Turning on Selective H<sub>2</sub>S Gas Sensing Activity in Ternary Nickel Tungstate Strongly Correlated Electron System Through Sub‐Gap Band Manipulation
Bibliographic record
Abstract
Abstract Sensing the precise concentration of chemicals within a complex atmosphere stands as a critical technology with far‐reaching implications in environmental, agricultural, and medical domains. While sensitivity limits are pushed down to the ppb levels through diverse material tuning approaches, ensuring robust selectivity for targeted analyte gases remains a challenge due to the absence of effective methodologies. Here, a band structure modulation is presented in the selective detection of H2S utilizing NiWO4‐based compound, achieved through strategically manipulating sub‐gap states. The approach involves tailoring the sub‐gap within NiWO4 by employing point defect engineering mechanisms of Cu substitutional and Li interstitial doping. Unlike the featureless pristine and Cu‐doped NiWO4, Li/Cu‐co‐doped NiWO4 exhibits a sensing response to H2S gas, exhibiting an approximately six‐fold increase in sensitivity. Through density functional theory calculations and Mott–Schottky analysis, it is unveiled that this high sensitivity and selectivity toward H2S stem from the generation and positioning of Cu d‐orbital‐derived sub‐gap states, matching the reduction potential of H2S, which is triggered in the presence of substitutional Cu and interstitial Li. This result suggests a novel strategy for customizing sensing materials based on the reduction potential of analyte gases.
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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".