Thiolate‐Protected Single‐Atom Alloy Nanoclusters: Correlation between Electronic Properties and Catalytic Activities
Bibliographic record
Abstract
Abstract Due to their interesting chemical and optical properties, metal nanoclusters are used in various catalytic reactions and in energy conversion. By incorporating thiolate‐protecting ligands, their size and composition can be tuned. Doping these nanoclusters to form single‐atom alloy (SAA) nanoclusters is shown to further enhance these properties as a result from the synergy between the dopant and host atoms. In addition to their optical and chemical properties, SAA nanoclusters also have interesting electronic properties. However, these properties are often underdiscussed when studying SAA nanoclusters. This review provides an overview of representative studies done on the in‐depth understanding of the electronic properties and catalytic activities of Ag‐based and Au‐based thiolate‐protected SAA nanoclusters. The use of density functional theory (DFT), X‐ray absorption spectroscopy, and X‐ray photoelectron spectroscopy are employed to correlate the changes in charge states of thiolate‐protected SAA nanoclusters with their superior catalytic activity versus monometallic nanoclusters. DFT, UV–vis spectroscopy, and voltammetric methods link the changes in molecular energy levels of thiolate‐protected SAA nanoclusters to their enhanced catalytic performance over monometallic nanoclusters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".