E-Nose: Multichannel Analog Signal Conditioning Circuit With Pattern Recognition for Explosive Sensing

Palaparthy, Vijay; Doddapujar, Shambhulingayya N.; Gupta, Gaurav; Das, P.; Chandorkar, Saurabh A.; Mukherji, Soumyo; Baghini, Maryam Shojaei; Rao, V. Ramgopal

doi:10.1109/jsen.2019.2946253

Cited by 30 publications

(7 citation statements)

References 29 publications

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“…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. 171 Holthoff et. al demonstrated selective detection of 2,4-dinitrotoluene (DNT) down to 500 parts-per-trillion (ppt) using microcantilevers functionalized with molecular imprinted polymers (MIPs).…”

Section: Micro-electromechanical Systemsmentioning

confidence: 99%

“…386 LDA and PCA have been applied to a range of data types in the field of ETD, with many examples already covered in this review. 66,70,161,171 A number of machine learning algorithms have been successfully applied to E-nose systems to classify sensor inputs, leading to improved chemical selectivity. Examples include; random forest, 68,176 support vector machines 91,387 and artificial neural networks.…”

Section: Artificial Intelligence (Ai) Approaches To Etdmentioning

confidence: 99%

“…Palaparthy et al reports an E-nose with piezo-resistive microcantilever sensors coated with mercaptobenzoic acid, methylbenzene thiol, and amino thiol phenol for selective detection of TNT, RDX, and PETN at ppb levels. 171 Holthoff Figure 10. Energy level diagrams showing generalized FRET and PET processes.…”

Section: Animal Olfactory Systemsmentioning

confidence: 99%

“…Selectivity in microcantilever MEMS can be achieved by incorporating transducing elements with selective binding for analytes of interest. Palaparthy et al reports an E-nose with piezo-resistive microcantilever sensors coated with mercaptobenzoic acid, methylbenzene thiol, and amino thiol phenol for selective detection of TNT, RDX, and PETN at ppb levels . Holthoff et al demonstrated selective detection of 2,4-dinitrotoluene (DNT) down to 500 ppt using microcantilevers functionalized with MIPs .…”

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: Artificial Intelligence (Ai) Approaches To Etdmentioning

confidence: 99%

Section: Animal Olfactory Systemsmentioning

confidence: 99%

Section: Sensorsmentioning

confidence: 99%

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Recent Developments in the Field of Explosive Trace Detection

2020

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“…Node 1 (S1), Node 2 (S2), Node 3 (S3) and 1 node with commercial LWS (PHYTOS 31). Before trying to estimate the LWD values, we must apply some data cleaning and processing techniques to improve our data quality [39,40]. This process is called data pre-processing.…”

Section: A Data Pre-processingmentioning

confidence: 99%

IoT Enabled, Leaf Wetness Sensor on the Flexible Substrates for In-Situ Plant Disease Management

et al. 2021

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Early plant disease detection and providing the control measures have become highly desirable to improve crop yield. Leaf wetness duration (LWD) is one of the essential parameters related to the development of fungal disease on the leaf canopy. To measured LWD, the leaf wetness sensor (LWS) is widely used. Commercially available LWS are made on printed circuit board (PCB) technology, which has an operational issue during field deployment such as weight of the sensor, contact resistance between the sensor and the leaves, form factor and most importantly, affordability. To mitigate the issues associated with the commercially available LWS, in this work, we have fabricated the in-house IoT-enabled and affordable electronic leaf wetness sensor on the flexible substrates, which is used for integrated plant disease management. Fabricated LWS comprises the interdigitated electrodes (IDEs) on the polyimide flexible substrate. The lab measurement results indicate that fabricated LWS on the flexible substrates offers a response of about 36000% when LWS is exposed to water w.r.t air. The observed response time of the fabricated LWS is about 10 seconds and hysteresis of about ± 4 %. Further, sensor capacitance changes only by 6% over a temperature range from 20 °C to 65 °C. Furthermore, three fabricated sensors LWS and in-house developed internet of things (IoT) enabled systems are deployed on the Ocimum tenuiflorum (Tulsi) medical plant. Field measurement indicates that measured LWD using the fabricated flexible LWS and commercially available LWS (Phytos 31:LWS-L12), METER Group, Inc. USA) shows the absolute difference of 30 minutes.

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