The geometric design of electrodes plays a crucial role in determining the biosensor sensitivity and resolution by altering the Electric field (E). Specifically, the value of electric sensing parameters like resistance (R), capacitance (C), and impedance (Z), inherently depends on the strength of the generated electric field between electrodes. So, one must generate a large electric field for the applied AC or DC voltage. Unfortunately, the rigorous practical study on the same is limited on account of cleanroom-based fabrication techniques' cost and time. So, it is essential to study and analyze the geometrical performance of electrodes using simulations - present work aimed at this. In this work, we specifically selected gold-interdigitated microelectrodes (IDμEs) as one of the most viable alternatives to conventional two-electrode systems based on the enhanced field strength (E) generated for the same applied voltage. Specifically, the geometric study of gold IDμEs was carried out using the COMSOL Multiphysics simulator by varying the inter-finger distance and number of fingers between the electrodes. Based on the generated electrical field strength, one can select the best design for biosensor fabrication.

Drop-casted polypyrrole (PPY) nanomaterial-based point-of-care Traumatic Brain Injury (TBI) immunosensing platforms reported previously demand trained manpower at field-test, due to poor adhesion between nanomaterial and electrode surface, limiting the point-of-care purpose. The usage of conventional clean-room-based physical and chemical vapor deposition techniques in creating strong adhesion is limited on account of cost and process complexity. Addressing this technical gap, we report a novel low-cost clean-room-free technique that can effectively electrodeposit the PPY simultaneously onto the working areas of array of Interdigitated microelectrodes (IDμEs) from the precursor solution. Through optimization of deposition cycles and molar concentration ratio of monomer and oxidizing agents, a high-quality nanomaterial was electrodeposited on IDμEs' surface. Further, by using the electrodeposited PPY as a bioelectrical transducer, the TBI-specific UCHL1 and GFAP target analytes were simultaneously detected in terms of variation of DC-Resistance and AC-Capacitance parameters, recorded through chemiresistive I-V and chemicapacitive C-F responses of bioelectrodes, respectively. Such simultaneous multianalyte-detection in terms of multiple parameters increases the diversity of decision-making parameters by several folds, inherently aids in enhancing the diagnostic accuracy of TBI test kit. Here, the efficiency of the electrodeposited PPY-based chemiresistive and chemicapacitive immunosensing platforms in detecting TBI-specific target analytes simultaneously in real-time human-plasma samples was analyzed in terms of sensitivity, resolution, LoD, RoD, long-term stability (30 weeks), and the same is compared with drop-cast PPY-based immunosensing platform. Notably, the electrodeposited PPY sensing platforms showed superior performance in terms of sensitivity, LoD, device variability and long-term stability without demanding any trained manpower in the field.

In the huge growth of semiconductor industry, it is noticed that the device simulation is a very sluggish process. It is very promising to use Machine Learning (ML) techniques in device modeling as their combination will create great results in semiconductor industry and reduce the computational time. Organic Thin Film Transistor (OTFT) is a promising alternative to amorphous silicon devices due to its flexibility, low cost, and can be manufactured at reduced temperatures. In traditional TCAD simulation, at once only a single simulation of OTFT for fixed length, width and dielectric thickness can be done, for change in any of the input parameter again simulation has to be done. To avoid this ML is used to predict drain current for simultaneous changes in input parameters. This introduces a machine learning based structure to model OTFT integrated with ML algorithm named Random Forest Regressor (RFR). ML based device model for p-type OTFT takes length, width and thickness of dielectric layer as input parameters and drain current as output parameter. Experimental results has shown that our ML-based model can predict drain current accurately. R2-value is found be around 0.997253. ML based performance optimization is a promising alternative to traditional technology computer aided design (TCAD) tools. The highest ION/IOFF ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for OTFT. ION/IOFF ratio is obtained to be 1011. The trained RFR models can accelerate the optimization in terms of performance and serves as promising alternative.

