Cluster University of Srinagar

In this study, the excitation functions of evaporation residues produced in the ^18 O + ^144 Sm system have been measured through offline γ ray spectrometry. The statistical model code PACE-4, ALICE91, and EMPIRE 3.2.2 were utilized to distinguish the evaporation residues populated through the compound and non-compound nuclear fusion processes. For a comprehensive investigation of fusion suppression and its correlation with the charge of the target and the Q_α value of the projectile, the compound nuclear fusion cross sections of various projectile-target combinations were analyzed within the framework of the Universal Fusion Function (UFF) reduction method. The study revealed that the measured fusion functions of various systems were suppressed compared to the UFF, where the degree of suppression was dependent on the projectile’s Q_α value. Besides that, an exponential increase in the suppression was further observed with increasing Q_α values, while the target charge had a marginal effect. A generalized formula based on present study has been proposed to estimate fusion suppression of different systems as a function of Q_α value of the projectile. The above observations indicates that the involvement of non-compound nuclear reactions leads to a reduction in the compound nuclear fusion cross section at energies above the Coulomb barrier. This is primarily attributed to the loss of flux in the compound nuclear fusion channel, which is offset by an increased flux in non-compound fusion processes.

A major drawback of traditional sensors is their sluggish recovery time, which hinders their use in real-time sensing applications. This study introduces a novel optoelectronic approach for benzene (C6H6) detection using armchair and zigzag silicene nanoribbons (ASiNR and ZSiNR) under varying photon energies (0-5 eV) to investigate the electronic, charge transport and photo response behavior, via first-principles density functional theory (DFT) and non-equilibrium Green's function (NEGF) formalism. Our findings reveal that the hydrogen-edge passivated ASiNR and ZSiNR exhibit semiconducting and quasi-metallic band structures respectively. Upon benzene adsorption, the electronic and transport properties of silicene nanoribbons undergo significant modulation, resulting in substantial conductivity changes, as confirmed by I-V characteristics and transmission spectra. The strong adsorption energy of benzene (up to-0.93 eV for ASiNR and-0.87 eV for ZSiNR in the dark) ensures stable detection. At the same time, UV irradiation drastically reduces recovery times by up to 99.9 %, enabling rapid sensor regeneration. Furthermore, ZSiNR exhibits superior photocurrent response, making it a highly efficient candidate for optoelectronic VOC sensing. These results establish silicene nanoribbons as promising platforms for next-generation gas sensors, leveraging photon-assisted mechanisms for ultra-sensitive and highly responsive VOC detection at sub-ppb concentrations.

In the Kashmir Himalayas, the regeneration of key forest species such as Spruce (Picea smithiana) and Fir (Abies pindrow) is declining, especially at higher elevations. This is largely due to extended gaps between good seed years and the excessive accumulation of slowly decomposing conifer litter, which requires 10-13 years for 95% breakdown. The persistence of litter is driven by high litterfall and slow decomposition, influenced by external factors (temperature, humidity, soil quality, decomposers) and intrinsic litter traits (lignin, cellulose, hemicellulose, nitrogen). Conifer litter, with a high lignin content (approximate to 29.26%), is particularly resistant, creating a major barrier to nutrient cycling and natural regeneration. To address this challenge, the present study investigated the biodegradation potential of rhizospheric microflora from high-altitude forests of Kashmir. Enzymatic screening revealed strong extracellular activities, with notable prevalence of protease (85.71%), cellulase (64.28%), xylanase (60.71%), and other enzymes crucial for organic matter turnover. Molecular identification of the most efficient strains confirmed their potential as eco-friendly agents to accelerate litter degradation. By clearing the forest floor and enhancing seed-soil contact, these microbes foster favorable conditions for natural regeneration. Thus, rhizospheric microflora represent a sustainable, nature-based solution to overcome litter accumulation and support forest resilience in the fragile Himalayan ecosystem.

The growing concern over toxic volatile organic compounds (VOCs), such as methanol and ethanol, which pose health and environmental risks, highlights the need for advanced gas sensors with high sensitivity, rapid response, and quick recovery. Specifically, well-defined, nanoporous structures are crucial for improving sensor sensitivity and selectivity. This article uses first-principles calculations based on density functional theory (DFT) to study the structural, electronic, and gas-sensing properties of one-dimensional nanoporous armchair silicene nanoribbon (ASiNR) and fluorine-functionalized nanoporous ASiNR (F-ASiNR) devices. We have created various point defects, including monovacant (MV), divacant (DV), and trivacant (TV), by selectively removing silicon atoms from the structure. To achieve this, we identified optimal adsorption sites, adsorption energies, interaction distances, and work function values. Additionally, current-voltage analysis confirms that nanoporous ASiNR sensors are highly sensitive, with the TV configuration showing the highest ethanol response of 433 % and excellent selectivity for ethanol over other sensor types. Furthermore, we calculated recovery times under visible light at 300 K, which varied considerably, with the TV sensor exhibiting the shortest recovery times of 2 ms and 1.36 ms for methanol and ethanol, respectively. Our results demonstrate that the proposed nanoporous ASiNR sensors are promising for detecting toxic VOCs and have strong potential for real-world applications in environmental monitoring and safety.

The p53 mutation is the most common genetic mutation associated with human neoplasia. TP53 missense mutations, which frequently arise early in breast cancer, are present in over thirty percent of breast tumors. In breast cancer, p53 mutations are linked to a more aggressive course of the disease and worse overall survival rates. TP53 mutations are mostly seen in triple-negative breast cancer, a very diverse kind of the disease. The majority of TP53 mutations originate in the replacement of individual amino acids within the p53 protein's core domain, giving rise to a variety of variations referred to as "mutant p53s." In addition to gaining carcinogenic qualities through gain-of-function pathways, these mutants lose the typical tumor-suppressive features of p53 to variable degrees. The gain-of-function impact of stabilized mutant p53 causes tumor-specific dependency and resistance to therapy. P53 is a prospective target for cancer therapy because of its tumor-suppressive qualities and the numerous alterations that it experiences in tumors. Phenotypic abnormalities in breast cancer, notably poorly differentiated basal-like tumors are frequently linked to high-grade tumors. By comparing data from cell and animal models with clinical outcomes in breast cancer, this study investigates the molecular mechanisms that convert gene alterations into the pathogenic consequences of mutant p53's tumorigenic activity. The study delves into current and novel treatment approaches aimed at targeting p53 mutations, taking into account the similarities and differences in p53 regulatory mechanisms between mutant and wild-type forms, as well.