Encapsulation of 3D plasmonic nanostructures with ultrathin hydrogel skin for rapid and direct detection of toxic small molecules in complex fluids
Bibliographic record
Abstract
Nanogap-rich 3D plasmonic nanostructures provide enhanced molecular Raman fingerprints in a nondestructive and label-free manner. However, the molecular detection of small target molecules in complex fluids is challenging due to nonspecific protein adsorption, which prevents access of the target molecules. Therefore, the molecular detection for complex mixtures usually requires a tedious and time-consuming pretreatment of samples. Herein, we report the encapsulation of 3D plasmonic nanostructures with an ultrathin hydrogel skin for the rapid and direct detection of small molecules in complex mixtures. To demonstrate the proof of concept, we directly detect pesticide dissolved in milk without pretreatment. This detection is enabled by the selective permeation of target molecules into the 3D mesh of the hydrogel skin and the adsorption onto plasmonic hotspots, accompanied by the rejection of large adhesive proteins and colloids. The high sensitivity of nanogap-rich plasmonic nanostructures in a conjunction with the molecular selection of the hydrogel skin enables the fast and reliable detection of tricyclazole in whole milk with a limit of detection as low as 10 ppb within 1 h. We believe that this plasmonic platform is highly adaptable for in situ and on-site detection of small molecules in various complex mixtures including foods, biological fluids, and environmental fluids.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".