Ratiometric Detection of Nerve Agents by Coupling Complementary Properties of Silicon-Based Quantum Dots and Green Fluorescent Protein
Bibliographic record
Abstract
Ratiometric photoluminescent detection of the toxicologically potent organophosphate ester nerve agents paraoxon (PX) and parathion (PT) using the complementary optical and chemical properties of the long Stokes shift green fluorescent protein variant, mAmetrine1.2 (mAm), and red-emitting silicon-based quantum dots (SiQDs) is reported. PX and PT selectively quench SiQD photoluminescence (PL) through a dynamic quenching mechanism, thereby, facilitating the development of a ratiometric sensor platform that shows micromolar limits of detection for PX and PT and that is unaffected by the presence of common inorganic and organic interferents. As a part of the present study, we also demonstrate that the paper-based sensors derived from mAm and SiQDs detect PX and PT at concentrations as low as 5 μM using a readily available commercial color analysis smartphone "app". The ratiometric sensor reported herein can potentially be used for the convenient and rapid on-site detection and quantification of PX and PT in real-world samples.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".