Filter paper based SERS substrate for the direct detection of analytes in complex matrices
Bibliographic record
Abstract
Surface-enhanced Raman spectroscopy (SERS) is an emerging analytical technique for chemical analysis, which is favourable due to its combination of short measurement time, high sensitivity and molecular specificity. However, the application of SERS is still limited, largely because in real samples the analyte is often present in a complex matrix that contains micro/macro particles that block the probe laser, as well as molecular contaminants that compete for the enhancing surface. Here, we show a simple and scalable spray-deposition technique to fabricate SERS-active paper substrates which combine sample filtration and enhancement in a single material. Unlike previous spray-deposition methods, in which simple colloidal nanoparticles were sprayed onto solid surfaces, here the colloidal nanoparticles are mixed with hydroxyethyl cellulose (HEC) polymer before application. This leads to significantly improved uniformity in the distribution of enhancing particles as the film dries on the substrate surface. Importantly, the polymer matrix also protects the enhancing particles from air-oxidation during storage but releases them to provide SERS enhancement when the film is rehydrated. These SERS-paper substrates are highly active and a model analyte, crystal violet, was detected down to 4 ng in 10 μL of sample with less than 20% point-by-point signal deviation. The filter paper and HEC effectively filter out both interfering micro/macro particles and molecular (protein) contaminants, allowing the SERS-paper substrates to be used for SERS detection of thiram in mud and melamine in the presence of protein down to nanogram levels without sample pre-treatment or purification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".