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This study explores the utilization of the organic conductive molecule Polypyrrole (PPy) for the modification of Indium Gallium Zinc Oxide (IGZO) nanoparticles, aiming to develop highly sensitive ozone sensors. Pyrrole (Py) molecules undergo polymerization, resulting in the formation of extended chains of PPy that graft onto the surface of IGZO nanoparticles. This interaction effectively diminishes oxygen vacancies on the IGZO surface, thereby promoting the crystallization of the IGZO (1114) facets. The resultant structure exhibits promising potential for achieving high-performance wideband semiconductor gas sensors. The IGZO/PPy device forms a Straddling Gap heterojunction, facilitating enhanced electron transfer between IGZO and ozone molecules. Notably, the adsorption and desorption of ozone gas occur efficiently at a low temperature of approximately 25 °C, obviating the need for additional energy typically associated with wide bandgap semiconductor materials. Density Functional Theory (DFT) calculations attribute this efficiency to the enhanced number of active sites for ozone adsorption, facilitated by hydrogen bonds. The substantial conductivity of PPy, combined with its planar ring structure, induces positively charged polarization on the IGZO side upon ozone adsorption. The resultant device exhibits exceptional sensitivity, boasting a 4-fold improvement compared to sensors reliant solely on IGZO. Additionally, the response time is significantly reduced by a factor of 10, underscoring the practical viability and enhanced performance of the IGZO/PPy sensor field.

The performance of solar photovoltaic (SPV) systems is significantly affected by faults in the SPV arrays. Changes in environmental conditions and nonlinear characteristics of solar cells make it challenging to identify faults by conventional protection schemes. Existing learning-based fault diagnosis approaches require voluminous data and sensors, making them costly and challenging to integrate with existing SPV systems. Moreover, the lack of panel-level monitoring in existing approaches renders them incapable of pinpointing the precise location of the faulty panel. To address these issues, this article proposes a methodology for detecting faults, localizing the exact faulty panel, and accurately classifying faults in SPV arrays. The methodology involves a sensorless electronic circuit using a bipolar junction transistor (BJT) and Zener diode for fault detection, an Internet of things (IoT)-based web application for monitoring individual solar panels for fault localization, and a deep autoencoder (DAE)-based semi-supervised learning module followed by a hybrid support vector machine and logistic regression (SVM-LR) for fault classification. The proposed methodology is a low-cost and less data-intensive solution that results in precise fault detection, exact faulty panel localization, and fault classification with an overall accuracy of 99.67%. The methodology is validated in a laboratory-scale real-time setup of a grid-connected SPV system under varying electrical and environmental conditions.

The use of hydrogels as scaffolds for three-dimensional (3D) cell growth is an active area of research in tissue engineering. Herein, we report the self-assembly of an ultrashort peptide, a tetrapeptide, Asp–Leu–IIe–IIe, the shortest peptide sequence from a highly fibrillogenic protein TDP-43, into the hydrogel. The hydrogel was mechanically strong and highly stable, with storage modulus values in MPa ranges. The hydrogel supported the proliferation and successful differentiation of bone marrow-derived mesenchymal stem cells (BMSCs) in its matrix as assessed by cell viability, calcium deposition, alkaline phosphatase (ALP) activity, and the expression of osteogenic marker gene studies. To check whether the hydrogel supports 3D growth and regeneration in in vivoconditions, a rabbit critical bone defect model was used. Micro-computed tomography (CT) and X-ray analysis demonstrated the formation of mineralized neobone in the defect areas, with significantly higher bone mineralization and relative bone densities in animals treated with the peptide hydrogel compared to nontreated and matrigel treatment groups. The ultrashort peptide-based hydrogel developed in this work holds great potential for its further development as tissue regeneration and/or engineering scaffolds.

The power system landscape is presently undergoing a dramatic change with the rapid integration of numerous distributed energy resources (DERs) at the grid edge. Broadly, we consider grid-edge DERs as any electricity source, storage, and demand response program connected to the medium-to-low-voltage distribution system.

