Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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The quantification of gases in breath has gained significant attention as a modern diagnosis method due to its non-invasive nature, and as a painless and straightforward method for the early detection of physiological disorders. Several notable clinical applications have been established for disease diagnosis by correlating exhaled breath samples and specific diseases. In addition, diverse breath molecules represent a biomarker of specific illnesses and are precisely identified by the standard analytical method. However, because of the bulky equipment size, expensive cost, and complexity in measurement when using analytical methods, many researchers are focusing on developing highly selective, sensitive, stable, robust, and economical sensors for breath analysis. It is essential to optimize approaches such as breath sampling, biomarker sensing, data analysis, etc. However, the detection of ppb-level biomarkers in exhaled breath is too challenging to solve due to the abundance of interfering gases. We present a brief and comprehensive review of a recent diagnostic technique that employs nanomaterial (NM)-based sensors to identify the volatile organic compounds (VOCs) associated to diseases. Because they are easily fabricated, chemically versatile, and can be integrated with existing sensing platforms, NMs are ideal for such sensors. Initially, this review provides crucial details about certain representative biomarkers found in diseased patients’ exhaled breath and the demand for breath sensors. Subsequently, the review highlights diverse sensor technologies such as electrical, optical, and mass-sensitive gas sensors and describes their sensing capability for detecting the biomarkers’ concentrations and their primary endeavor of diagnosing disease. Finally, the pitfalls and challenges of sensor characteristics are discussed. This article lays the basis for developing high-performance gas sensors based on novel NMs.

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Department of Sensor and Biomedical Technology

School of Electronics Engineering

Vellore Campus

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Nanomaterial-Based Sensors for Exhaled Breath Analysis: A Review

This chapter discusses the development of polymer modified quartz tuning forks (QTFs) as a niche chemical sensor. The chapter begins with a short introduction to chemical sensors and how they have become imperative to our quality of life. It then moves on to describe the development of QTFs as chemical sensors from their original use as commercial timing crystals. The chapter also discusses the various ways in which QTFs may be used as sensors and focuses specifically on the development of polymer modified QTFs as direct chemical sensors. The unique ability of these polymer modified QTF sensors to detect analytes by measuring variation in Young's modulus of polymer is detailed by exploring the various applications and use cases which have been reported in literature. The chapter aims to offer a glimpse of the potential of QTF direct sensors by showcasing the diversity, sensitivity and selectivity of QTF sensors towards different analytes for various applications.

Nanotechnology in Electronics, Materials Properties, and Devices: State of the Art and Future Challenges

<a href="https://scholar.google.co.in/citations?view_op=view_citation&amp;hl=en&amp;user=AF2N0x0AAAAJ&amp;sortby=pubdate&amp;citation_for_view=AF2N0x0AAAAJ:Y0pCki6q_DkC">Quartz Tuning Fork–Based Nanosensors</a>

With the recent spurt in technological advancements, especially the advent of autonomous navigation, image processing has become ubiquitous in everyday life. Image reconstruction is an imaging application aimed at rebuilding images from a variety of features obtained from sparse input ground truths or input sources. In this work, we present an image reconstruction method to recreate and represent detailed images solely from binary edges and flat color using Generative Adversarial Networks (GANs) for a universal application, whereas all major recent breakthroughs are related to either medical imaging or noise removal. Our work has yielded promising results and we were able to obtain realistic images of objects and human faces with original as well as altered feature sets. We present here the results with both qualitative and quantitative comparisons with some benchmark frameworks.

2021 Innovations in Power and Advanced Computing Technologies (i-PACT)

Refinement Image Regeneration

Gas sensors have become indispensable, not just in potentially hazardous industries, but also for the everyday lives of ordinary citizens. In this work, Quartz Tuning Forks (QTFs) modified with nanostructured polymer wires were simulated using COMSOL Multiphysics to investigate their feasibility as gas sensors. Different polymers were used to functionalize the QTFs and a study was done to analyze which polymer is best suited for use as potential Volatile Organic Compounds (VOCs) sensors. The suitability of the polymer was reflected in the shift in eigenfrequency of the system. The relationship between properties of the polymer and the frequency response of the polymer functionalized QTF system when the QTF was both mass loaded and spring-loaded with different polymers, was studied.

<a href="https://scholar.google.co.in/citations?view_op=view_citation&amp;hl=en&amp;user=AF2N0x0AAAAJ&amp;sortby=pubdate&amp;citation_for_view=AF2N0x0AAAAJ:2osOgNQ5qMEC">Investigation of Polymer Functionalized QTFs as Potential Gas Sensors Using COMSOL Multiphysics</a>

The Wheat head detector and health classifier that is proposed in this work is a system that takes an aerial image of field wheat, at a certain distance, as an input and detects all the wheat-heads in it. After detecting them, we were able to predict the health of the crop, i.e. healthy or diseased, identifying diseased wheat heads by processing the input images. This work is based on machine learning and uses state-of-the-art machine learning algorithms i.e. Residual Neural Network (ResNet-50) and a convolutional neural network (CNN) model. Along with this, two datasets were used to train these models. Global wheat head detection (GWHD) dataset was used to train the ResNet-50 model for detecting wheat-heads and a self-constructed dataset is used to train the wheat health classifier (CNN).