With the rapid growth of the semiconductor industry, it is clear that device simulation has been considered as slow process. As a result of semiconductor device downscaling, obtaining the inevitable device simulation data is significantly more expensive because it requires complex analysis of multiple factors. Using Machine Learning (ML) techniques to device modeling is promising, as their combination will lead to great outcomes in the semiconductor industry. Nanosheet Field Effect Transistor (NSFET) is a promising device for high-performance integrated circuits due to their superior electrical control and reduced short-channel effects. This paper presents a ML based Nanosheet Field Effect Transistor modeling. In traditional Technology Computer-Aided Design (TCAD) simulation, at once only a single simulation of NSFET for fixed length, width and thickness can be done, for change in any of the input parameter again simulation has to be done. To overcome this, simultaneous changes in input parameters are predicted using machine learning. The length, width, and thickness of the dielectric layer are input parameters and the drain current is the output parameter for the ML-based device model for NSFET. Experimental results have shown that our ML-based model can predict drain current accurately. R2-value is found be around 0.99832. The highest ION/IOFF ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for NSFET. The primary goal of this work is to explore the possibility of ML model that can replace the device simulation to reduce the computational cost and drive energy-efficient devices.

Accurate detection of gram-positive bacterial colonies is essential for managing chronic wounds and overcoming delays in healing, as these bacteria can worsen wound conditions and impede recovery. This study introduces a cost-effective electrochemical sensing platform designed to support healthcare professionals in making timely, targeted treatment decisions. We developed the platform using chemically functionalized amine-terminated carbon surfaces combined with the TLR1/TLR2 heterodimer complex to detect gram-positive bacteria. The biosensors featuring these advanced carbon surfaces demonstrated superior performance due to their high surface area and efficient electron transfer capabilities. The TLR1/TLR2-based sensors accurately identified gram-positive bacteria, with a theoretical detection limit of 0.0413 CFU/mL. The sensors also exhibited high selectivity and sensitivity, with a response rate of 220.878 ((ΔR/R)/CFU/mL)/cm2 for the amine-terminated carbon surfaces. This novel electrochemical sensing platform provides an effective solution for real-time detection and management of gram-positive bacterial infections in chronic wound care.

An early diagnosis of Alzheimer's disease (AD) is challenging and affects millions worldwide. AB(1-40), a potential biomarker found in cerebrospinal fluid, blood, and its derivatives, is utilized as an alternative for an early diagnosis of Alzheimer's. This work presents an early detection of AD with the help of label-free electrochemical transduction mechanisms using AB(1-40) as a biomarker. To increase the diversity of decision-making parameters that inherently improve the disease's diagnostic accuracy, the detection was carried out with the help of DPV and EIS analysis. The sensing platform utilized electrospun tin-oxide (SnO2) nanofibers modified carbon electrodes as a transducing element comprising covalently immobilized AB(1-40) antibodies on which the target AB(1-40) binds specifically. The sensing platform detected the target analyte concentrations prepared in real-time human blood plasma in the linear detection range of 1 fg/mL - 10 ng/mL and 1 fg/mL - 100 pg/mL obtained from DPV and EIS, respectively. It also accounted for an extremely low detection limit of 0.785 and 0.573 fg/mL and a very high sensitivity of 4.095 (μA/(ng/mL))/cm2 and 285.94 (kΩ/(ng/mL))/cm2 obtained from DPV and EIS, respectively. Further, the proposed sensing platform showed excellent selectivity, repeatability, reproducibility and high interference resistance.

Traumatic Brain Injury, one of the significant causes of mortality and morbidity, affects worldwide and continues to be a diagnostic challenge. The most desirable and partially met clinical need is to simultaneously detect the disease-specific-biomarkers in a broad range of readily available body fluids on a single platform with a rapid, low-cost, ultrasensitive and selective device. Towards this, an array of interdigitated microelectrodes was fabricated on commercially existing low-cost single-side copper cladded printed-circuit-board substrate followed by the bioelectrodes preparation through covalent immobilization of brain injury specific biomarkers on carboxylic functionalized multi-walled carbon nanotubes embedded polypyrrole nanocomposite modified interdigitated microelectrodes. Subsequently, the immunological binding events were transduced as the normalized change in bioelectrode resistance with and without the target analyte via current-voltage analysis. As proof of concept, current-voltage responses were primarily recorded using a conventional probe station, and later, a portable handheld-electronic-readout was developed for the point-of-care application. The data compilation and analysis were carried out using the in-house developed android-based mobile app. Notably, the smartphone powered the readout through a PL-2303 serial connector, avoiding integrating power sources with the readout. Further, this technology can be adapted to other point-of-care biosensing applications.

