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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".