Noida International University

Chocolate is one of the most popular widely consumed food products (Tan et al. in Nutrients 13(9):2909, 2021. https://doi.org/10.3390/nu13092909 ) and comprises of various bioactive compounds including proteins, carbohydrates, fats, and antioxidants. In this study, attenuated total reflectance fourier transform infrared spectroscopy (ATR-FTIR spectroscopy) was employed for a preliminary investigation of the molecular composition and authenticity comparison of four commercially available different varieties of chocolate samples from the Indian market, namely Barone, Dairy Milk, Munch, and Nestlé. Experimental FTIR spectra were compared with theoretical vibrational spectra obtained from density functional theory (DFT) calculations at the B3LYP/6-311++G(d, p) level for major constituents, particularly theobromine and caffeine, showing good agreement between calculated and observed vibrational frequencies. Mulliken charge analysis and molecular electrostatic potential (MEP) mapping revealed electron-rich regions localized around oxygen and nitrogen atoms, while positive potential regions were observed near hydrogen atoms, consistent with the spectral assignments. Furthermore, bootstrap-assisted principal component analysis (PCA) was applied to the FTIR data and successfully discriminated four chocolate samples, demonstrating the reproducibility and reliability of the dataset. The combined application of ATR-FTIR spectroscopy, DFT calculations, and multivariate statistical analysis thus provides a robust and effective approach for chocolate profiling and authenticity verification.

In this paper, the sufficient condition for the integrability of the continuum fractional Hankel wavelet kernel is obtained and further Calderón’s reproducing formula is defined by exploiting the theory of fractional Hankel transform and fractional Hankel convolution operators.

The low bandgap (LBG) polymer-based nanocomposites have enormous capability to afford more advanced and less costly electronic technologies than the current commercialized ones. Here we evaluate the impact of LBG conducting polymer used in the active layer as PANI-ES: ICBA-based optoelectronic devices. The (bulk heterojunction) BHJ active layer has also been treated with solvent additives, namely 1,8-diiodooctane (DIO) and 1-chloreonapthlene) (CN), to assess the impact of solvent additives on the film properties and device performance. UV-Vis spectra analysis was performed to observe the modification of the absorption behavior of the active layer with the solution additives. Field emission scanning electron microscopy (FESEM) has been conducted to reveal the solvent additive-induced changes in the active layer. The modified structure of the active layer was observed using Raman spectroscopy. The device processed with DIO additive delivered higher photocurrent and responsivity than the device treated with CN. We estimated the diode parameters with the help of dark I-V curves to understand the interface quality of the devices. The impedance parameters are consistent with the changes detected in morphology and the device performance.

Friction Stir Welding (FSW) of aluminium alloy dissimilar welding is essential to lightweight constructions in the automotive, aerospace and energy sector, yet stabilizing the quality of the joints in this context continues to be a challenge because of extreme thermal gradients, Intermetallic compound (IMC) formation and the dynamics of the process. Traditional offline or rule-based optimization methods are not flexible to material flow and heat production changes in real-time, which usually leads to defects and poor mechanical behavior. The paper was dedicated to the optimization of the main FSW process parameters, such as the tool shoulder-to-pin diameter ratio (SP), tool rotational speed (TRS), and tool traverse speed (TTS) with the help of a reinforcement learning (RL)-based real-time optimization algorithm to maximize the weld integrity and performance. The suggested methodology incorporated the in-situ sensor feedback, such as torque, axial force, interface temperature, and tool vibration in a digital process environment to allow the continuous state monitoring during welding. A model-free RL model based on Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to dynamically adjust SP, RS and TF in order to regulate heat input and material flow. An objective rewarding multi-objective was developed to reduce IMC thickness, thermal variability, and maximise tensile strength and joint effectiveness. The dissimilar aluminium alloy butt joints were experimentally validated to converge rapidly on policy and to be able to stay stable amidst process disturbances. The RL-optimized process demonstrated a 19.7% increase in tensile strength (142 f 5.8 MPa to 170 f 4.2 MPa), a 23.3% reduction in IMC thickness (8.6 f 0.7 & micro;m to 6.6 f 0.5 & micro;m), and a 17.2% improvement in electrical conductivity compared to the optimized static baseline. The study demonstrates that reinforcement learning enables intelligent and self-optimizing FSW through realtime adjustment of SP, TRS, and TTS, thereby offering a scalable route toward autonomous manufacturing of high-performance Cu-Al dissimilar joints.

Textile dye wastewater is a major global contributor to environmental pollution, characterized by complex physicochemical characteristics such as high stability, resistance to degradation, and potential toxicity. These effluents pose significant ecological risks, including aquatic toxicity, bioaccumulation, and the presence of carcinogenic and mutagenic compounds. This review critically synthesizes various treatment technologies, categorizing them into physicochemical, chemical, and biological methods, and highlights the limitations of conventional processes, such as high energy consumption and problematic sludge production. A core focus of this work is the comprehensive analysis of biological treatment approaches, particularly the enzyme-mediated degradation mechanisms involving oxidoreductases like azoreductases, laccases, and peroxidases. The review delves into the catalytic pathways of these enzymes and discusses the effectiveness of microbial systems (bacteria, fungi, yeast, and algae) in achieving high decolorization and mineralization efficiencies. By integrating recent data and comparative analyses of current research trends, this manuscript emphasizes the novelty of its mechanistic exploration and forward-looking perspective. It concludes by outlining critical research gaps and future directions, including the optimization of hybrid treatment systems, enzyme engineering, and the scaling of biotechnological processes for achieving sustainable, cost-effective, and complete textile dye wastewater remediation.