Harcourt Butler Technical University

A range of Ni-based catalysts were prepared by supporting nickel on γ-Al2O3, SiO2, zeolites, and mesoporous materials using simple wet impregnation and co-precipitation techniques. Bimetallic and promoted systems (Ni–M = Co, Mo, W, Sn, La, and Cu) were systematically evaluated, with particular emphasis on Ni–Mo catalysts of varying Ni/Mo molar ratios. The catalysts were examined using techniques such as XRD, BET surface area analysis, H2-TPR, H2 pulse chemisorption, SEM, and 1H NMR spectroscopy. The hydrodearomatization reactions were carried out in a fixed-bed continuous reactor under relatively mild operating conditions (200–250 °C, 10–30 bar H2). Catalytic performance was strongly dependent on support nature, promoter metal, preparation method, and thermal pretreatment. Among monometallic systems, Ni/γ-Al2O3 exhibited superior activity compared to Ni/SiO2 and zeolite-supported catalysts. Mo promotion significantly enhanced Ni dispersion and hydrogenation efficiency, with the Ni–Mo/γ-Al2O3 catalyst at a Ni/Mo molar ratio of 15:1 delivering the highest performance. Under optimized conditions, aromatic content in LAB raffinate was reduced from ∼ 24 vol% to ∼ 2.6 vol% with overall conversion exceeding 90%. The co-precipitated Ni–Al (2:1) catalyst demonstrated excellent stability during a 100-h continuous run, retaining ∼ 80–90% activity and showing effective regeneration. The developed Ni–Al and Ni–Mo/Al2O3 catalysts enable efficient, stable, and economically attractive upgrading of LAB raffinate into jet/rocket fuel–compatible products under relatively mild operating conditions, offering a viable alternative to noble-metal-based hydrotreating catalysts.

A new technique enabling to improve feature selection process based on weighted intuitionistic fuzzy (IF) similarity relation (WIFSR) is suggested in the present study. Firstly, we discuss a novel WIFSR by improving the idea of IF similarity relation. Secondly, IF granular structure (IFGS) is established on the basis of WIFSR. Thirdly, IF rough set model is outlined based on the idea of aforesaid IFGS. Next, positive region is computed based on the lower approximation of IF rough set. Then, dependency of decision dimension over set of conditional dimension is calculated based on positive region and cardinality of the decision system. With granular structures, features/dimensions can be represented with different levels of abstraction to provide a dynamic and flexible selection approach. Moreover, we present a WIFSR designed to measure the similarity between features by taking into account their relevancy and non-redundancy. Proposed approach effectively addresses the problem of feature selection by measuring degree of dependency between features in IFGS framework. Mathematical validation is illustrated for all the established notions. Proposed method is experimentally evaluated on various datasets, and we successfully demonstrate its efficiency in terms of determining the inherent features while safeguarding them from later uncertainty and noise. Our experimental results illustrate that the suggested approach, in the context of accuracy and standard deviation, outperforms the existing feature selection methods. At the end, a new scheme is demonstrated to enhance the overall prediction performances of machine learning methods for antiviral peptides.

The pursuit of sustainable green technologies to reduce emissions has positioned microbial fuel cells (MFCs) as a promising solution. The operation of MFCs depends on biocatalytic drivers, often called the “engines” of the system. This review highlights the significance of exoelectrogenic microbes for achieving high-performance MFCs. Investigating their roles in both consortia and pure cultures, as well as the molecular mechanisms underlying electron transfer and biofilm activity, is essential for optimizing power production. This review discusses established exoelectrogens, such as Shewanella sp., Geobacter sp., and Pseudomonas sp., and highlights emerging strains, focusing on their behaviour within MFC systems. It also describes electrochemical characterization, molecular and microscopic techniques, and advanced omics-based approaches for studying the genes and proteins involved in electron transfer from the extracellular surface to the electrode. Thus, Improved understanding has enabled the modification of microbial properties through genetic and biofilm engineering, selective enrichment, and consortium optimization using artificial intelligence and machine learning models. Collaborative progress in material design, microbial strain development, and technological integration may enable future MFCs to store and supply power to devices in real-time.

Bergenia ciliata a himalayan medicinal herb, which has been traditionally used due to its extensive pharmacological properties. Nevertheless, the possible anticancer application on the molecular level has not been fully explored. This experiment was developed to determine the phytochemicals of B. ciliata as natural inhibitors of the epidermal growth factor receptor (EGFR), which is a major target in most epithelial cancers. Phytochemical data on the IMPPAT and PubChem databases were collected. The compounds were using SwissADME and ProTox-II on drug-likeness, absorption, and toxicity. Then six candidates with Lipinski rule and pharmacokinetics conditions were docked to EGFR (PDB ID: 4HJO) with AutoDock vina. The binding affinities of cianidanol and Leucocianidol were the highest, − 8.8 kcal/mol and − 8.7 kcal/mol respectively, as compared to the reference drug erlotinib which has a binding affinity of − 8.3 kcal/Mol. There were several hydrogen bonds and hydrophobic interactions with such critical residues as Lys_721, Thr_766, Asp_831 and Phe_832.Simulations of 100 ns of molecular dynamics showed constant RMSD (0.10–0.20 nm), low fluctuations of residues, and small radius of gyration of all the complexes. MM/PBSA required interactions revealed that the stabilization was dominated by van der Waals forces and electrostatic repulsions with the total binding energies of − 51 kJ/mol, − 46 kJ/mol, and − 34 kJ/mol with cianidanol, leucocianidol, and erlotinib respectively. The studies suggests that EGFR is strongly bound by B. ciliata phytochemicals, and their biocompatible profiles are safer and more inclined to biocompatibility compared to the conventional inhibitor. These findings have indicated that these compounds can be useful lead scaffolds in the development of anticancer drugs in future as they have been shown to possess promising properties that would be further validated by studies conducted in in vitro and in vivo.

We report an anomalous photoresponse in LiInP2Se6-gated monolayer MoS2 field effect transistors (FETs), driven by sub-bandgap photocarrier excitation and relaxation in LiInP2Se6. The MoS2/LiInP2Se6 heterostructure exhibits gate-tunable persistent negative photoconductivity, a rare phenomenon in 2D FET platforms. Notably, the photoconductivity change scales with incident light intensity, with weaker illumination producing slower, smaller responses and stronger illumination inducing faster, stronger suppression. Exploiting the nonlinear, intensity-dependent photoresponse of LiInP2Se6, we demonstrate an image-processing platform that enables contrast modulation directly at the sensor level. By varying two controllable parameters, namely, applied top-gate bias and light exposure time, the device response can be tuned to modify the contrast of the image. This intrinsic behavior illustrates how contrast tunability can be achieved on a chip, offering a simple and compact route toward elementary preprocessing functions in vision devices.