Gold Nanoparticles-Based Colorimetric Assays for Environmental Monitoring and Food Safety Evaluation
Bibliographic record
Abstract
Recent years have witnessed an exponential increase in the research on gold nanoparticles (AuNPs)-based colorimetric sensors to revolutionize point-of-use sensing devices. Hence, this review is compiled focused on current progress in the design and performance parameters of AuNPs-based sensors. The review begins with the characteristics of AuNPs, followed by a brief explanation of synthesis and functionalization methods. Then, the mechanisms of AuNPs-based sensors are comprehensively explained in two broad categories based on the surface plasmon resonance (SPR) characteristics of AuNPs and their peroxidase-like catalytic properties (nanozyme). SPR-based colorimetric sensors further categorize into aggregation, anti-aggregation, etching, growth-mediated, and accumulation-based methods depending on their sensing mechanisms. On the other hand, peroxidase activity-based colorimetric sensors are divided into two methods based on the expression or inhibition of peroxidase-like activity. Next, the analytes in environmental and food samples are classified as inorganic, organic, and biological pollutants, and recent progress in detection of these analytes are reviewed in detail. Finally, conclusions are provided, and future directions are highlighted. Improving the sensitivity, reproducibility, multiplexing capabilities, and cost-effectiveness for colorimetric detection of various analytes in environment and food matrices will have significant impact on fast testing of hazardous substances, hence reducing the pollution load in environment as well as rendering food contamination to ensure food safety.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".