Detecting gram -ve bacterial colonies is crucial in address-ing the clinical challenges associated with chronic wounds and delayed healing. These bacteria can exacerbate wound conditions, hindering natural healing and potentially leading to infections. The electrochemical sensing platform presented in this study serves as a valuable tool for healthcare professionals to make timely and targeted treatment decisions. Toward this, we developed a cost-effective electrochemical sensing platform leveraging the TLR4/MD-2 complex to detect gram -ve bacterial colonies. Our biosensors were meticulously fashioned using polyaniline (PANi) and hollow PANi (HPANi) nanofibers. Notably, the HPANi-based sensors, owing to their distinctive hollow structure, facilitated amplified responses under comparable experimental conditions compared with PANi-based counterparts. The designed sensing platform demonstrated exceptional accuracy in identifying Escherichia coli (gram -ve), showcasing a theoretical detection limit of 0.215 CFU/mL for PANi and a remarkably improved 0.14 CFU/mL for HPANi. These sensors displayed outstanding selectivity for gram -ve bacteria, even amidst gram +ve bacteria and fungi. Moreover, our platform demonstrated remarkable sensitivity, yielding 3.04 ((ΔR/R)/CFU/mL)/cm2 for the HPANi-based sensor, surpassing the performance of the PANi-based sensor at 1.98 ((ΔR/R)/CFU/mL)/cm2.

Alzheimer's Disease (AD) is the most common form of dementia associated with progressive loss of neuronal cells due to progressive accumulation of amyloid-beta (AB/Aβ) peptides in plaque form. Early diagnosis is the key to effective AD treatment, which can be carried out by detecting Aβ 1-40 (AB40) and Aβ 1-42 (AB42) potential biomarkers in easily accessible body fluids at sub pico gram per mL. With this aim, we have developed carboxylic functionalized multi-walled carbon nanotubes (fMWCNTs) embedded Polypyrrole (PPY) nanocomposite (PPY-fMWCNT) based highly sensitive and selective chemiresistive immunosensing platform that has the potential to detect multiple analytes simultaneously on the same substrate at sub femto gram per mL range. The binding event of antibody and antigen was transduced in terms of normalized resistance of bioelectrodes, measured through a four-probe probe station. By using PPY-fMWCNT as a bioelectrical transducer, the proposed sensing platform detected AB40 peptides (in real-time human plasma samples) in the linear detection range of 10 fg/mL to 10 ng/mL with a very low limit of detection (LoD) and a high sensitivity of 0.564 fg/mL and 55.67 ((ΔR/R0)/ng.mL-1)/cm2, respectively. The sensitivity of bare PPY (18.44 ((ΔR/R0)/ng.mL-1)/cm2) compared with fMWCNTs embedded PPY sensing platform enhanced 2.02 times without compromising in LoD. The analytical performance of the platform is further evaluated in terms of selectivity, repeatability and interference, posing its significance in the early detection of AD.

