MoS <sub>2</sub> –PdS <sub>2</sub> Heterostructure for NO <sub>2</sub> Sensing under Ambient Conditions: An Experimental and Computational Study
Bibliographic record
Abstract
To protect the human environment, it is crucial to develop gas sensors that can effectively detect harmful gases at room temperature (RT). Even small traces of harmful gases like nitrogen dioxide (NO 2 ) are challenging to detect at RT. To resolve this issue, the van der Waals (vdWs) junction of 2D transition metal dichalcogenides (TMDCs) and novel metal dichalcogenides (NMDCs) holds significant potential for sensing devices because of intriguing properties at the junction. This study presents an efficient NO 2 gas sensor based on the vdW junction of PdS 2 and MoS 2 material working at RT (30 °C). Compared with pristine PdS 2, the conductivity of the vdW junction improved significantly. The MoS 2 /PdS 2 heterojunction sensor demonstrates remarkable response and selectivity toward NO 2 at RT, which are inaccessible in PdS 2 and MoS 2 as individual sensors. The heterojunction sensor exhibits a relative response of ∼25% to 20 ppm of NO 2 as compared to pristine PdS 2 (∼7%) and pristine MoS 2 (∼12%) sensors and has a significantly lower limit of detection (LOD) of 1.4 ppb. The sensor demonstrates reasonably good response and recovery time, excellent stability, and long-term durability. Also, density functional theory (DFT) calculations indicate that the p–p junction of MoS 2 /PdS 2 provides more favorable sites for NO 2 adsorption. This is due to the more negative adsorption energy, which improves charge transfer during the adsorption of NO 2 and boosts the electrical response of the gas sensors. This study offers a prospective framework for the development of gas sensors based on 2D vdW heterojunctions, which demonstrate enhanced sensing performance at RT conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".