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Enregistrement W4247222244 · doi:10.1149/ma2021-01631698mtgabs

Fluorinated Bisphenol Sorbent Materials for Spectroscopic Chemical Threat Sensing and Photonics Applications

2021· article· en· W4247222244 sur OpenAlexaboutno aff
Courtney A. Roberts, Tyler G. Grissom, Roselyn Rodrigues, Viet K. Nguyen, Andrew Kusterbeck, Michael R. Papantonakis, Nathan F. Tyndall, Dmitry A. Kozak, Todd H. Stievater, R. Andrew McGill

Notice bibliographique

RevueECS Meeting Abstracts · 2021
Typearticle
Langueen
DomaineChemical Engineering
ThématiqueAnalytical Chemistry and Sensors
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSorbentChemistryAnalyteAbsorption (acoustics)Organic chemistryAdsorptionChromatographyMaterials science

Résumé

récupéré en direct d'OpenAlex

Alcoholic and phenolic hydrogen-bond (HB) acidic absorbents activated by fluorine chemistries have been previously developed at the U.S. Naval Research Laboratory and elsewhere to augment sorbent HB acidity, reduce sorbent HB basicity and target complementary HB basicity of a wide range of hazardous target chemicals. A significant limitation of HB sorbents developed to date has been the propensity for sorbent self-association. This intermolecular bonding between sorbent molecules hinders sorbate access to active HB acid sites in the sorbent, limiting its overall efficacy. Sorbent self-association is evidenced by broadened hydroxyl peaks in the mid-infrared (MIR) region, which can obscure important absorption frequencies that appear upon absorption of an analyte into the sorbent. In this current work our aim is to develop improved HB acidic sorbent compounds which minimize undesired sorbent self-association in order to apply these sorbents to infrared- and Raman-based sensing devices for chemical threat detection. A series of new HB acidic sorbents have been synthesized and the subsequent sorbent-sorbate interactions have been characterized by a number of methods. MIR spectroscopy has been used to help elucidate sorbent-analyte vapor interactions. The newly synthesized sorbents have been challenged with various analyte vapors, including toxic industrial chemicals, chemical warfare agent simulants, and background interferents. A particular focus of these characterization efforts has been to observe the spectral changes that occur in the hydroxyl region of the MIR upon exposure of a sorbent material to an analyte vapor. Analyte binding of an HB base occurs principally at the sorbent hydroxyl site. The resulting redshift of the hydroxyl stretching frequency is characteristic of the basicity of the analyte. These sorbent materials are specifically designed to be selective toward hazardous chemicals through complementary hydrogen-bonding interactions between sorbent and analyte molecules. Generally, common interferents, such as hydrocarbons, have little or no hydrogen-bond basicity, while hazardous chemicals of interest have moderate to high basicity. More strongly HB basic analytes trigger larger redshifts of the hydroxyl absorption frequency. Benchtop FTIR characterization has confirmed that these newly designed sorbent materials are responsive to threat chemicals of interest at low concentrations and largely unresponsive to interferent chemicals, even at relatively high concentrations. Based on the strong affinity of these sorbents to threat chemicals of interest and the significant spectral changes that occur in the MIR upon formation of the hydrogen-bonded complex, these sorbent materials make useful candidates for MIR sensing applications. A frequency shift of the hydroxyl stretch indicates a sorbate has formed a HB with the sorbent. The magnitude of the frequency shift correlates with the basicity of the analyte, which is indicative of the class of compound to which the newly bound chemical belongs. In a sensing application, this feature can provide an alert that a hazardous chemical is present, even if it is an unknown threat. While the hydroxyl region allows for class specificity of unknown compounds, the fingerprint region complexity may facilitate specific analyte recognition. At present, this work has been focused on analysis of the hydroxyl region and distinction of different classes of compounds, but future efforts will turn to the fingerprint region to provide an avenue for specific chemical identification. Raman spectroscopy has also been used to characterize these sorbent materials. Specifically, a technique known as waveguide-enhanced Raman spectroscopy (WERS) has been used, which features the use of highly evanescent, low-loss waveguides with the sorbent material as a top cladding.2 WERS can be achieved using an incredibly small footprint with a sorbent-functionalized nanophotonic waveguide that is approximately a few centimeters long. Using WERS, the differential Raman spectra of the sorbent material interacting with different chemical warfare agent simulants has been measured at parts-per-billion detection levels. The spectra exhibit extrapolated three-sigma detection limits as low as 3 ppb. Continuing efforts are focused on adapting this technique to photonic integrated circuit-based fabrication and chip-scale Raman spectroscopy for trace chemical vapor detection. This presentation will highlight the design of these next-generation sorbents as a tool to facilitate MIR- and Raman-based sensing of threat chemicals. It will focus on analyzing sorbent-analyte spectral interactions and discuss how to exploit these features to develop MIR- and Raman-based sensors. References: Roberts, C. A.; McGill, R. A. Bisphenol hypersorbents for enhanced detection of, or protection from, hazardous chemicals. U.S. Patent Application 2019/0134601 A1, 2019. Tyndall, N. F.; Stievater, T. H.; Kozak, D. A.; Koo, K.; McGill, R. A.; Pruessner, M. W.; Rabinovich, W. S.; Holmstrom, S. A. Optics Letters, 2018, 43, 4803-4806.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,248
Écart entre enseignants0,235 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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