Elucidating Local Structure and Positional Effect of Dopants in Colloidal Transition Metal Dichalcogenide Nanosheets for Catalytic Hydrogenolysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Tailoring nanoscale catalysts to targeted applications is a vital component in reducing the carbon footprint of industrial processes; however, understanding and controlling the nanostructure influence on catalysts is challenging. Molybdenum disulfide (MoS 2 ), a transition metal dichalcogenide (TMD) material, is a popular example of a nonplatinum-group-metal catalyst with tunable nanoscale properties. Doping with transition metal atoms, such as cobalt, is one method of enhancing its catalytic properties. However, the location and influence of dopant atoms on catalyst behavior are poorly understood. To investigate this knowledge gap, we studied the influence of Co dopants in MoS 2 nanosheets on catalytic hydrodesulfurization (HDS) through a well-controlled, ligand-directed, tunable colloidal doping approach. X-ray absorption spectroscopy and density functional theory calculations revealed the nonmonotonous relationship between dopant concentration, location, and activity in HDS. Catalyst activity peaked at 21% Co:Mo as Co saturates the edge sites and begins basal plane doping. While Co prefers to dope the edges over basal sites, basal Co atoms are demonstrably more catalytically active than edge Co. These findings provide insight into the hydrogenolysis behavior of doped TMDs and can be extended to other TMD materials.
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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".