Gupta, Ankur

We report significantly enhanced sensitivity of AlGaN/GaN-based high electron mobility transistor (HEMT) sensor by the targeted synthesis of IT and 2H coexisting phase MoS2 and applying the gate bias voltage. The HEMT structures on Si (111) substrates were used for the detection of Hg2+ ions. The optimum sensitive regime in terms of V GS and V DS of the sensor was investigated by keeping the drain source voltage V DS constant at 2 V and by only varying the gate bias voltage V GS from 0 to 3 V. The strongest sensing response obtained from the device was around 0.547 mA ppb-1 at V GS = 3 V, which is 63.7% higher in comparison to the response achieved at 0 V which shows a sensing response of around 0.334 mA ppb-1. The current response depicts that the fabricated device is very sensitive and selective towards Hg2+ ions. Moreover, the detection limit of our sensor at 3 V was calculated around 6.21 ppt, which attributes to the strong field created between the gate electrode and the HEMT channel due to the presence of 1T metallic phase in synthesized MoS2, indicating that the lower detection limits are achievable in adequate strong fields.

The advancement of clean and sustainable energy technologies is necessary to fulfill the world's growing energy demand. Food preservation must consider the usage of PV-assisted solar dryers to eliminate the traditional energy-intensive drying systems. However, there is a significant technology gap in the drying system to effectively utilize solar energy. Some modifications can be done through the absorber surface by adding obstacles for the selection of an effective design that increases the heat transmission to the fluid flowing inside the solar collector and dryer. The primary objective of this research was to improve the PV-assisted solar dryer performance by modifying the absorber through the incorporation of square obstacles equipped with threaded pin fins. The investigations were carried out in plain absorber (Model-I) and modified absorber (Model-II) to compare the performance at 0.0024, 0.0072, and 0.012 kg/s airflow rates. The reduction in moisture content of green papaya and green banana was 71.42 % and 49.27 % more using Model-II instead of drying outside the dryer. The maximum drying efficiency was 29.55 % and 29.80 % in Model-II for crops A and B, respectively at 0.0072 kg/s. The thermal energy and thermal exergy efficiency were 33.43 % and 27.17 % higher, respectively in Model-II than compared to Model-I. The lower payback of 1.51 yr and higher CO2 mitigation of 46.17 tons were attained. The quality analysis findings recommended that the proposed design can be applied in numerous food drying applications and is a promising solution for further advancing absorber design for solar collectors and dryers.

The ability to drill smaller size holes down to micron level is a challenging task in many industries that manufacture high-tech products. Due to restriction in the tool size existing in conventional manufacturing, nonconventional approaches are being adopted to meet the challenges related to miniaturization. Laser sources have emerged as a clean source of energy for microfabrication techniques. In this work, micron level holes in the polyethylene terephthalate (PET) substrate are fabricated with fiber laser machining arrangement. Design of experiments (DOE) was used to perform the experiments giving equal weightage to all the input variables and the results were analysed through analysis of variance (ANOVA) to develop the optimization model. Experiments have been performed while taking power, frequency, pulse on time and cycle time as process parameters and hole diameter and heat affected zone (HAZ) as output parameters. Further, a mathematical model has also been developed and simulated for the hole fabrication through laser as the energy input. The experiment conducted using the optimized parameters resulted in hole diameter of 457 μm and HAZ thickness of 284 μm.

Among various explored nanostructured materials, zinc oxide (ZnO) nanomaterials (NMs), exhibit extraordinary physical and chemical properties and have an advantage among other variety of NMs due to the biocompatibility, cost-effectiveness, and low toxicity. It has emerged out as one of the most researched NMs for variety of applications including biomedical domain in the last few decades. In biomedicine, particularly in antifungal and antibacterial fields, this NM has demonstrated a promising potential in the biological applications. Given the enormous potential of the ZnO NMs, the state-of-the art and most important advances in this field are discussed in this chapter.

In this article, we fabricate a nature inspired Ficus religiosa leaf mesh with a micro/nano hierarchical structure utilizing the ZnO/PANi@PMMA nanocomposite having exceptional hydrophobic and oleophilic characteristics, with a water contact angle of ∼130° and an oil contact angle of ∼2°, respectively. Different oil/water mixtures were subsequently separated using the mesh, and after 8 cycles, the separation efficiency was ∼96.86 ± 0.8% for gasoline. In contrast, the reported mesh carries antibacterial properties and has successfully degraded the methylene orange (MO) dye solution in 140 min with ∼91% efficiency in the presence of sunlight, envisioning its potential use in water purification applications.