Air pollution caused by various anthropogenic activities and biomass burning continues to be a major problem in India. To assess the effectiveness of current air pollution mitigation measures, we used a 3D global chemical transport model to analyze the projected optical depth of carbonaceous aerosol (AOD) in India under representative concentration pathways (RCP) 4.5 and 8.5 over the period 2000–2100. Our results show a decrease in future emissions, leading to a decrease in modeled AOD under both RCPs after 2030. The RCP4.5 scenario shows a 48%–65% decrease in AOD by the end of the century, with the Indo‐Gangetic Plain (IGP) experiencing a maximum change of ∼${\sim} $25% by 2030 compared to 2010. Conversely, RCP8.5 showed an increase in AOD of ∼${\sim} $29% by 2050 and did not indicate a significant decrease by the end of the century. Our study also highlights that it is likely to take three decades for current policies to be effective for regions heavily polluted by exposure to carbonaceous aerosols, such as the IGP and eastern India. We emphasize the importance of assessing the effectiveness of current policies and highlight the need for continued efforts to address the problem of air pollution from carbonaceous aerosols, both from anthropogenic sources and biomass burning, in India. Air pollution from human activities and biomass burning is a significant issue in India. To understand the efficacy of current efforts, a computer model is used to study the projected levels of carbonaceous aerosols (measured as optical depth) from 2000 to 2100. The results suggest that emissions are expected to decrease after 2030, leading to a drop in modeled aerosol levels. In an optimistic scenario of RCP4.5, aerosol levels could decrease by 48%–65% by the end of the century, with the Indo‐Gangetic Plain (IGP) showing the most improvement by 2030. However, in a scenario without any significant measures, aerosol levels may increase by 29% by 2050 and not improve significantly by the end of the century. The study indicates that it might take around 30 years for current pollution control measures to make a noticeable difference in heavily polluted regions like the IGP and eastern India. These findings underscore the importance of evaluating the effectiveness of current policies and the need to address air pollution in India caused by carbonaceous aerosols emitted from both, human activities and biomass burning. Results presented here are crucial for policymakers and the public to understand the progress made and the challenges that persist in combating air pollution over Indian region. The future changes in carbonaceous aerosols carry significant consequences for air quality together with climate change in IndiaMitigation of carbonaceous aerosols is essential to maximize the co‐benefits for future air qualityFuture shifts in emissions will affect the degree of mitigation needed for anthropogenic sources over India after the year 2030 The future changes in carbonaceous aerosols carry significant consequences for air quality together with climate change in India Mitigation of carbonaceous aerosols is essential to maximize the co‐benefits for future air quality Future shifts in emissions will affect the degree of mitigation needed for anthropogenic sources over India after the year 2030

This paper presents the surface modification and characterization of h-BN-doped PVP thin film and its use as a humidity sensing probe. The thin film of h-BN-doped PVP has been prepared by spin coating method and characterized by XRD, SEM, TEM, UV–Vis, and FTIR spectroscopy. SEM and TEM images indicate that the PVP is densely packed with h-BN. The bandgap calculated by UV–visible spectroscopy varies from 4.9 to 5.2 eV on increasing the doping level. .The crystalline nature of the blend has been observed through X-ray diffraction spectrum with minimum crystallite size of 2 nm. The h-BN-doped PVP thin film was employed as a sensor with a sensitivity of 98.75 kΩ/%RH and 4.67 pF/%RH for impedance and capacitance, respectively. The minimum response/recovery time of the sensor (14/18 s) indicates that the blend can be used as a cost-efficient humidity sensor. Density functional theory (DFT) has been applied to simulate the adsorption of water molecules on the h-BN-doped PVP nanocomposite. The dipole moment, adsorption energy, and HOMO–LUMO gaps have been evaluated with increasing the number of water molecules. PDOS plots show alteration in the makeup of HOMO and LUMO on the adsorption of water molecules. MESP map illustrates the transfer of charge density from h-BN to the water molecules.

