Sensitive Detection of Specific Volatile Organic Compounds by Functionalized Transition Metal Dichalcogenide Monolayers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Timely detection of liver cirrhosis (LC) is critical for effective clinical management and improved patient outcomes. Among emerging diagnostic approaches, detection of volatile organic compounds (VOCs), related to LC, offers a noninvasive, rapid, and cost-effective alternative to conventional methods. In this work, we employed spin-polarized density functional theory (DFT) to systematically investigate the interaction of LC-related VOCs using transition-metal dichalcogenides (TMDs), specifically WX 2 monolayers (X = S, Se 2 ). Five VOCs, namely, 2-pentanone, dimethyl sulfide (DMS), isoprene, limonene, and methanol, were selected based on their experimental association with LC. To enhance the sensitivity and selectivity of TMDs, Mn and Fe atoms were used to dope the chalcogen sites of WX 2, inducing strong dipole moments and improved van der Waals (vdW) interactions. The doped systems demonstrated significantly higher adsorption energies ( E ads, 1.5–2.1 eV), charge transfer (Δ q = 0.4–0.8 e), and magnetization changes (Δ M ≠ 0) for VOCs compared to air molecules ( E ads < 0.5 eV, Δ q < 0.1 e, Δ M = 0), confirming strong selectivity. Work function shifts Δϕ > 0.4 eV (for VOCs) and changes in the density of states near the Fermi level further support enhanced electronic response upon VOC adsorption. Our study offers atomic-scale insights into adsorption energetics, charge transfer, and electronic structure modulation that can guide future experimental efforts in nanobiosensor development. We also critically examine the scope and limitations of our theoretical framework, emphasizing the need for experimental validation to translate these findings into practical diagnostic technologies.
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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 | 0.000 | 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) | 0.000 | 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".