Traumatic Brain Injury (TBI), a major cause of mortality and neurological disability affecting people of all ages worldwide, remains a diagnostic and therapeutic challenge to date. Rapid, ultra-sensitive, selective, and wide-range detection of TBI biomarkers in easily accessible body fluids is an unmet clinical need. Considering this, in this work, we report the design and development of a facile, label-free, highly stable and sensitive, chemi-impedance-based sensing platform for rapid and wide range detection of Ubiquitin-carboxy terminal hydrolase L1 (UCHL1: FDA-approved TBI specific plasma biomarker), using carboxylic functionalized MWCNTs embedded polypyrrole (PPY) nanocomposites (PPY/f-MWCNT). The said nanocomposites were synthesized using chemical oxidative polymerization method. Herein, the functionalized MWCNTs are used as conducting fillers so as to increase the polymer's dielectric constant according to the micro-capacitor model, thereby augmenting both DC electrical conductivity and AC dielectric property of the nanocomposite. The proposed immunosensing platform comprises of PPY/f-MWCNT modified interdigitated microelectrode (IDμEs) array, on which anti-UCHL1-antibodies are immobilized using suitable covalent chemistry. The AC electrical characterization of the nanocomposite modified IDμEs, with and without the antibodies, was performed through generic capacitance vs. frequency (C–F, 1 KHz – 1 MHz) and capacitance vs. applied bias (C–V, 0.1 V–1 V) measurements, using an Agilent B1500A parametric analyzer. The binding event of UCHL1 peptides to anti-UCHL1-antibodies was transduced in terms of normalised changes in parallel capacitance, via the C–F analysis. Further, we have tested the detection efficiency of the said immunoassay against UCHL1 spiked human plasma samples in the concentration range 10 fg/mL – 1 μg/mL. The proposed sensing platform detected UCHL1 in spiked-plasma samples linearly in the range of 10 fg/mL – 1 ng/mL with a sensitivity and LoD of 4.22 ((ΔC/C0)/ng.mL−1)/cm2 and 0.363 fg/mL, respectively. Further, it showed excellent stability (30 weeks), repeatability, reproducibility, selectivity and interference-resistance. The proposed approach is label-free, and if desired, can be used in conjunction with DC measurements, for biosensing applications.

Label-free simultaneous detection of Alzheimer's disease (AD) specific biomarkers Aβ40 and Aβ42 peptides on a single platform using polypyrrole nanoparticle-based chemiresistive biosensors is reported here. The proposed interdigitated-microelectrode based inexpensive multisensor-platform can concurrently detect Aβ40 and Aβ42 in spiked-plasma in the range of 10-14 – 10-6 g/mL (with LoDs being 5.71 and 9.09 fg/mL, respectively), enabling the estimation of diagnostically significant Aβ42/Aβ40 ratio. A detailed study has been undertaken here to record the individual sensor responses against spiked-plasma samples with varying amounts and proportions of the two target peptides, towards enabling disease-progression monitoring using the Aβ-ratio. As compared to the existing cost-ineffective brain-imaging techniques such as PET and MRI, and the high-risk CSF based invasive AD biomarkers detecting procedures, the proposed approach offers a viable alternative for affordable point-of-care AD diagnostics, with possible usage in performance evaluation of therapeutic drugs. Towards point-of-care applications, the portable readout used in this work was conjugated with an android-based mobile app for data-acquisition and analysis.

Uncontrolled fibrous aggregation of proteins in the human brain implicates a range of anomalous biological phenomena, ultimately leading to Alzheimer's Disease (AD). Aggregates of β-Amyloid(1-42) (AB42) have been considered the most viable biomarker for early diagnosis of AD; therefore, it is highly essential to detect AB42 peptides in easily accessible body fluids, preferably at low concentrations. Considering this, we report the design and development of a facile, sensitive, and label-free electrochemical biosensor for AB42 peptide detection, using electrospun SnO2 nanofibers (SNF) as the transducing material. The sensing platform, comprising of AB42-specific capture antibodies covalently immobilized onto SNF nanofiber modified carbon working electrodes, acts as an immunoassay on to which the target analytes bind specifically. In response, the charge transfer resistance at the sensor interface gets modified proportionately and is recorded using electrochemical impedance spectroscopy. Herein, we have tested the efficiency of the said immunoassay against AB42 spiked buffer and plasma samples, in the concentration range 1 fg/mL–1 μg/mL. The proposed platform accounts for a sensitivity (limit of detection (LoD)) of 274.96(kΩ/ng.mL−1)/cm2 (0.146 fg/mL) and 302.05(kΩ/ng.mL−1)/cm2 (0.638 fg/mL) for AB42 spiked buffer and plasma samples, respectively. Furthermore, the proposed SNF-derived electrochemical immunoassay shows appreciable stability (over 126 days), selectivity, repeatability, reproducibility, and interference-resistance.

