Ultrasensitive Surface‐Enhanced Raman Spectroscopy Detection by Porous Silver Supraparticles from Self–Lubricating Drop Evaporation
Bibliographic record
Abstract
Abstract This work demonstrates an original and ultrasensitive approach for surface‐enhanced Raman spectroscopy (SERS) detection based on evaporation of self‐lubricating drops containing silver supraparticles. The developed method detects an extremely low concentration of analyte that is enriched and concentrated on sensitive SERS sites of the compact supraparticles formed from drop evaporation. A low limit of detection of 10 −16 m is achieved for a model hydrophobic compound rhodamine 6G (R6G). The quantitative analysis of R6G concentration is obtained from 10 −5 to 10 −11 m . In addition, for a model micro‐pollutant in water triclosan, the detection limit of 10 −6 m is achieved by using microliter sample solutions. The intensity of SERS detection in this approach is robust to the dispersity of the nanoparticles in the drop but became stronger after a longer drying time. The ultrasensitive detection mechanism is the sequential process of concentration, extraction, and absorption of the analyte during evaporation of self‐lubrication drop and hot spot generation for intensification of SERS signals. This novel approach for sample preparation in ultrasensitive SERS detection can be applied to the detection of chemical and biological signatures in areas such as environment monitoring, food safety, and biomedical diagnostics.
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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".