Influences of nanoparticle loading in paper-SERS sensors
Bibliographic record
Abstract
We fabricate SERS sensors by inkjet printing and demonstrate that their SERS response correlates with their diffuse reflectance characteristics. Using a modified commercial inkjet-printer, SERS sensors are prepared with multiple printing passes. Performances of the printed sensors only become noticeable after five printing passes as observed in both SERS and diffuse reflectance measurements. 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. Although sensors with a very high number of printing passes exhibit a much stronger SERS response from the benzenethiol reporter molecule, we also noticed a significant increase in the background from blank sensors. This may not be a desirable feature particularly for the detection of weakly bound molecules. Controlling SERS background and attaining a desirable SERS enhancement would need to be balanced in the design of sensors for the end-user’s specific need.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".