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 ZrS 2 monolayer doped with transition metals (TMs) Ni, Pd, or Pt, for three target gases CO 2 , SO 2 , and NO 2 . Our findings reveal that the pristine ZrS 2 monolayer exhibits only a weak physical adsorption, whereas TM-doped monolayers show significantly enhanced sensing capabilities. The Ni–ZrS 2 monolayer increases the charge transfer for NO 2 by a factor of 6.75, while the Pd–ZrS 2 monolayer shows an eightfold improvement for SO 2 . Notably, the adsorption of NO 2 leads to substantial modifications in the band structure of the ZrS 2 monolayer, suggesting its use as a resistive NO 2 sensor. The Pd–ZrS 2 and Pt–ZrS 2 monolayers exhibit shorter recovery times at temperatures of 348 K and 398 K after adsorbing SO 2 and NO 2 , highlighting their suitability for repeated detection. Additionally, the transport properties of TM-ZrS 2 -based devices are analyzed by NEGF. For CO 2 , SO 2 , and NO 2 , sensitivities of up to 1.53, 4.60, and 2.01 are observed for the Pd–ZrS 2 , Pt–ZrS 2 , and Ni–ZrS 2 -based devices, respectively, under specific bias. This study demonstrates that TM-ZrS 2 -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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".