Paper‐based surface‐enhanced Raman spectroscopy sensors for field applications
Bibliographic record
Abstract
Abstract Paper‐based surface‐enhanced Raman spectroscopy (SERS) sensors can be fabricated easily by dropcasting or inkjet printing colloidal Au nanoparticles onto cellulose‐based filter papers. They are flexible, economical, and sensitive and provide the crucial advantage of point‐of‐need sampling for application in the field. In this study, paper‐based SERS sensors are fabricated through inkjet printing of a colloidal Au sol onto a filter paper substrate. We have characterized their SERS performances with benzenethiol and pyridine molecules using a handheld Raman analyzer. Due to the heterogeneous loading of the Au nanoclusters on the paper substrate, we introduce the concept of receiver operating characteristic as an alternate measurand to quantify the performance of these sensors. With their inherent filtration sampling capability, we demonstrate the use of paper SERS sensors for the detection of chemical aerosols. Lastly, we present the use of a precision materials printer to deposit quantifiable amounts of analyte (fentanyl) uniformly across the active sensing area of a paper SERS sensor. This will allow for analyte‐loaded certified references to be prepared and used in the field as standards for comparison.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".