The present study examines the influence and effectiveness of sales representative in an emerging market in enhancing retail sale in cases of a high involvement product like apparel and low involvement product like grocery. The present research examined the effect of five factors, namely, sales representative's personal characteristics, competence, sales representative's-initiated promotion, involvement and ethical behaviour on purchase behaviour of consumers. The step-wise regression method was used to support the proposed hypotheses. The results of the study concluded that in both high and low involvement product category, sales representative's-initiated promotion and involvement affect the purchase behaviour of consumers. Other than these two factors, the competence of the salesperson plays an important role in high involvement products, whereas his personal characteristics are important in case of low involvement products. The research provides a guide for sales managers for developing a training and strategy development program for their sales force.

The present paper describes the effect of co-catalyst on the growth of multiwall carbon nanotube (MWCNT) by chemical vapor deposition (CVD) technique. The fascinating properties of CNT make them a suitable material for optoelectronic devices such as sensors, LED, solar cell, and field emission displays. MWCNTs were fabricated using CVD, by decomposing ethanol over finely dispersed Co metal as a catalyst at 750 °C. The effects of growth condition on the quality and morphology of MWCNTs were investigated by SEM, FTIR and XRD. SEM photographs show that the nanotubes are densely packed having a diameter of 10–15 nm. The bandgap was calculated by UV–visible spectroscopy and it was found varying from 3.08 to 3.5 eV by changing the substrates. The average size of tubes (length) was found to be 250 nm. FTIR exhibited that the synthesized MWCNTs were semiconducting in nature with the oxygen vacancies causing the variations in refractive index with the exposure of moisture.

The Laser Interferometer Space Antenna (LISA) has two scientific objectives of cosmological focus: to probe the expansion rate of the universe, and to understand stochastic gravitational-wave backgrounds and their implications for early universe and particle physics, from the MeV to the Planck scale. However, the range of potential cosmological applications of gravitational-wave observations extends well beyond these two objectives. This publication presents a summary of the state of the art in LISA cosmology, theory and methods, and identifies new opportunities to use gravitational-wave observations by LISA to probe the universe.

Reference evapotranspiration (ET0) is a crucial parameter in hydrology that is used to estimate the amount of water lost through evaporation and transpiration from a standard reference crop under specified climatic conditions. ET0is calculated based on several meteorological variables such as temperature, humidity, wind speed, and solar radiation. However, in many regions, there may be limited availability of these meteorological data, making it difficult to estimate ET0using conventional methods. This study aimed at using machine learning (ML) techniques to estimate ET0with minimal climatic inputs, using the Food and Agriculture Organization (FAO)-56 Penman–Monteith model as the standard reference method. Different ML models, including Long Short-Term Memory (LSTM) neural networks, Gradient Boosting Regressor (GBR), Random Forest (RF), and Support Vector Regression (SVR), were developed with climatic variables as input parameters. To summarize, the study evaluated different machine learning models to estimate ET0with minimal climatic inputs and compared their performance with the standard Penman–Monteith model for Hawalbagh experimental farm observatory. The results showed that the LSTM model performed better than other ML models, followed by SVR and RF, in estimating ET0with minimal climate data. The study concluded that LSTM and SVR models are the most robust ML models for estimating ET0in such scenarios. The study found that even with limited climatic data, such as a two-parameter combination (maximum temperature and relative humidity, relative humidity and sunshine hours) or a three-parameter combination (minimum temperature, relative humidity, and precipitation, minimum temperature, maximum temperature, and precipitation), promising results in ET0estimation can be achieved. The study's findings are significant for estimating ET0in data-scarce regions, especially for agricultural water management in semi-arid climates.

