Standoff infrared spectroscopy on energetic materials using hydrogel microcantilevers

Chae, Inseok; Khan, Mohammad Faheem; Song, Jaeki; Lee, Jaeseol; Lee, Jungchul; Thundat, Thomas

doi:10.1109/memsys.2016.7421543

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Detection limits of 600 ng/cm 2 and 40 ng/cm 2 were achieved, for RDX and PETN, respectively. 170 Selectivity in microcantilever-MEMS can be achieved by incorporating transducing elements with selective binding for ana-lytes of interest. Palaparthy et.al reports an Enose with piezo-resistive micro-cantilever sensors coated with mercaptobenzoic acid, methyl benzene thiol and amino thiol phenol for selective detection of TNT, RDX, and PETN at ppb levels.…”

Section: Micro-electromechanical Systemsmentioning

confidence: 99%

“…IR-active analytes present on the microcantilever causes a reduction in the natural resonance frequency of the microcantilever, thus providing a molecular “fingerprint”. Detection limits of 600 and 40 ng/cm 2 were achieved for RDX and PETN, respectively . Selectivity in microcantilever MEMS can be achieved by incorporating transducing elements with selective binding for analytes of interest.…”

Section: Sensorsmentioning

confidence: 99%

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Recent Developments in the Field of Explosive Trace Detection

2020

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Explosive trace detection (ETD) technologies play a vital role in maintaining national security. ETD remains an active research area with many analytical techniques in operational use. This review details the latest advances in animal olfactory, ion mobility spectrometry (IMS), Raman and colorimetric detection methods. Developments in optical, biological, electrochemical, mass and thermal sensors are also covered in addition to the use of nanomaterials technology. Commercially available systems are presented as examples of current detection capabilities and as benchmarks for improvement. Attention is also drawn to recent collaborative projects involving government, academia and industry, to highlight the emergence of multimodal screening approaches and applications. The objective of the review is to provide a comprehensive overview of ETD by; highlighting challenges in ETD and providing an understanding of the principles, advantages and limitations of each technology and relating this to current systems.

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Section: Micro-electromechanical Systemsmentioning

confidence: 99%

Section: Sensorsmentioning

confidence: 99%

Recent Developments in the Field of Explosive Trace Detection

2020

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Bulk detection of explosives and development of customized metal oxide semiconductor gas sensors for the identification of energetic materials

Konstantynovski

Njio

Börner

et al. 2018

Sensors and Actuators B: Chemical

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Discrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning

Jeong,

Park,

Kim

et al. 2024

Sensors

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Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring.

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Standoff infrared spectroscopy on energetic materials using hydrogel microcantilevers

Cited by 3 publications

References 6 publications

Recent Developments in the Field of Explosive Trace Detection

Recent Developments in the Field of Explosive Trace Detection

Bulk detection of explosives and development of customized metal oxide semiconductor gas sensors for the identification of energetic materials

Discrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning

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