Dr. B. R. Ambedkar Institute of Technology

As the world transitions toward renewable energy solutions, anaerobic digestion (AD) is emerging as a key technology for biogas production. Among the key parameters influencing biogas production, temperature plays a critical role. Without adequate thermal regulation, biogas production may drop by over 50 %, impacting plant feasibility. This review comprehensively analyses thermal management in AD, emphasizing the critical role of temperature regulation and the factors affecting thermal demand. A structured three-tiered approach that incorporates heat retention, recovery, and external supply is presented to optimize thermal efficiency and reduce operational costs. Passive techniques utilizing insulation and phase change materials can reduce heat loss by up to 82 % and lag internal temperature fluctuations. Waste heat recovery from effluent, equipment, and/or integrated industry can meet up to 100 % of thermal energy requirements. Although solar thermal systems are observed to offer low-emission and operating-cost heating, their high capital and land requirements may limit feasibility. Hybrid systems combining solar, biogas, and biomass can provide scalable, climate-resilient alternatives with lower emissions and improved reliability. The review also dwells into Techno-economic evaluations showing that, despite capital and operating costs, well-designed systems achieve net-positive energy outcomes and short payback periods. Chronological advancements in computational fluid dynamics and modelling demonstrate prediction accuracy exceeding 85 %, significantly reducing design uncertainties and implementation costs. This review concludes with challenges and recommendations for modular, strategic, site-specific hybrid heating strategies, future policy, and research suggestions. By integrating technical insights, it offers a comprehensive framework to enhance AD performance, energy efficiency, and environmental sustainability.

Supercapacitors, as electrochemical charge storage device, require multiple attributes for commercialization, such as cost-effective and high-performance materials. Activated carbon (AC) derived from biomass directly addresses this need due to its favorable electrochemical properties and the abundant availability of low-cost feedstocks. In this study, we report for the first time the synthesis of AC from Terminalia arjuna fruit (AF) using a two-step process, pyrolysis and subsequent KOH activation, for the fabrication of an electric double-layer capacitor (EDLC). Two samples were obtained by varying the activation conditions, among which AFACK 1:3 demonstrates a high specific surface area, numerous defects, and suitable pore structures, which collectively enhanced efficient charge storage and facilitated favorable ion transport kinetics. At a current density of 1 A g-1, AFACK 1:3 achieved a specific capacitance of 486.75 F g-1. In the as-fabricated flexible-type symmetric supercapacitor device (AFACK 1:3//AFACK 1:3), a maximum energy density of 44.7 Wh kg-1 was achieved at a power density of 0.8 kW kg-1, accompanied by impressive cycling stability.

Extrusion-based direct ink writing (EBDIW) has emerged as a versatile technique for fabricating complex, customizable, and flexible electronic architectures using viscoelastic functional inks. This study reports the formulation and characterization of multi-walled carbon nanotube (MWCNT)/polydimethylsiloxane (PDMS) inks with tunable rheological properties suitable for EBDIW. MWCNT/PDMS inks with 2-10 wt% MWCNT loadings were formulated, and rheological analysis identified 4 wt% as the threshold critical for printability. Printable inks within the 4-10 wt% range produced well-defined 3D structures, with optimal shape fidelity observed at 6-8 wt%. Below 4 wt%, excessive spreading occurred, while above 10 wt%, nozzle clogging was observed. Scanning electron microscopy (SEM) revealed the uniform incorporation of MWCNTs throughout the polymer matrix, facilitating the formation of conductive networks and enabling fine structure printing. The FT-IR and XRD analyses revealed strong interfacial interactions and improved structural alignment between MWCNTs and PDMS chains. Mechanical testing demonstrated an enhancement in Young's modulus and tensile strength up to 4.89 MPa and 3.81 MPa at 8 wt% MWCNTs, respectively, followed by a decline due to filler agglomeration. Electrical conductivity of 3D-printed MWCNT/PDMS composites increased consistently with increasing MWCNT content, reaching 6.67 x 10- 2 S/m at 10 wt%. This work provides a mechanistic understanding of how filler concentration influences the rheological, mechanical, and electrical behavior of MWCNT/PDMS composites, enabling the 3D printing of complex, conductive structures for next-generation soft electronic applications.

The current investigation examines the spatial dynamics of laser self-focusing and terahertz (THz) generation during the interaction of zeroth-order Bessel-Gaussian (BG) laser beam with the plasma medium. Self-focusing and terahertz generation in the plasma medium are analyzed by considering the combined effects of relativistic and ponderomotive nonlinearities along with a density ramp. The combined effect of these nonlinearities induces a transverse density gradient, stimulating an electron plasma wave (EPW) inside the plasma. The self-focused laser beam interacts with the excited electron plasma wave, producing terahertz radiation in the plasma medium. An expression for the laser beam width and the corresponding yield of terahertz generation is derived using the WKB approximation and the moment theory approach. The study concludes that laser and plasma parameters, along with the density gradient, significantly influence laser self-focusing and the yield of terahertz generation in plasma.

Reliable, explainable prediction of milling surface roughness (Ra) in Inconel 625 enables tighter control of a notoriously difficult-to-machine superalloy. This study introduces an end-to-end, sensor-fusion and explainable deep-learning pipeline that integrates cutting-force and tri-axial vibration measurements. Raw signals are first smoothed via Savitzky-Golay filtering and then decomposed using ICEEMDAN to isolate physically meaningful oscillatory modes. A Lyapunovbased sensitive-mode criterion retains only the most informative components, while Sequential Feature Selection further reduces redundancy and mitigates overfitting. The refined feature set drives bidirectional recurrent regressors-Bi-LSTM and Bi-GRU-capable of capturing temporal dependencies in forward and backward directions for accurate Ra estimation. Model transparency is ensured through SHAP-based attribution, which links predictions to specific force-vibration features and modal scales, clarifying how multiscale dynamics influence surface finish. Taken together, this transparent, high-fidelity framework supports process optimization, tool-path tuning, and adaptive control in Inconel 625 milling. Using a CCRD dataset, the approach delivers high accuracy across tool variants. For cutting tool T1, the best XAI-Bi-LSTM model achieved R2 = 95.72 %, RMSE = 0.027 mu m, |R95%| = 1.85% and MAE = 0.023 mu m; forT2, R2 = 90.78 %, RMSE = 0.038 mu m, |R95%| = 1.90% and MAE = 0.034 mu m. SHAP analysis highlights depth of cut, feed, and entropy/spectral-center features as dominant contributors, aligning with known machining physics. The dataset showed maximum Ra = 0.62 mu m, with T2 producing higher roughness than T1. The results indicate that the proposed interpretable pipeline maintains strong predictive performance while exposing process-relevant factors that can guide parameter selection and monitoring.