Raman Sensor Design for Point of Care Medical and Environmental Analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».