Raman Sensor Design for Point of Care Medical and Environmental Analysis
Bibliographic record
Abstract
Due to its ability to directly probe water containing samples as well as yielding specific signal, Raman spectroscopy is rapidly expanding to various fields. The great potential of this technique has also drawn a lot of attention on the different ways by which Raman signal can be enhanced, notably by plasmonic nanostructures. Plasmonic materials enhance Raman by generating an enhanced oscillating field. The strength of that field has a great impact on the intensity of the Raman signal obtained from analysis with these materials. This field can be optimized by various methods shown before like an optimized nanostructure, addition of a stronger plasmonic metal, or alternating layers of metal and dielectric. We report here the result of a study of a newly designed plasmonic sensor combining multiple of these previously mentioned optimizations. This sensor is designed to be used for point of care analysis of street drugs on Canadian supervised consumption or overdose prevention sites and for analysis of microalgaes secretions in the ocean. Street drugs are to be analysed in order to detect contaminants and dangerous component. A user warned about a dangerous compound in its drugs is more likely to reduce his dose or even avoid taking the drug at all, effectively preventing unfortunate consequences. On the other hand, algaes play an important role in the marine ecosystem. Their life cycles and the bio-molecules they produce correlate with the environment in which they grow. Being able to detect, identify and quantify these secretions in real time directly in the ocean would allow us to use them as sensors for global warming. As an example, the melting of the icecaps would locally dilute the nutrients which in turn would change the way the algaes survive and their secretions. The resulting sensor must therefor offer strong Raman signal and good stability, be easy to use and produce, and be cheap to produce. To tackle these multiple challenges, we have developed a sensor produced directly on a stressed polymer sheet available in arts and craft stores. The sheets are covered with layers of metal and nanostructures before being heated in an oven. The heat causes the polymer to shrink, effectively wrinkling the metal sheets on its surface. This type of surface has first been studied with various thicknesses of silver and gold. The best surface was then studied with additional structures on its surface in order to create both a stronger and a more homogeneous surface. Keeping the targeted analytes in mind, the sensors’ potential was evaluated both with 633 nm and 785 nm laser. Ability to choose the wavelength is important as a lower laser wavelength offers a stronger Raman intensity, but a higher wavelength limits fluorescence of the analyte or solution. Sensors were also tested both in air and in water, and compared with other Raman sensors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".