Crystallization Engineering of CuNi <sub>2</sub> S <sub>4</sub> Ultra‐Fine Nanocrystals with Optimized Band Structures for Efficient Photocatalytic Pollutant Degradation and Hydrogen Production
Bibliographic record
Abstract
Abstract The mono‐dispersed cubic siegenite CuNi 2 S 4 ultra‐fine (≈5 nm) nanocrystals are fabricated through crystallization engineering under hot injection. The strong hydroxylation on mostly exposed CuNi 2 S 4 (220) surface leads to the formation of multi‐valence (Cu + , Cu 2+ , Ni 2+ , Ni 3+ ) species with unsaturated hybridization and coordination micro‐environments, which can induce rich redox reactions to optimize interfacial kinetics for the adsorbed reaction intermediates. The as‐synthesized CuNi 2 S 4 nanocrystals with ultra‐small particle size and the characteristics of being highly dispersed can increase specific surface area and hydroxylated active sites, which considerably contribute to the improvement of photocatalytic activities. Experimental and theoretical studies indicate that the CuNi 2 S 4 with unique surface condition can properly modulate the charge density distribution and the electronic band structure, thus achieving an optimal band gap for enhancing visible light absorption. Additionally, the strong hydroxylation on CuNi 2 S 4 (220) surface can not only make the photocatalytic process stable in alkaline environment but also bring about an impurity level between conduction and valence band, which facilitates the separation of photo‐induced charge carriers by suppressing the rapid re‐combination of exited electrons and holes. The optimization of band structure should be the intrinsic reason for the efficient photocatalytic pollutant degradation and hydrogen production under visible light illumination.
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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".