Nanocomposite coating has a substantial impact on thermomechanical characteristics. There are several methods for developing nanocomposite coatings; however, electrodeposition is one of the additive manufacturing approaches that has been shown to be suitable due to its adjustable parameters, cost-effective and ease of process. Copper composite coatings have been extensively utilized in the aerospace and automotive industries due to their superior mechanical, electrical, and thermal properties. In the proposed study, a "In-house pulse electrodeposition setup" was developed and used to effectively deposit Cu/SiC nanocomposite coatings from an aqueous sulphate solution. Scanning electron microscopy (SEM) and x-ray diffractometer (XRD) are used to examine the surface morphology, which confirmed the successful co-deposition. The microhardness of the Cu/SiC composite coatings and effect of process parameters is predicted using response surface methodology (RSM). Artificial neural network-particle swarm optimization (ANN-PSO) is used to train, test, and validate experimental data and to investigate the correlation between electrodeposition process parameters and their effects on microhardness. The process was operated at: pulse frequency (10 Hz-100 Hz), duty cycle (20-80%), bath agitation (200-500 rpm), and SiC concentration (1-5 g/L). The determination coefficient (R2), root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE) is used to validate several ANN-PSO models. The optimal morphology with the highest microhardness is produced under the following conditions: 10 Hz pulse frequency, 50% duty cycle, 350 rpm bath agitation, and 5 g/L SiC concentration. The findings showed that the suggested model is an appropriate, flexible, and reliable way for estimating the microhardness of Cu/SiC nanocomposite coatings.

This work presents the fabrication of candle-soot (CS) nanofibers through electrospinning and demonstrates their use as high-performance electrodes for supercapacitors. The electrospinning is facilitated by dispersing CS nanoparticles within a precursor polymer solution, which is carbonized to obtain free-standing CS/carbon nanofiber electrodes. The electrodes exhibit superior electrochemical performance when compared to pristine carbon nanofibers. The fabricated supercapacitor yields a maximum capacitance of 433.5 F/g at 0.5 mA/cm2 current density, with a maximum energy density of 60.2 Wh/kg, at a power density of 283.1 W/kg, which is also significantly superior compared to reported carbon nanofiber-based supercapacitors.

This study explores a smartphone-based spot detection framework for glucose in a rapid, simple, and affordable paper-based analytical device (PAD), which employs machine-learning algorithms to estimate various glucose concentrations. Herein, two different detection mixtures were chosen with chitosan (C) and without chitosan (WC) for the color change analysis. Being a biopolymer, chitosan improves the analytical performance of PADs when used with a chromogenic agent. Moreover, the influence of the illumination conditions and camera optics on the professed color of glucose strips was observed by choosing various illumination conditions and different smartphones. Hence, this study focuses on developing a framework for smartphone-based simple and user-friendly spot-based glucose detection (with a concentration range of 10-40 mM) at any illumination conditions and in any direction of illumination. Additionally, the combination of color spaces and machine-learning algorithms was applied for the signal enhancement. It was observed that the machine learning classifiers, cubic support vector machine (SVM) and narrow neutral network show higher accuracy for the WC samples, which are 92.7 and 92.3%, respectively. The samples with chitosan show higher accuracy for the linear discriminant and quadratic SVM classifiers, which are 94.1 and 93.9%, respectively. Simultaneously, cubic SVM shows ∼93% accuracy for both cases. In order to assess the performance of the devices, a blind test was also conducted. This study demonstrates the potential of the developed system for initial disease screening at the user end. By incorporating machine learning techniques, the platform can provide reliable and accurate results, thus paving the way for estimating the accuracy of the results for improved initial healthcare screening and diagnosis of any disease.

This research objective is to experimentally evaluate the performance of a photovoltaic-thermal indirect mode solar dryer for drying neem leaves under variable weather conditions of a year (winter and summer for sunny and cloudy days). The neem leaves moisture content findings indicated that the values decreased to 0.07 (d.b.) from 4.56 (d.b.) in 375 min for sunny winter, 465 min for cloudy winter, 315 min for sunny summer, and 405 min for cloudy summer weather conditions. The maximum drying parameters values were determined to be 6.15 kWh/kg for specific energy consumption, 0.23 kg/kWh for specific moisture extraction rate, and 18.54 % for drying efficiency in cloudy winter weather conditions. The maximum thermal efficiency of 29.17 % was achieved in sunny summer and the electrical efficiency was 10.14 % in cloudy winter. The maximum heat transfer and mass transfer coefficients and Sherwood number were determined to be in sunny summer with ranges of 0.34–21.54 W/m2K, 0.35–22.18 × 10−3 m/s, and 0.50–30.47, respectively. The findings of quality parameters reveal that the drying product quality was not much affected by measuring total color change, antioxidant activity, total flavonoid content, and total phenolic content. In sunny summer, an ideal working environment was obtained after taking into account all performance characteristics.

This paper studies the various designs of paper-based device to optimize the flow rate for the purpose of multiplexed detection of biomarkers. In this study, four different trident-shaped designs were investigated based on sample volume and flow time optimization. The selection of optimized design was performed based on minimum time for the flow of minimum sample volume. The optimization results show that the most optimal flow time was 20s with an optimized sample volume of 4μl, which was demonstrated by the channel design tapered at an angle of 20°. Further, theoretical analysis was also performed with COMSOL-based simulation analysis to investigate the optimized design module for the trident-shaped microchannel geometry over the paper substrate.