alcohol to benzaldehyde. Extensive structural characterization using HR-TEM, Raman, XPS, etc., confirmed wrapping of few-layered CN:PDI on CdS nanorods. The increased photoactivity in CdS/CN:PDI catalyst was ascribed to facile electron transfer from CdS to CN:PDI in comparison to CdS/g-C3N4, leading to an increased electron density on the surface of the photocatalyst to drive chemical reactions.
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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) | 0.000 | 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".