Efficient energy storage is becoming a serious niche area nowadays due to exponential growth in energy consumption. Different approaches have been developed and implemented to improve the performance of the devices, in which improving conductivity is a major issue. In the present work, cotton fabric was converted into a conductive material by incorporating graphene, using the Layer-by-Layer (LBL) method, followed by heating at 100 °C. The electrical conductivity of the cotton using different concentrations of graphene was studied. The graphene-coated cotton, at the 17th layer, with a concentration of 168.36 wt.% resulted in a surface resistance of 0.644 Ω/sq and retained the maximum resistance even after two months. Scanning electron microscopy (SEM) and Energy-dispersive X-ray spectroscopy analysis (EDX) were employed to comprehend the surface morphology and elemental compositions. Fourier transform infrared (FTIR) spectroscopy, UV-vis absorption, and X-ray diffraction (XRD) were used to determine the structural analysis, which revealed a good dispersion of graphene in the cotton samples obtained through dimethyl sulfoxide (DMSO) doping, which reduced the ripple of the cotton. The cotton fabric treated with graphene was thermally stable, as shown through thermal analysis. From the results obtained, it is evident that graphene-treated cotton fabric materials show tremendous potential for use in smart textiles and also as protective clothing.

Artificial light pollution, an escalating environmental issue, significantly disrupts natural nighttime light conditions. This research investigates the impact of artificial light at night (ALAN) on Solanum melongena L., focusing on photosynthesis, biochemical profiles, and ultrastructural changes. The study aims to elucidate how continuous light exposure at night affects plant physiology and metabolic processes. Plants were grown under two light conditions: natural sunlight followed by complete darkness (S1 plants, control) and ALAN in addition to natural sunlight (S2 plants), with key parameters measured, including growth metrics, photosynthetic rates, biochemical profiles, and ultrastructural changes assessed via microscopy. Results indicated that ALAN exposure led to increased shoot and root lengths, higher biomass, and larger leaf area compared to controls. Photosynthetic rates and stomatal conductance were found to be enhanced under ALAN, but internal CO₂ concentrations and transpiration rates showed significant alterations. Biochemically, ALAN-treated plants exhibited higher chlorophyll content, increased antioxidant enzyme activities, and elevated flavonoid and protein levels. Ultrastructural analysis revealed significant changes in chloroplast integrity, nuclear function, and mitochondrial activity under ALAN conditions. The study concludes that ALAN significantly affects the physiological, biochemical, and ultrastructural dynamics of S. melongena, highlighting the need for strategies to mitigate light pollution and preserve plant health and ecosystem balance.

Low gain avalanche detectors (LGADs) are poised for deployment in the intense radiation environments of future particle physics experiments. These advanced silicon sensors incorporate an additional moderately doped p ⁺ layer (in p-type substrate having n ⁺ as the high doping layer) to create a high electric field region, facilitating impact ionisation. This design imparts LGADs with superior timing capabilities, high signal-to-noise ratio, and radiation hardness compared to traditional silicon sensors. Given their application in particle colliders where they are exposed to high levels of charged and neutral hadrons, understanding LGAD behaviour under such conditions is critical to optimise their performance. In this study, we use the Silvaco technology computer-aided design simulation platform to evaluate the performance of neutron-irradiated LGADs against the available measurement results. The simulations incorporate the neutron radiation damage model, previously developed at the University of Delhi for traditional silicon sensors. However, LGADs also exhibit an acceptor removal mechanism, particularly in the gain layer, which is not accounted for in the existing model. Further, a distinctive feature of the LGAD is the importance of charge multiplication or impact ionisation even at small bias voltages. There is a need to optimise the impact-ionisation parameters, both for non-irradiated and neutron-irradiated LGADs. To address this, the present neutron radiation damage model is extended for thick (300 µ m) neutron-irradiated LGAD structures, where the acceptor removal mechanism and optimised impact ionisation model are integrated in the simulation framework. By exploiting the results, these LGAD parameters are optimised to successfully reproduce and explain experimental data for thick LGADs, full depletion voltage ( V _FD ) and charge collection (CC). This validation of our model is crucial for optimising radiation-hard LGAD designs, essential for mitigating increased dark current and decreased CC efficiency in high-neutron-fluence environments.

We present comprehensive technology computer-aided design modeling results for a proton-irradiated, 300 μ m thick low gain avalanche detector (LGAD) with a boron implant dose of 1.6 × 10 13 cm − 2 . We first assess the geometrical and doping parameters of the simulated LGAD through comparison of the electrical and transient current technique characteristics with experimental measurements. Subsequently, we model the effects of proton irradiation on the simulated device. Unlike traditional detectors (PIN and strip detectors), LGADs contain an additional gain layer that facilitates intrinsic charge multiplication but undergoes acceptor removal under irradiation, necessitating the incorporation of these effects in radiation-damage modeling. We employ the Delhi University’s Proton Damage Model (PDM), originally developed for traditional sensors, as a baseline to incorporate radiation-induced trap defects. We then extend the PDM by implementing the acceptor removal mechanism (ARM) to account for the acceptor removal in the gain layer. The simulation results, incorporating the combined PDM and ARM framework, still underestimate the experimental gain, indicating that the model requires further advancements to capture the real device behavior. To understand this discrepancy, we investigate the role of impact ionization in charge multiplication. While the default Selberherr impact ionization model effectively estimated the gain for a non-irradiated LGAD, reproducing the gain of the irradiated LGAD required a fluence-dependent parameterization. This parameterization led to reasonable agreement between the simulated gain and the corresponding gain measurements under red and IR laser illumination. Further, we examine the electrical characteristics of the irradiated device using both the default and the optimized Selberherr impact ionization models to assess the impact of the parameterization. Overall, this work provides comprehensive insights into proton-induced damage in LGADs and highlights the need for refined modeling approaches.