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This work explores the magneto-hydrodynamics (MHD) Jeffery-Hamel nanofluid flow between two rigid non-parallel plane walls with heat transfer by employing hybrid nanoparticles, especially Cu and Cu-Al[Formula: see text]O[Formula: see text]. Here the MHD nanofluid flow problem is extended with fuzzy volume fraction and heat transfer with diverse nanoparticles to cover the influence of thermal profiles with hybrid nanoparticles on the fuzzy velocity profiles. The nanoparticle volume fraction is described with a triangular fuzzy number ranging from 0 to [Formula: see text]. A novel double parametric form-based homotopy analysis approach is considered to study the fuzzy velocity and temperature profiles with hybrid nanoparticles in both convergent and divergent channel positions. Finally, the efficiency of the proposed method has been demonstrated by comparing it with the available results in a crisp environment for validation.
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http://dx.doi.org/10.1038/s41598-022-24259-6 | DOI Listing |
PLoS One
September 2025
Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt.
Polar protic and aprotic solvents can effectively simulate the maturation of breast carcinoma cells. Herein, the influence of polar protic solvents (water and ethanol) and aprotic solvents (acetone and DMSO) on the properties of 3-(dimethylaminomethyl)-5-nitroindole (DAMNI) was investigated using density functional theory (DFT) computations. Thermodynamic parameters retrieved from the vibrational analysis indicated that the DAMNI's entropy, heat capacity, and enthalpy increased with rising temperature.
View Article and Find Full Text PDFiScience
September 2025
State Key Laboratory of Advanced Marine Materials, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
Super austenitic stainless steels (SASS) face challenges like galvanic corrosion and antibacterial performance when welded to carbon steel (Q235) in marine environments. This study demonstrates that adding 1.0 wt% cerium (Ce) to SASS refines the heat-affected zone (HAZ) grain structure (from 7 μm to 2 μm), suppresses detrimental σ-phase precipitation, and forms a dense oxide film.
View Article and Find Full Text PDFChem Commun (Camb)
September 2025
Guangdong Provincial Key Laboratory of New Energy Materials Service Safety, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518055, China.
Thermocells (TECs) represent a promising technology for sustainable low-grade waste heat (<100 °C) harvesting, offering distinct advantages such as scalability, structural versatility, and high thermopower. However, their practical applications are still hindered by low energy conversion efficiency and stability issues. In recent studies, electrolyte engineering has been highlighted as a critical strategy to enhance their thermopower by regulating the solvation structure and redox ion concentration gradient, thereby improving conversion efficiency.
View Article and Find Full Text PDFJ Agric Food Chem
September 2025
Department of Biotechnology, Graduate School of Engineering, The University of Osaka, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
During brewing processes, proteins such as lipid transfer protein 1 (LTP1) are exposed to high temperatures, which later affects the beer foam properties. To develop high-quality beer, it is therefore essential to understand the protein chemical modifications and structural alternations induced by the high temperatures and their impact on beer foam. This study characterizes heat-induced chemical modifications and changes in the molecular size distribution and structure of LTP1 and its lipid-bound isoform, LTP1b, using size-exclusion chromatography and reverse-phase chromatography/mass spectrometry.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Max Planck Institute for Solar System Research, Göttingen 37077, Germany.
Turbulent convection governs heat transport in both natural and industrial settings, yet optimizing it under extreme conditions remains a significant challenge. Traditional control strategies, such as predefined temperature modulation, struggle to achieve substantial enhancement. Here, we introduce a deep reinforcement learning (DRL) framework that autonomously discovers optimal control policies to maximize heat transfer in turbulent Rayleigh-Bénard convection.
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