Chiral MoS<sub>2</sub> Quantum Dots: Dual‐Mode Detection Approaches for Avian Influenza Viruses
Bibliographic record
Abstract
Abstract Molybdenum disulfide (MoS2), a type of transition metal dichalcogenide material, has emerged as an important class among 2D systems. When 2D MoS2 materials are reduced to 0D quantum dots (QDs), they introduce new optical properties that point to several potential technological advantages in electronic, magnetic, optical, and catalytic properties. In this study, a simple way to produce chiral MoS2 QDs from MoS2 nanopowder is presented using l(+)‐ascorbic acid as a reducing agent. The calculated quantum yield of QDs is 11.06%. Experimental results reveal that the size of QDs is uniformly monodispersed (2–3 nm) and have a blue emissive fluorescence peak and circular dichroism (CD) peak located at 420 and 330 nm, respectively. Furthermore, a dual‐mode detection system based on fluorescence and chirality is performed using as‐synthesized MoS2 QDs, where QDs are conjugated with anti‐hemagglutinin antibodies of avian influenza virus and made into an immunobridge in the presence of target virus and anti‐neuraminidase antibodies conjugated magnetic nanoparticles (MNPs). The photoluminescence and CD spectra of unconjugated QDs after separated magnetochirofluorescent (MNPs‐QDs) nanohybrids by external magnets enables influenza virus A (H5N1) detection with the limit of detection value of 7.35 and 80.92 pg mL−1, respectively.
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) | 0.000 | 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 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".