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
Why this work is in the frame
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it