Scalable and Contactless Optical Dye Sensors Based on Differential Reflectivity of Excitonic Peaks by MoS<sub>2</sub> Nanostructures
Bibliographic record
Abstract
Due to their excellent optoelectronic properties, two-dimensional nanomaterials are becoming key in developing various sensors for detecting harmful environmental pollutants. In this study, we present an approach for detecting methylene blue (MB) pollutants using a contactless optical sensor based on molybdenum disulfide (MoS 2 ) nanostructures. Our approach involves exploiting the interaction between the optical absorption of MB and the excitons of MoS 2, considered as markers, to monitor the presence of the MB contaminant at various concentrations. For this purpose, MoS 2 nanostructures are deposited onto a quartz substrate via chemical vapor deposition, exhibiting exceptional crystalline quality and a triangular-like morphology. We demonstrate a high-sensitivity (with a limit of detection as low as 1 ng·L –1 ) dynamic response for the MoS 2 /quartz-based device in reflectivity measurements from MoS 2 nanostructure excitons as a function of the MB concentration variation. Specifically, we show that the reflectivity intensity ratio at A and B exciton positions is directly related to the change in MB concentration in the analyte. Furthermore, the proposed sensor device features highly persistent reusability, owing to its contactless configuration with the MB pollutant, which preserves the MoS 2 sensing layer from poisoning and/or alteration. Our findings provide valuable insights for potential developments aimed at achieving highly accurate and reliable next-generation optical sensors based on two-dimensional nanomaterials.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".