Progress of optical biosensors for analyzing pathogens and organic pollutants in water since 2015
Bibliographic record
Abstract
Contamination of water with pathogens and organic pollutants is one of the major environmental problems posing a risk to human health. Climate change with extreme weather events exacerbates this problem. The ability to monitor pollutants in a fast, continuous, and accurate manner is in increasing demand, especially under the climate change context, but is challenged by their ubiquity and trace concentrations. Optical biosensing is an attractive solution, owing to its rapid and accurate detection with high sensitivity. Principally, an optical biosensor recognizes bioactive toxins and contaminants via tailored bioreceptors (e.g., aptamers, enzymes, and cells) and transduces the biological response to optical signals. Research efforts have focused on tailoring bioreceptors and enhancing signal transduction by nanoparticles. This study comprehensively reviewed the mechanisms for optical biosensing and the recent development of bioreceptors and nanomaterials that enhance the rapid, easy, and accurate analysis of emerging contaminants in water. The advantages and challenges for the sensitivity, selectivity, and durability of biosensors are discussed, together with the opportunities and development strategies.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".