Publication:
Quantum cascade laser-grazing angle spectroscopy detection of high explosives deposited on various substrates using air spray

dc.contributor.advisor Hernández Rivera, Samuel P.
dc.contributor.author Colón Mercado, Annette Mariel
dc.contributor.college College of Arts and Sciences - Sciences en_US
dc.contributor.committee Mina, Nairmen
dc.contributor.committee Torres Candelaria, Jessica
dc.contributor.department Department of Chemistry en_US
dc.contributor.representative Hernández Maldonado, Arturo J.
dc.date.accessioned 2020-11-05T19:38:03Z
dc.date.available 2020-11-05T19:38:03Z
dc.date.issued 2020-04-19
dc.description.abstract The mid-infrared (MIR) laser reflectance of samples of high explosives (HEs) deposited on reflective and matte substrates using spray deposition was measured and used to generate multivariate (MVA) models for the analysis of the accurate detection and classification. A quantum cascade laser (QCL) was optically coupled to a grazing angle probe mount (QCL-GAP) to operate in reflectance mode at an incidence angle of 82 from the surface normal. The experimental conditions enabled reflection-absorption IR spectroscopy (RAIRS) of the HE/substrates. The optical arrangement allowed to obtain spectra with high signal to noise (S/N) ratios, therefore allowing the signals of the HE to be detectable even at low surface concentrations. Reflective substrate: stainless steel (SS) and partially reflective substrates: roughened aluminum (Al) and Teflon, were sprayed with HE solutions for the creation of real-world samples. The HE used for the spray deposition was pentaerythritol tetranitrate (PETN). These samples were compared to inkjet standards containing: 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), and 2,4,6-trinitrophenylmethylnitramine (Tetryl). Samples were successfully classified into the classes of Al substrates and the highly interfering MIR signals from acrylonitrile butadiene styrene (ABS) substrates. Loadings plots were constructed based on the information obtained from the PCA models for each substrate, allowing us to understand the variation that contributed to the separation of the HEs by classes. Soft independent modeling by class analysis (SIMCA) was used to predict the HEs vs. PETN deposited using a spray. The results predicted a percentage of 91.7% for Teflon, 100% for Al, and 87.5% for SS. The results presented are expected to simplify the detection of hazardous chemicals and other threats to environmental, defense, and national security applications. en_US
dc.description.abstract La reflectancia de un laser de infrarrojo medio (“MIR,” por siglas en inglés) se midió usando muestras de materiales energéticos (“HE”) depositados sobre sustratos reflectivos y no reflectivos mediante deposición por aerosol. Se utilizó esta data para generar modelos de análisis de multivariables (“MVA”) para el análisis preciso de detección y clasificación de HE/sustratos. Un láser de cascada cuántica (“QCL”) se acopló ópticamente a una sonda de ángulo de rozamiento (“GAP”) para operar en modo de reflectancia a un ángulo de incidencia de 82 con respecto a la normal de la superficie. Las condiciones experimentales permitieron operar en el modo de espectroscopía IR de reflexión-absorción (“RAIRS”) de materiales energéticos depositados sobre superficies. El arreglo óptico permitió obtener espectros con cocientes de señal a ruido (S / N) altas, permitiendo así que los HE se pudieran detectar a bajas concentraciones superficiales. Acero inoxidable pulido (“SS”), sustrato altamente reflectivo y sustratos parcialmente reflectivos: aluminio rugoso (“Al”) y Teflón, se rociaron con soluciones de PETN para la preparación de muestras parecidas a las encontradas en el mundo real. El HE utilizado para la deposición por aerosol fue tetranitrato de pentaeritritol (“PETN”). Estas muestras se compararon con las muestras de estándares impresas con tecnología de impresora tintal (“inkjet”) que contenían: 1,3,5-trinitroperhidro-1,3,5-triazina (“RDX”) y 2,4,6-trinitrofenilmetilnitramina (“Tetryl”). Las muestras se clasificaron exitosamente en las clases de sustratos de Al y las señales MIR altamente interferentes de sustratos de acrilonitrilo butadieno estireno (“ABS”). Se construyeron gráficas de análisis de components principales “PCA”) en base a la información obtenida de los modelos para cada sustrato, lo que permite comprender la variación que contribuyó a la separación de los HE por clases. Se usó un modelaje independiente suave por análisis de clase (“SIMCA”) para predecir el PETN depositado usando la técnica de aerosol en los sustratos. Los resultados pronosticaron un porcentaje de 91.7% para Teflón, 100% para Al y 87.5% para SS. Se espera que los resultados presentados simplifiquen la detección de sustancias químicas peligrosas de interés en defensa y seguridad nacional y contaminantes ambientales. en_US
dc.description.graduationSemester Spring en_US
dc.description.graduationYear 2020 en_US
dc.description.sponsorship This material is based upon work supported by the U.S. Department of Homeland Security, Science and Technology Directorate, Office of University Programs, under Grant Award 2013-ST-061-ED0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. en_US
dc.identifier.uri https://hdl.handle.net/20.500.11801/2696
dc.language.iso en en_US
dc.rights.holder (c) 2020 Annette Mariel Colón Mercado en_US
dc.rights.license All rights reserved en_US
dc.subject High energetic (HE) en_US
dc.subject Principal component analysis (PCA) en_US
dc.subject Mid-infrared spectroscopy en_US
dc.subject.lcsh Quantum wells en_US
dc.subject.lcsh Semiconductors lasers en_US
dc.subject.lcsh High energy forming en_US
dc.subject.lcsh Infrared spectroscopy en_US
dc.subject.lcsh PETN en_US
dc.subject.lcsh Principal component analysis en_US
dc.title Quantum cascade laser-grazing angle spectroscopy detection of high explosives deposited on various substrates using air spray en_US
dc.type Thesis en_US
dspace.entity.type Publication
thesis.degree.discipline Chemistry en_US
thesis.degree.level M.S. en_US
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