Transition metal-doped ZrS<sub>2</sub> monolayer as potential gas sensor for CO<sub>2</sub>, SO<sub>2</sub>, and NO<sub>2</sub>: density functional theory and non-equilibrium Green’s functions’ analysis
Bibliographic record
Abstract
Abstract Density functional theory combined with the non-equilibrium Green’s function (NEGF) is used to systematically analyze the adsorption and sensing properties of a pristine ZrS2 monolayer doped with transition metals (TMs) Ni, Pd, or Pt, for three target gases CO2, SO2, and NO2. Our findings reveal that the pristine ZrS2 monolayer exhibits only a weak physical adsorption, whereas TM-doped monolayers show significantly enhanced sensing capabilities. The Ni–ZrS2 monolayer increases the charge transfer for NO2 by a factor of 6.75, while the Pd–ZrS2 monolayer shows an eightfold improvement for SO2. Notably, the adsorption of NO2 leads to substantial modifications in the band structure of the ZrS2 monolayer, suggesting its use as a resistive NO2 sensor. The Pd–ZrS2 and Pt–ZrS2 monolayers exhibit shorter recovery times at temperatures of 348 K and 398 K after adsorbing SO2 and NO2, highlighting their suitability for repeated detection. Additionally, the transport properties of TM-ZrS2-based devices are analyzed by NEGF. For CO2, SO2, and NO2, sensitivities of up to 1.53, 4.60, and 2.01 are observed for the Pd–ZrS2, Pt–ZrS2, and Ni–ZrS2-based devices, respectively, under specific bias. This study demonstrates that TM-ZrS2-based devices can realize repetitive and sensitive detection of specific target gases at different temperatures and biases, revealing their potential 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".