Enhanced Raman sensors for vapour and aerosol detection
Bibliographic record
Abstract
We fabricate SERS sensors by inkjet printing of colloidal Au nanoparticles on porous paper substrates. Using a modified commercial inkjet-printer, SERS sensors are prepared with multiple printing passes. SERS response is correlated with their diffuse reflectance characteristics. Chemical analyte detection is only possible with both SERS and diffuse reflectance measurements from sensors that has been subjected to more than 5 printing passes. This suggests that the simpler diffuse reflectance measurement can be used as an alternative method to characterize and optimize the SERS performance of the printed sensors. Sensors with a higher number of printing passes exhibit a much stronger SERS response from strong adsorbing analytes such as benzenethiol molecule. We compare the performances of 8 and 15 printing passes sensors with the commercially available paper-based sensors, p-SERS. We calculate their Relative Enhancement Factor (REF) by comparing their performances to the first order phonon vibration of Si, which serves as a reference standard. Lastly, we demonstrate the use of such sensors for the detection of chemical aerosols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".