Aptamer-Based Biosensors for Environmental Monitoring
Bibliographic record
Abstract
Due to their relative synthetic and chemical simplicity compared to antibodies, aptamers afford enhanced stability and functionality for the detection of environmental contaminants and for use in environmental monitoring. Furthermore, nucleic acid aptamers can be selected for toxic targets which may prove difficult for antibody development. Of particular relevance, aptamers have been selected and used to develop biosensors for environmental contaminants such as heavy metals, small-molecule agricultural toxins, and water-borne bacterial pathogens. This review will focus on recent aptamer-based developments for the detection of diverse environmental contaminants. Within this domain, aptamers have been combined with other technologies to develop biosensors with various signal outputs. The goal of much of this work is to develop cost-effective, user-friendly detection methods that can complement or replace traditional environmental monitoring strategies. This review will highlight recent examples in this area. Additionally, with innovative developments such as wearable devices, sentinel materials, and lab-on-a-chip designs, there exists significant potential for the development of multifunctional aptamer-based biosensors for environmental monitoring. Examples of these technologies will also be highlighted. Finally, a critical perspective on the field, and thoughts on future research directions will be offered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".