A Versatile Strategy for Shish-Kebab-like Multi-heterostructured Chalcogenides and Enhanced Photocatalytic Hydrogen Evolution
Bibliographic record
Abstract
A series of multi-heterostructured metal chalcogenides (CdS-Te, NiS/CdS-Te, and MoS2/CdS-Te) with a surprising shish-kebab-like structure have been synthesized via a one-step microwave-assisted pyrolysis of dithiocarbamate precursors in ethylene glycol. Subsequently, CdS-Te composites were exploited as a self-sacrificial template to craft various CdS-Te@(Pt, Pd) multi-heterostructures. Highly uniform dispersion and intimate interactions between CdS and multicomponent cocatalysts, together with improved separation of photogenerated carriers due to the presence of Te nanotubes (NTs) and trace CdTe, enable CdS-based heterostructured photocatalysts to exhibit greatly enhanced efficiency and stability in the photocatalytic production of H2. Thorough morphological characterizations revealed that the growth of metal sulfide/Te heterostructures originates from the growth of Te tubes, which is likely governed by diffusion-limited depletion of the Te precursor and the dissolution-crystallization process of Te seeds followed by the formation of metal sulfide kebabs.
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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".