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 (ID?Es) 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 ID?Es' 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

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 (SnO) 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 (?A/(ng/mL))/cm and 285.94 (k?/(ng/mL))/cm obtained from DPV and EIS, respectively. Further, the proposed sensing platform showed excellent selectivity, repeatability, reproducibility and high interference resistance.

Traumatic Brain Injury (TBI) is a physical damage to the brain and a significant cause of mortality and morbidity affecting all ages worldwide, remaining as a diagnostic and therapeutic challenge to date. The design and development of rapid, low-cost, highly accurate, and long-term stable point-of-care TBI diagnostic test kits is an unmet clinical need. In light of this, here we report a novel multianalyte chemicapacitive immunosensing platform that can detect FDA-approved Glial Fibrillary Acidic Protein (GFAP) biomarkers in real-time human plasma samples using carboxylic functionalized MWCNTs (fMWCNTs) embedded Polypyrrole (PPY) as a bioelectrical transducer. Herein, the low-cost GFAP bioelectrodes were prepared through covalent immobilization of anti-GFAP-antibodies on PPY-fMWCNTs modified array of interdigitated microelectrodes (ID?Es, fabricated on low-cost single-side copper clad PCB substrates). The binding event of GFAP peptides with anti-GFAP-antibodies in real-time human plasma samples was captured in terms of AC capacitance measured through C-F analysis (using an Agilent B1500A parametric analyzer) and quantified in terms of normalized change in capacitance of GFAP bioelectrodes with and without exposure of target GFAP peptides spiked in real-time human plasma samples (10 fg/mL – 1 ?g/mL). The proposed PPY-fMWCNTs nanocomposite-based chemicapacitive immunosensing platform effectively detected GFAP target analytes in linear detection range 10 fg/mL – 10 ng/mL with a sensitivity and LoD of 3.9743 ((?C/C0)/ng.mL?1)/cm2 and 0.3854 fg/mL, respectively. Further, it also showed superior performance in terms of selectivity, reproducibility, long-term stability (30 weeks) and interference resistance. The proposed AC-capacitive approach is facile, label-free and can be combined with DC-resistive measurements to improve the diversity of decision-making parameters that inherently aid in improving the diagnostic accuracy of TBI test kit

An innovative breakthrough that addresses the shortcomings of FinFET is the use of tree-shaped Nanosheet FET. This study examines the temperature dependence of the performance of 12 nm Tree-shaped NSFET on DC and analog/RF properties using a gate stack of high-k HfO2 and SiO2. From 200 K to 350 K, a detailed DC performance analysis was performed, including the transfer characteristics (ID vs VGS), output characteristics (ID vs VDS), subthreshold swing (SS), and ION/IOFF ratio. Additionally, we examined how temperature influence power consumption, dynamic power, and the ON-OFF performance metric (Q). Having the off current lesser than nA at all the temperatures, the proposed device shows good ION/IOFF switching performance. At an LG of 12 nm, the cutoff frequency (fT) is found to be in the Tera Hz region, and the Q varies from 0.9 to 5.1 ?S-dec/mV at temperatures between 200 K and 350 K. Additionally, the impact of IB height (HIB) is investigated at 15–25 nm with the step of 5nm and the impact of IB width (WIB) is investigated at 3 - 5 nm on Tree-shaped NSFET and the impact of variation in the work function is also done in this paper. The effect of scaling with different gate lengths from 20 nm down to 10 nm and its DC characteristics are examined in this paper. The power consumption of the Tree-shaped NSFET increases with temperature. From all these results, the proposed Tree-shaped NSFET shows great potential as a high-frequency competitor at the nanoscale.

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.

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 ((?R/R)/CFU/mL)/cm2 for the HPANi-based sensor, surpassing the performance of the PANi-based sensor at 1.98 ((?R/R)/CFU/mL)/cm2.