Molecular Imprinting Polymer-Based Sensing of Neonicotinoids
Bibliographic record
Abstract
Neonicotinoids are a novel class of insecticides that exhibit environmental persistence and off-target effects on both humans and ecosystems. Therefore, there is a need for sensitive and selective sensors to monitor concentrations of neonicotinoids in environmental water and soil systems. Molecularly imprinted polymer (MIP)-based sensors are an emerging technology with strong potential for reliable, sensitive, and selective detection of neonicotinoids. Moreover, MIPs are versatile and compatible with a wide range of analytical techniques, which can further enhance measurement capabilities in the development of practical and robust sensors. Despite this promise, many routes remain underexplored for neonicotinoid detection. This review reports on the current state of neonicotinoid chemical sensors and detection methods using MIPs and highlights potential applications of MIP-based approaches as cost-effective and easy-to-operate solutions for monitoring neonicotinoids. Recent sensors incorporating MIPs and electrochemical or optical techniques for neonicotinoid detection are described and compared. Approaches employing magnetic solid-phase extraction and quartz crystal microbalance are also discussed. Additionally, the influence of monomer choice for MIP synthesis, as well as the use of additives and nanomaterials for sensor construction and analyte detection, is reviewed. These methods may promote sustainability, reusability, ratiometric or simultaneous detection of neonicotinoids, and sensor portability for on-site monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".