Thermal and spectroscopic analysis of nitrated compounds and their break-down products using gas chromatography/vacuum UV spectroscopy (GC/VUV)

Cruse, Courtney A.; Goodpaster, John V.

doi:10.1016/j.aca.2020.11.041

Cited by 10 publications

(6 citation statements)

References 64 publications

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“…GC−VUV has been previously reported to be a useful tool for the investigation of the decomposition temperatures of explosives based on correlations with spectral shape and oxygen balance. 44 The addition of OB simply adds one more input feature, so the incremental increase in the performance of RF and GBT models could be expected. The diminished performance of the MLP model with the addition of OB might be due to the addition of the OB feature increasing the complexity of the model and sparsity of the input data.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning

Ho Manh,

Chen,

Rosenberger

et al. 2024

J. Chem. Inf. Model.

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Ultraviolet (UV) absorption spectroscopy is a widely used tool for quantitative and qualitative analyses of chemical compounds. In the gas phase, vacuum UV (VUV) and UV absorption spectra are specific and diagnostic for many small molecules. An accurate prediction of VUV/UV absorption spectra can aid the characterization of new or unknown molecules in areas such as fuels, forensics, and pharmaceutical research. An alternative to quantum chemical spectral prediction is the use of artificial intelligence. Here, different molecular feature representation techniques were used and developed to encode chemical structures for testing three machine learning models to predict gas-phase VUV/UV absorption spectra. Structure data files (.sdf) and VUV/ UV absorption spectra for 1397 volatile and semivolatile chemical compounds were used to train and test the models. New molecular features (termed ABOCH) were introduced to better capture pibonding, aromaticity, and halogenation. The incorporation of these new features benefited spectral prediction and demonstrated superior performance compared to computationally intensive molecular-based deep learning methods. Of the machine learning methods, the use of a Random Forest regressor returned the best accuracy score with the shortest training time. The developed machine learning prediction model also outperformed spectral predictions based on the time-dependent density functional theory.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning

Ho Manh,

Chen,

Rosenberger

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Cruse, C. and Goodpaster, J. reported that they optimized various parameters (inlet temperatures, flow rate, and detector gas pressure) for a GC/VUV (gas chromatography-vacuum ultraviolet) system for the examination of several organic high explosives using statistical analysis called response surface methodology or RSM [ 343 ].…”

Section: Instrumental Analysis Of Explosivesmentioning

confidence: 99%

Interpol review of the analysis and detection of explosives and explosives residues

Klapec

Czarnopys

Pannuto

2023

Forensic Science International: Synergy

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“…Thus, the creation of quick and precise explosive detection systems can significantly increase public safety and stop terrorist attacks. The development of miniaturized, integrated, and portable explosive detection systems is hampered by the inherent drawbacks of traditional detection methods such as gas chromatography, , spectroscopic analysis, , and image processing, which are huge in size, expensive, and reliant on complicated algorithms. Recent years have seen a fast advancement in sensor technology, making the aforementioned problems manageable.…”

Section: Introductionmentioning

confidence: 99%

Dual Improvement in Sensitivity and Humidity Tolerance of a NO₂ Sensor Based on 3-Aminopropyltriethoxysilane Self-Assembled Monolayer-Functionalized SnSe₂ for Explosive Photolysis Gas Detection

Guo

Shi

Liu

et al. 2023

ACS Appl. Mater. Interfaces

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Explosives can be analyzed for their content by detecting the photolytic gaseous byproducts. However, to prevent electrostatic sparking, explosives are frequently preserved in conditions with low temperatures and high humidity, impeding the performance of gas detection. Thus, it has become a research priority to develop gas sensors that operate at ambient temperature and high humidity levels in the realm of explosive breakdown gas-phase detection. In this work, 3-aminopropyltriethoxysilane (APTES) self-assembled monolayer-functionalized tin diselenide (APTES-SnSe 2 ) nanosheets were synthesized via a facile solution stirring strategy, resulting in a room-temperature NO 2 sensor with improved sensitivity and humidity tolerance. The APTES-SnSe 2 sensor with moderate functionalization time outperforms the pure SnSe 2 sensor in terms of the response value (317.51 vs 110.98%) and response deviation (3.11 vs 24.13%) under humidity interference to 500 ppb NO 2 . According to density functional theory simulations, the stronger adsorption of terminal amino groups of the APTES molecules to NO 2 molecules and stable adsorption energy in the presence of H 2 O are the causes of the improved sensing capabilities. Practically, the APTES-SnSe 2 sensor achieves accurate detection of photolysis gases from trace nitro explosives octogen, pentaerythritol tetranitrate, and trinitrotoluene at room temperature and various humidity levels. This study provides a potential strategy for the construction of gas sensors with high responsiveness and antihumidity capabilities to identify explosive content in harsh environments.

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Thermal and spectroscopic analysis of nitrated compounds and their break-down products using gas chromatography/vacuum UV spectroscopy (GC/VUV)

Cited by 10 publications

References 64 publications

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning

Interpol review of the analysis and detection of explosives and explosives residues

Dual Improvement in Sensitivity and Humidity Tolerance of a NO₂ Sensor Based on 3-Aminopropyltriethoxysilane Self-Assembled Monolayer-Functionalized SnSe₂ for Explosive Photolysis Gas Detection

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Thermal and spectroscopic analysis of nitrated compounds and their break-down products using gas chromatography/vacuum UV spectroscopy (GC/VUV)

Cited by 10 publications

References 64 publications

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning

Interpol review of the analysis and detection of explosives and explosives residues

Dual Improvement in Sensitivity and Humidity Tolerance of a NO2 Sensor Based on 3-Aminopropyltriethoxysilane Self-Assembled Monolayer-Functionalized SnSe2 for Explosive Photolysis Gas Detection

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Dual Improvement in Sensitivity and Humidity Tolerance of a NO₂ Sensor Based on 3-Aminopropyltriethoxysilane Self-Assembled Monolayer-Functionalized SnSe₂ for Explosive Photolysis Gas Detection