Pandit Deendayal Petroleum University

We present a novel approach to enhance the efficiency and stability of inorganic CsPbI2Br based perovskite solar cells (PSCs) through the incorporation of a solvated palladium (Pd) complex, PdBr2(PhCN)2, into perovskite precursor inks (PhCN = benzonitrile). This modification not only stabilizes the α-phase of CsPbI2Br perovskite owing to Pd2+ ions, which were found to be incorporated into the perovskite structure at the B-site, but also effectively improves the perovskite film morphology through the removal of benzonitrile molecules during annealing. Further systematic study reveals that the addition of PdBr2(PhCN)2 results in a better matched energy-level alignment and reduced charge carrier recombination compared to the unmodified material. As a result, the optimized device enables an efficiency of 16.4% with an open-circuit voltage (VOC) of 1.27V, which outperforms the control device (14.1%, 1.19V). The device with PdBr2(PhCN)2 shows substantially enhanced environmental and operational stabilities, retaining ≈75% and 90% of their initial PCEs after 500h aging, respectively. This study demonstrates the potential of B-site engineering via solvated precursors as an effective route toward stable and efficient inorganic perovskite photovoltaics.

Purpose This study aims to investigate the tribological performance of 95% Al6061 reinforced with 4% B4C and 1% self-lubricated MoS2 in hybrid metal matrix composites fabricated via stir casting. Pin-on-disk experiments were conducted under varying loads, sliding velocities and temperatures to evaluate wear loss and coefficient of friction (COF). Design/methodology/approach To identify optimal operating conditions, a multicriteria decision-making strategy was adopted using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) alongside its fuzzy-based formulation, enabling comprehensive ranking of experimental trials. Findings In the fuzzy TOPSIS evaluation indicate that experimental tribological condition with normal load, sliding velocity and temperature having A5 (10 N, 1.25 m/s, 50 °C) achieved the highest overall coefficient value (0.7465), whereas experiment A2 recorded the lowest (0.0275). A comparative assessment revealed that fuzzy TOPSIS provided greater robustness by effectively incorporating uncertainty and subjective judgment, which are often overlooked by conventional crisp methods. In fuzzy TOPSIS compared to alternative A2, alternative A5 demonstrates a significant enhancement in tribological performance, with approximately 93.6% reduction in wear loss and 50.1% reduction in COF, confirming its superior wear and friction characteristics. These findings demonstrate that fuzzy TOPSIS serves as a more reliable decision-making tool for optimizing pin-on-disk test parameters. Originality/value This study presents a structured comparison between classical and fuzzy TOPSIS methodologies for the tribological optimization of self-lubricating Al6061/B4C/MoS2 hybrid composites, an area that has received limited attention in prior studies. The findings demonstrate that the fuzzy decision-making framework more effectively determines optimal test parameters, resulting in notable reductions in wear loss and frictional response. Such improvements enhance composite durability and performance reliability, with potential implications for environmentally sustainable engineering applications.

Obesity is a complex and multifaceted disease associated with metabolic diseases and severe health problems worldwide. There is an urgent need for novel therapeutic strategies since the currently available treatment options are often ineffective or cause adverse effects. Since they can modulate crucial biochemical pathways,phytochemicals, which are plant-derived compounds, provide a promising approach to the design of anti-obesity drugs. Such ways through which these substances work are preventing adipogenesis, regulating lipid metabolism, dropping inflammation, improving insulin sensitivity, and reestablishing energy balance. These are some of the main topics of this review. Numerous phytochemicals have emerged as auspicious agents targeting adipocytes, gut bacteria, and essential enzymes elaborate in metabolic pathways associated to obesity, such as polyphenols, flavonoids, and terpenes. Additional preclinical and clinical data testify to the rationality of these molecules toward their intended therapy aims. Prospects for incorporating phytochemicals into treatment plans for obesity are deliberated next, along with potential synergistic effects of plant bioactives and allegations for polyherbal formulations.

In precision manufacturing, the selection of cutting tools and uncontrolled tool wear is a major challenge which directly affects manufactured product quality, surface integrity and process efficiency. This work uses advanced deep learning (DL) architectures: InceptionNetv3, ResNeXt, and InceptionResNetv2 that have been tuned through metaheuristic optimization strategies to monitor and choose milling cutters. The Tunable Q Wavelet Transform (TQWT) is used in the proposed approach to preprocess and convert vibration signals to scalograms, which act as an input feature for deep learning models. To further improve prediction accuracy, each model’s hyperparameters are adjusted using Ant Lion Optimization (ALO) and the Clonal Selection Algorithm (CSA). Experimental investigations on milling dataset demonstrate that, the CSA-optimized InceptionResNetv2 exhibited the average prediction accuracy (98.25%), according to five-fold cross-validation for cutter c4. The ALO and CSA tuned models demonstrate improved generalization through five-fold cross validations ensuring robustness under varying operating and cutter conditions. The proposed approach offers a computationally efficient and scalable solution for real time cutter selection and tool wear monitoring in industrial environments. By eliminating conventional feature extraction and enabling adaptive learning from vibration based scalograms, the proposed framework can be integrated in Industry 4.0 manufacturing systems for predictive maintenance and process optimization.