In this paper various shapes of DR antennas excited by common feed have been proposed and successfully implemented for wideband applications. Proposed structures are Hemispherical, Arrow-shaped and Triangular DRA, while common excited feed is inverted trapezoidal conformal patch. These shapes of DR offer significant optimization in several parameters such as impedance bandwidth, peak gain and bandwidth per unit volume of the antenna. By using inverted trapezoidal patch feed mechanism an impedance bandwidth (VSWR<2) of about 63 % for hemispherical shape, 66 % for arrow shape, and 72 % for triangular shape DRA has been achieved with maximum bandwidth per unit volume. Proposed wideband DRAs i. e. triangular, hemispherical, and arrow shapes of DR antennas cover almost complete C-band (4 GHz–8 GHz) frequency spectrum of microwave. The average peak gain within the operating band for hemispherical, arrow, and triangular shape DRA are about 5, 5.4, and 5.5 dB respectively. A comparative analysis of proposed structures for various antenna parameters has been analyzed by HFSS (High-Frequency Structure Simulator) and validated by experimental results.

In this work, we use a chemical method to design novel 2D-material/0D-quantum dot (MoS2/SnS) heterostructures. Furthermore, the unique 2D/0D heterostructure enhanced the NO2gas-sensing capability 3 times and increased the sensing recoverability by more than 90%. Advanced characterization tools such as SEM, TEM, XRD, and AFM confirm the formation of MoS2/SnS heterojunction nanomaterials. Using AFM data, the average thickness of the MoS2layer was found to be 5 nm. The highest sensor response of 0.33 with good repeatability was observed at 250 ppb of NO2. Sensing characterization reveals the ultra-fast response time, that is, 74 s, at 50 ppb of NO2. The limit of detection for detecting NO2was also found to be very low, that is, 0.54 ppb, by using MoS2/SnS heterostructures. The theoretical calculations based on density functional theory well corroborated and quantified the intermolecular interaction and gas adsorption on the surface of MoS2/SnS.

Abstract: Agriculture in hill and mountain ecosystems is predominantly rainfed with common occurrence of moisture stress. It is a natural disaster which evolves in time and its impacts last for a long time. In the present investigation, long-term monthly precipitation data for 40 years (1980–2019) were used for characterizing agricultural drought in Almora and Nainital districts of Uttarakhand in India. Different drought indices based on meteorological data like standard precipitation index (SPI), percentage of departure (Pd) and percent of normal (Pn) were used. Percentage of departure is calculated from deviation of monthly precipitation from the long-term average monthly precipitation. Percent of normal is calculated by dividing the precipitation by normal precipitation for time being considered. SPI values were calculated based on gamma distribution of long-term monthly precipitation data. The Pearson’s correlation coefficient between monthly percentage of departure and different SPI time scales (1, 3 and 6 months) were analyzed. SPI-1 (July and August) for both the stations showed very strong correlation with the corresponding monthly percentage of departure (r> 0.97) than SPI-3 and SPI-6. Therefore, it is suggested that SPI as a stand-alone indicator should not be interpreted to identify drought in a hilly region. Research highlights: Meteorological drought indices have been used to identify agricultural drought. SPI-1 showed very strong correlation with percentage of departure. Meteorological based SPI was well correlated with satellite based drought indices. Study suggest to use multiple drought indices for drought Identification.

In this work, synthesis of TiO2nanorods using wet chemical method alongwith their photovoltaic and humidity sensing applications are reported. After successful synthesis of TiO2nanorods, the material was investigated through X-ray diffraction (XRD), Scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR) and UV-Vis spectroscopy. Surface morphology and topology of the synthesized nano material as analyzed by SEM showed porous and tubular structure. EDS confirmed the elemental composition. XRD revealed the rutile phase with minimum crystallite size as 78.60 nm. The optical characterization of nano material has been completed by using FTIR and UV-Vis spectroscopy to confirm the required bond formation and estimate the bandgap (5.86 eV) respectively. Brunauer–Emmett–Teller (BET) characterization exhibited the highly porous nature of the material revealing the surface area of 37.445 m2/g together with mean pore diameter ranging from 9 to 18 nm. Further, the material was employed as photovoltaic cell and humidity sensor. The photovoltaic efficiency was obtained as ∼0.3909 % whereas sensitivity towards humidity as 2.93 pF/%RH and 0.42 MΩ/%RH for capacitance and impedance respectively at room temperature.