Enhanced NH3 Sensing Performance of Mo Cluster-MoS2 Nanocomposite Thin Films via the Sulfurization of Mo6 Cluster Iodides Precursor
Bibliographic record
Abstract
The high-performance defect-rich MoS2 dominated by sulfur vacancies as well as Mo-rich environments have been extensively studied in many fields, such as nitrogen reduction reactions, hydrogen evolution reactions, as well as sensing devices for NH3, which are attributed to the under-coordinated Mo atoms playing a significant role as catalytic sites in the defect area. In this study, the Mo cluster-MoS2 composite was creatively synthesized through a one-step sulfurization process via H2/H2S gas flow. The Mo6 cluster iodides (MIs) coated on the fluorine-doped tin oxide (FTO) glass substrate via the electrophoretic deposition method (i.e., MI@FTO) were used as a precursor to form a thin-film nanocomposite. Investigations into the structure, reaction mechanism, and NH3 gas sensing performance were carried out in detail. The results indicated that during the gas flowing, the decomposed Mo6 cluster iodides played the role of template and precursor, forming complicated Mo cluster compounds and eventually producing MoS2. These Mo cluster-MoS2 thin-film nanocomposites were fabricated and applied as gas sensors for the first time. It turns out that after the sulfurization process, the response of MI@FTO for NH3 gas increased three times while showing conversion from p-type to n-type semiconductor, which enhances their possibilities for future device applications.
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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".