Surface-enhanced Raman scattering based on noble metal nanoassemblies for detecting harmful substances in food
Bibliographic record
Abstract
Residues of harmful substances in food can severely damage human health. The content of these substances in food is generally low, making detection difficult. Surface-enhanced Raman scattering (SERS), based on noble metal nanomaterials, mainly gold (Au) and silver (Ag), has exhibited excellent capabilities for trace detection of various substances. Noble metal nanoassemblies, in particular, have extraordinary flexibility and tunable optical properties, which cannot be offered by single nanoparticles (NPs). These nanoassemblies, with their various morphologies synthesized using NPs through artificially induced self-assembly or template-driven preparation, can significantly enhance the local electric field and create "hot spots" due to the gaps between adjacent NPs. Consequently, the SERS properties of NPs become more prominent, leading to improved performance in the trace detection of various substances and detection limits that are considerably lower than the current relevant standards. Noble metal nanoassemblies show promising potential in ensuring food safety. This review discusses the synthesis methods and SERS properties of noble metal nanoassemblies and then concentrates on their application in detecting biotoxins, drug residues, illegal additives, and heavy metals. The study provides valuable references for further research into the application of nanoassemblies in food safety detection.
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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 | 0.001 |
| 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 | 0.002 |
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".