Wheat Head Detection and Crop Health Classification System

VIT Unitversity

2019 IEEE Bombay Section Signature Conference (IBSSC)

IoT and Machine Learning based approach for Fully Automated Greenhouse

Several volatile organic compounds (VOCs) are present in the exhaled human breath whose concentration can vary depending on the physiological changes occurring within a human being. These changes in the concentration or the occurrence of a particular VOC can be used as signature of a particular disease in a person. In the present work, a sensor has been developed to detect VOCs such as 1,4-dimethoxy-2,3-butanediol (BD), and cyclohexanone (CH), acetone, methanol and ethanol. Except for BD and CH, the rest of the VOCs are present in a healthy person in ppm levels. CH and BD have been reported to be present in the exhaled human breath of breast cancer patients in ppm levels and can be used to distinguish between a healthy person and a person with breast cancer. The selectivity of the sensor towards these two compounds in the presence of other VOCs commonly present in human breath like acetone, ethanol and methanol has been studied. The sensor has been developed using modified Quartz Tuning Forks (QTFs) with the intent of developing an array of such sensors identifying different VOCs present in a healthy human's breath. Two differently modified QTFs have been used to detect 1 ppm of 1,4-dimethoxy-2,3-butanediol and 20 ppm of cyclohexanone. Linear Discriminants Analysis (LDA) has been used to classify seven different VOCs. For this purpose, features extracted from sensor responses -shift in resonant frequency, response time and recovery time of the sensors- have been used as features in the model. Differently modified array of QTFs along with the use of LDA can be a useful pathway towards development of a QTF based sensor array for human breath analysis.

Materials Research Express

<a href="https://scholar.google.co.in/citations?view_op=view_citation&amp;hl=en&amp;user=AF2N0x0AAAAJ&amp;sortby=pubdate&amp;citation_for_view=AF2N0x0AAAAJ:9yKSN-GCB0IC">Quartz tuning fork based sensor for detection of volatile organic compounds: towards breath analysis</a>

The authors describe a sensor capable of detecting methanol adulteration of ethanol. The sensor is based on the use of quartz tuning forks (QTFs) that were functionalized with polymer wires made from a combination of polystyrene (PS) and aniline. Exposure to organic vapors causes the resonance frequency of the functionalized QTF to change, and this can be used to identify the type and concentration of the analyte. A mixture of methanol and ethanol vapors in varying concentrations was exposed to the QTF polymer system. The resulting shift in the resonance frequency of the QTF was firstly used to determine the concentration of alcohol vapor, which is reflected in the amount of shift. Secondly, the nature of change in resonance frequency was used to determine the type of alcohol exposed to the sensor. The sensitivity and selectivity of the sensors to ethanol and methanol vapors has been investigated. A portable hand-held prototype sensor has been developed which displays the percentage of two alcohols it is exposed to. It can detect ethanol adulteration where the methanol concentration is as low as 5%. [Figure not available: see fulltext.]. © 2017, Springer-Verlag Wien.

Microchimica Acta

Quartz tuning fork based portable sensor for vapor phase detection of methanol adulteration of ethanol by using aniline-doped polystyrene microwires

Abstract Here we investigate the feasibility of using quartz tuning forks (QTFs) modified with nanostructure decorated polymer wires as potential gas sensors. The sensing parameters of different sensors for various volatile organic compounds like ethanol, methanol, chloroform and acetone were investigated. The exposure of the functionalized QTF sensors to VOC vapours induces a change in the mass as well as the mechanical properties of the polymer wire due to which the resonant frequency of the system changes. The dependence of the general behaviour and the frequency response of the sensors on both mass loading and spring loading effect was investigated and verified experimentally. © 2015 Elsevier B.V.

Sensors and Actuators, B: Chemical

Investigation of QTF based gas sensors

A methanol sensor based on polymer functionalized Quartz Tuning Fork (QTF) has been demonstrated. Thin polymer wires, formed of different polymers and with different additives, were used to form bridges between the two tines of the QTFs. The mechanical properties of the polymer wires, as well as the effective mass of the wires determine the resonant frequency of the QTF sensor. The interaction of the polymer wires with the analyte vapors caused a shift in resonant frequency of the sensor and this shift in frequency, as well as the quantum of shift was measured to determine the selectivity and sensitivity of the sensor to the analytes. The effect of ethanol and methanol vapors on the QTF sensor was observed and the dependence of the selectivity and sensitivity of the sensor on the presence of various polymers as well as the type of polymer loading is discussed. © 2015 IEEE.