In the context of the recent pandemic, the necessity of inexpensive and easily accessible rapid-test kits is well understood and need not be stressed further. In light of this, we report a multi-nucleotide probe-based diagnosis of SARS-CoV-2 using a bioelectronics platform, comprising low-cost chemiresistive biochips, a portable electronic readout, and an Android application for data acquisition with machine-learning-based decision making. The platform performs the desired diagnosis from standard nasopharyngeal and/or oral swabs (both on extracted and non-extracted RNA samples) without amplifying the viral load. Being a reverse transcription polymerase chain reaction-free hybridization assay, the proposed approach offers inexpensive, fast (time-to-result: ≤ 30 min), and early diagnosis, as opposed to most of the existing SARS-CoV-2 diagnosis protocols recommended by the WHO. For the extracted RNA samples, the assay accounts for 87 and 95.2% test accuracies, using a heuristic approach and a machine-learning-based classification method, respectively. In case of the non-extracted RNA samples, 95.6% decision accuracy is achieved using the heuristic approach, with the machine-learning-based best-fit model producing 100% accuracy. Furthermore, the availability of the handheld readout and the Android application-based simple user interface facilitates easy accessibility and portable applications. Besides, by eliminating viral RNA extraction from samples as a pre-requisite for specific detection, the proposed approach presents itself as an ideal candidate for point-of-care SARS-CoV-2 diagnosis.

This chapter reports a short review on electrochemical nanoengineered biosensors in infectious disease diagnosis. Early and timely diagnosis of infectious diseases has tremendous medical and social significance which advocates the development of new diagnostic tools. In this chapter, we discussed various electrochemical sensors for detection and diagnosis of tropical or subtropical fevers particularly dengue fever and malaria parasite. We also addressed the several important aspects of biosensors, namely, selectivity, sensitivity, and interference, and also the effect of engineering the nanomaterials (0D, 1D, 2D) on these aspects. In detail, we discussed the various techniques to immobilize the biomolecules on working electrode (glassy carbon, gold electrode, flexible substrates). Further, we discussed the several miniaturized sensing platforms with integrated microfluidic channels which can ensure for development of sensors for point-of-care applications.

In this study we have reported the design and development of a facile, sensitive, selective, and label-free electrochemical sensing platform for the detection of atrazine based on MWCNT-embedded ZnO nanofibers. Electrospun nanofibers were characterized using scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscope (XPS), UV-Visible spectroscope (UV-VIS), and Fourier-transform infrared spectroscope (FTIR). Electrochemical properties of MWCNT-ZnO nanofiber-modified electrodes were assessed using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Binding event of atrazine to anti-atrazine antibody, which immobilized on nanofiber-modified electrode via EDC and NHS chemistry, was transduced with EIS. Due to high conductivity, surface area, and low bandgap of MWCNT-ZnO nanofibers, we have achieved the sensitivity and limit of detection (LoD) of sensor as 21.61 (KΩ μg−1 mL−1) cm−2 and 5.368 zM for a wide detection range of 10 zM–1 µM. The proposed immunosensing platform has good stability, selectivity, repeatability, and reproducibility, and are less prone to interference.

This chapter is a short review of the current research in noninvasive sweat and gas sensors for healthcare applications. Recent studies on the detection of various sweat biomarkers such as glucose, lactose, and metal ions by using wearable sensors have been explored. Respiration sensing using humidity in breath to monitor sleep disorders, cardiovascular, and pulmonary diseases is discussed. Selective volatile organic compound sensors and arrays of sensors to detect specific diseases and discrimination of multiple diseases are reviewed. Finally, the use of ingestible sensors, an emerging and convenient technology, to diagnose various diseases using gas profiles in the gut is described. In conclusion, we reflect on the challenges and future scope of these diagnosing techniques.

This paper reports label free sub-femtomolar detection of cardiac biomarker troponin T using ZnSnO 3 perovskite nanomaterial based electrochemical biosensors. Herein, we have demonstrated two separate synthesis schemes for the perovskite material, one being a hydrothermal synthesis method, and the other being electrochemical deposition. In this work, for the electrochemical detection of troponin T with the hydrothermally synthesized ZnSnO 3 , we have used a glassy carbon electrode (GCE), on to which the nanomaterials are dropcasted so as to create a heterogeneous working electrode. In a separate scheme, we have used indium tin oxide coated polyethylene terephthalate (ITO/PET) substrates as the working electrodes, on to which a thin film of ZnSnO 3 nanomaterial has been deposited electrochemically. Subsequently, the capture antibodies corresponding to the targeted cardiac troponin T are immobilized on to the surface functionalized working electrodes using NHS/EDC chemistry. The electrochemical detection of the target analyte has been performed in the concentration range of 1 fg mL -1 to 1 μg mL -1 , using electrochemical impedance spectroscopy. For the GCE and ITO/PET based bioelectrodes, the approximated limits of detection are found to be 0.187 fg mL -1 and 0.571 fg mL -1 respectively, whereas the respective values of sensitivity of the same are 35.25 kΩ (μg mL -1 ) -1 cm -2 and 8.813 kΩ (μg mL -1 ) -1 cm -2 .

