White Light Transmission Spectroscopy for Rapid Quality Control Imperfection Identification in Nanoimprinted Surface-Enhanced Raman Spectroscopy Substrates
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Miniaturized biomedical sensor development requires improvements in lithographic processes in terms of cost and scalability. Of particular promise is nanoimprint lithography (NIL), but this can suffer from a lack of high-fidelity pattern reproducibility between master and imprinted substrates. Herein, we present a multidisciplinary investigation into gold- and iron-coated NIL sensors including custom optics and spectroscopy, scanning probe microscopy, and data analysis insights. Polyurethane NIL-made nanodome arrays were interrogated with white light transmission spectroscopy, coupled with principal component analysis (PCA) to investigate potential offsets in the photon-substrate plane interaction angle, an imperfection in NIL substrates. Large-angle mismatches (2–10°) were found to be easily discernible by PCA with statistically significant differences ( p = 0.05). Unexpected dips in some spectra are postulated to be due to interacting localized and propagating plasmon polaritons, which is supported with a coupled two-oscillator model. General insights are made regarding the interpretation of PCA loadings, which should be related to physical phenomena, and where maximum variance is not necessarily the most meaningful criterion. Smaller angles (<1°) show no significant differences with overlapping confidence intervals in PCA space. Surface-enhanced Raman spectroscopy (SERS) measurements on gold-coated nanodomes returned relative standard deviations of 6–10% via analysis of gelatin, which is of interest as a nasal lining approximation. Interestingly, nanodomes coated in iron produced small, but useful SERS enhancements, which was subsequently interrogated via scanning thermal probe microscopy showing temperature increases of up to 5 °C over the area of one nanostructure (∼1 μm 2 ). Nanostructures remained intact despite the surprising large local temperature increase relative to a gold-coated comparison sample (∼2 °C). The current study provides a framework for the rapid and accurate quality control assessment of imperfections in NIL-produced nanostructures for sensing applications in SERS and surface plasmon resonance, which may need precisely fabricated nanostructures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".