Bibliographic record
Abstract
Surface Enhanced Raman Spectroscopy (SERS) enhances spontaneous Raman spectroscopy by the virtue of plasmon resonance of nanoparticles. Clinical application of SERS is challenging as nanoparticles remain in the body for long periods of time and a full toxicity analysis has yet to be extensively studied. In this study, Nanosphere lithography (NSL) was used to create optical fibers with nanoparticle enhanced tips for remote sensing using SERS. A custom designed RS collection setup was created for optimal collection of spectra from the optical fibers. It was found that an optical fiber with 0.5 numerical aperture (NA) allowed for better detection of Raman peaks while mitigating the fluorescence background of the optical fiber without any optical filters. Such a sensing platform can potentially be used to temporarily introduce nanoparticles into a sensing environment as it allows retracting the nanoparticles along with the tip. Nanoporous SERS platform has been fabricated using nanoporous silica glass with 7 nm and 17 nm pore diameters. An inexpensive fabrication approach of sputter deposition of Au layers was employed on prefabricated nanoporous silica glasses. 7 nm pore glasses provided larger enhancement than the glasses with 17 nm pores. A gold layer thickness of 25 nm was observed to produce largest enhancements. Nanoporous SERS substrates allow a larger effective SERS area compared to NSL based fabrication substrates and such nanoporous structures can be potentially fabricated on optical fiber tips for remote sensing. Finite Element Modeling (FEM) method was implemented for simulating single nanoparticles, an infinite periodic array of nanoparticles and nanoporous films using COMSOL Multiphysics software package. The extinction spectra obtained theoretically were found to match the experimental results for single nanoparticles. The maximum enhancement for the periodic array was two orders of magnitude larger than single particles while the integrated (average) enhancement was only two and a half times larger. Nanoporous films were also modelled using the FEM technique. Preliminary clinical data were collected from excised breast tissues for evaluating RS as a tool for cancer diagnostics. Spectral peaks from healthy tissues were found to be prominent than cancerous tissues and further experiments are needed to create a multivariate classification model for diagnostics.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".