Atrazine, a pesticide of chloro-triazine family and a class 3a carcinogen, is known to severely affect human endocrine system. In light of its detrimental environmental effects, ensuring its easy and ultrasensitive detection is highly essential. In this paper, we demonstrate facile and label free electrochemical detection of atrazine, using electrospun manganese oxide nanofibers (MNF). Here, we report an anti-atrazine-antibody based immunosensor, based on low bandgap Mn2O3 nanofibers, for ultrasensitive and highly selective detection of atrazine. The electrospun nanofibers used in this work were characterized using field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD) analysis, X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and UV–vis Spectroscopy. The proposed platform was able to detect atrazine in the concentration range of 10−21 g/mL, which in comparison to the previously reported atrazine sensors is a fairly superior performance. The observed lower limit of detection (LOD) was 0.22 × 10−21 g/mL, with a sensitivity of 52.54 (kΩ/μg mL−1)/cm2. We have also demonstrated atrazine detection in spiked water samples, so as to demonstrate that the proposed platform is suitable for real-time applications. Furthermore, the sensing platform is label free, comprising of a simple protocol and hence is facile in nature.

Atrazine, a class 3a carcinogen, is a pesticide of chloro triazine family and is known to severely affect the human endocrine system upon consumption. The toxic effects of atrazine cause damage not only to the humans but also to animals and plants. In lieu of the detrimental effects of atrazine on environment, it is essential to develop a sensor platform capable of its detection in water. Here, we propose ultrasensitive electrochemical detection of atrazine using electrospun SnO2 nanofibers. In this study, the nanofibers have been characterized using Field Emission Spectroscopy, X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS), UV-Vis-NIR spectroscopy and Fourier transform infrared spectroscopy (FTIR). Using a label-free transduction, we have detected atrazine in fairly low concentrations, with the limit of detection being 0.9 zM and the sensitivity being 4.11 (μA/μM)/cm2, in a wide dynamic detection range varying from 1 zM to 1 μM. Furthermore, we have reported atrazine detection in trace levels in spiked real time water samples, which is an essential step in ensuring that the sensing platform can be deployed for practical applications. In addition to this, the sensor exhibits excellent selectivity, reasonable stability (when stored at 4 °C), and good interference-resistance.

This paper demonstrates a simple, label-free detection methodology for detecting single point DNA mutations. Single point mutation detection is a key enabler for diagnosis and prevention of several genetic disorders that manifest into cancers. Specifically for this purpose, herein, an electrochemical biosensor utilizing electrospun graphene doped manganese III oxide nanofibers (GMnO) is developed. The charge transfer resistance offered by GMnO is extremely sensitive to the localized change in the conductivity. This sensitivity, attributed to the low band gap of Mn2O3 and high charge transfer kinetics of graphene, is explored in the proposed mutation detection platform. As a proof of concept, ultrasensitive detection of BRCA1 gene specific point mutation is demonstrated. The target specific single stranded probe DNA is immobilized onto GMnO modified glassy carbon working electrodes via chemisorption. Post target-DNA hybridization, differential pulse voltammetry is employed to facilitate detection of targeted point mutation, wherein, difference in peak currents is used to distinguish the target DNA as normal or mutant. Efficiency of the proposed method is evaluated against a target concentration ranging from 10 pM−1 μM. With respect to the mutated target DNA, the LoD of the proposed device is found to be 0.8±0.069 pM. The proposed approach can be extended for detecting any mutation/hybridization of interest by simply adapting an appropriate functionalization protocol.