Purpose This study aims to explore an investigation of stagnation-point flow and thermal transport characteristics of a radiative ternary hybrid nanofluid, consisting of engine oil as the base fluid, supplemented with nanoparticles of SiO2, Cu and ZrO2. It addresses the flow on a stretching/shrinking surface, accounting for the effects of velocity slip and melting. Design/methodology/approach The corresponding partial differential equations are then converted into self-similar equations and are solved using MATLAB’s bvp4c algorithm. Since the model has two solution branches, a linear stability analysis is conducted to select the physically relevant flow regime. The high level of statistical regression indicates the strength of the model, as evidenced by the significant F-statistic, low p-value and high coefficient of determination. Findings The findings indicate that a higher rate of melting and high rates of slip amplify momentum and thermal boundary layers, resulting in better heat transfer performance, and a decreased value of the parameters causes dual-solution behaviour and a delay in the boundary-layer separation. Originality/value Due to these features, the applications of the findings, in terms of polymer extrusion, storage of thermal energy, cooling of phase changes, the nano-lubrication systems and a sophisticated manufacturing process with high thermal loads, show high applicability.

Solar cookers have evolved into an eco-friendly alternative to conventional cooking methods by reducing dependency on fossil fuels. Because of advancements in materials, insulation techniques, and reflector designs, their efficiency has significantly increased over time. Modern solar cookers incorporate PCM to enhance heat retention and enable cooking during non-sunny hours. PCM technology aids in the storage of thermal energy and improves overall cooking efficiency by absorbing excess heat and releasing it gradually. Recent studies have looked into using machine learning (ML) algorithms to optimize solar cooker performance. Regression models such as ANN, SVR, decision trees, and hybrid ML models have been used to predict temperature changes, optimize design parameters, and improve heat retention. Phase Change Materials (PCM) and Machine Learning (ML) have been combined to greatly increase the solar cookers' efficiency and versatility. ML-based predictive models will be essential in improving heat retention, optimizing reflector angles, and increasing overall thermal efficiency as AI-driven optimization advances further. This will make solar cooking more dependable and accessible for a range of climatic conditions. The present paper studies the advancement in solar cookers, experimental studies, and the best ML models used for predicting temperature, material performance, and cooker efficiency. Below figure shows the data acquisition of both PCM and non PCM for machine learning regression • Evolution of Solar Cookers – Traces the advancements in solar cookers, emphasizing eco-friendly alternatives to fossil-fuel-based cooking. • Improved Efficiency – Discusses innovations in materials, insulation techniques, and reflector designs that have enhanced solar cooking performance. • Phase Change Materials (PCM) for Heat Retention – Explores how PCM helps store thermal energy, enabling cooking even during non-sunny hours. • Machine Learning (ML) in Solar Cooking – Reviews the role of ML models in optimizing solar cookers by predicting temperature variations and improving efficiency. • Use of Regression Models – Highlights applications of ANN, SVR, decision trees, and hybrid ML models in predicting heat retention and optimizing design. • AI-Driven Optimization – Explains how ML techniques help refine reflector angles, material selection, and thermal performance for various climates. • Integration of IoT – Discusses the role of IoT in monitoring and controlling solar cookers for better efficiency and real-time optimization. • Experimental Studies – Summarizes experimental research on PCM-enhanced solar cookers and ML-driven performance enhancements. • Future Trends – Identifies potential advancements in AI, thermal storage, and automation to improve the reliability of solar cooking. • Impact on Sustainability – Emphasizes the contribution of AI and PCM in making solar cookers a viable, eco-friendly solution for global energy needs.

Bhimband, located in Bihar in eastern India hosts a well-known geothermal system that appears to be strongly influenced by fault-controlled circulation, and the surface springs there routinely reach temperatures of about 60-65 °C. In this research, the geothermal environment was analyzed using a combination of geological observations and a number of geophysical surveys, including gravity, magnetic, and electrical resistivity surveys to gain a better understanding of the subsurface environment and heat flow characteristics. Using these data, a probabilistic volumetric assessment was carried out through Monte Carlo simulations and the results indicate that the region may store on the order of ∼6.50 × 10 14 J of thermal energy out of which roughly ∼9.76 × 10 13 J could realistically be recovered. Based on these findings, a preliminary field development strategy was outlined for a small-scale binary power plant (∼3.1 MW) operating on an Organic Rankine Cycle and designed for a 20-year life. The plan suggests a phased drilling program of 24 wells which includes exploration, appraisal, production and reinjection wells, arranged in a manner that would allow long-term and balanced extraction of heat. The revised economic analysis estimates a CAPEX of ₹86.5 crore and OPEX of ₹45 crore over 20 years, resulting in a simple payback period of ∼20 years and an IRR of ∼8%, reflecting the high upfront investment and modest project scale. The environmental factors were incorporated into the design with a focus on reinjecting the cooled fluid to maintain the reservoir pressure and minimize surface emissions. On the whole, the assessment tends towards the technical feasibility of geothermal power production at Bhimband and provides a useful initial framework for sustainable geothermal development in India, in the wake of the country's increasing interest in clean energy alternative. • This study presents a comprehensive geothermal resource assessment by integrating detailed geological investigations with geophysical surveys (gravity, magnetic, and electrical resistivity) to characterize subsurface conditions and evaluate the geothermal potential of the study area. • The interpreted geophysical and geological data were further utilized in a probabilistic volumetric resource estimation using Monte Carlo simulation, which indicates a total stored thermal energy of approximately 2.7 × 10 14 J, with about 4 × 10 13 J estimated as technically recoverable. • Based on the estimated resource potential, a 1.3 MW binary Organic Rankine Cycle (ORC) power plant is proposed as part of a conceptual Field Development Plan (FDP). • Preliminary economic modelling estimates a capital expenditure (CAPEX) of around ₹83 crore and an operating expenditure (OPEX) of approximately ₹45 crore .

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The photocatalytic conversion of carbon dioxide into value-added fuels and chemicals is a promising route to mitigate greenhouse gas emissions while enabling sustainable energy storage. This study investigates the use of catalysts composed of exfoliated graphitic carbon nitride modified with either single-atom Ni sites or Pt nanoparticles for photo-thermal CO2 hydrogenation under combined light irradiation and thermal activation. The photocatalysts have been extensively characterized by different physico-chemical techniques, allowing the study of structure-activity relationships. Both metal-modified catalysts exhibit significantly enhanced CO2 conversion compared to pristine g-C3N4, which is attributed to improved light absorption, reduced charge recombination, and the role of metals as catalytically active sites. However, the catalytic behaviour varies markedly depending on the metal. Pt nanoparticles promote a thermally driven pathway assisted by light, enhancing overall activity and favouring CO formation, whereas Ni single-atom sites preserve the photocatalytic character of the support and shift the selectivity of hydrogenation towards methanol rather than methane primarily obtained over metal-free C3N4. These findings indicate that the combined photonic and thermal energy inputs efficiently activate both CO2 and H2, accelerating reaction kinetics and modifying reaction pathways beyond those achieved in purely thermal or purely photocatalytic systems.

Partial debranching and microwave treatment stabilize amylopectin-flavor interactions for better tsampa processing outcomes.

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