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Record W3025515890 · doi:10.1149/ma2020-01352429mtgabs

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

2020· article· en· W3025515890 on 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

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSorbentAnalyteChemistryAbsorption (acoustics)Organic chemistryChromatographyAdsorptionMaterials science

Abstract

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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: 1. 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. 2. 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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.017
GPT teacher head0.262
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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