• The nacre-inspired WPU/1GNPs@1CNTs film was fabricated by bionic LBL method. • It showed high in-plane λ of 25.2 W m − 1 K − 1 and out-of-plane λ of 1.94 W m − 1 K − 1 . • It also presented excellent Joule heating performance and ultralow cost. • It achieved superior performances in BTM compared to commercial counterpart. Thermally conductive polymeric nanocomposite films have demonstrated great potential for battery thermal management (BTM). However, it has remained an enormous challenge to create low-cost, scalable, flexible, highly thermally conductive films capable of Joule heating performance due to a lack of rational material design strategies. Herein, an efficient approach is proposed to overcome this long-standing barrier via the strategy of welding carbon nanotubes on the graphene nanoplatelets’ surface (GNPs@CNTs) and the bionic lay-by-lay (LBL) assembly technique. The horizontally aligned continuous GNPs layers function as primary in-plane thermally conductive paths, minimizing the thermal resistance. Meanwhile, the secondary CNTs network interconnects the GNPs into an integrated and densified 3D thermally conductive framework. As results, the as-prepared nacre-inspired waterborne polyurethane (WPU) nanocomposite film presents outstanding thermally conductive performances (high in-plane thermal conductivity ( λ ) of 25.2 W m −1 K −1 and out-of-plane λ of 1.94 W m −1 K −1 ), ultralow cost (96.5 USD/kg), and excellent Joule heating performance (Joule-thermal response of 13.5 °C/s), which far outperforms previous thermal management counterparts. Also, the WPU nanocomposite film could achieve higher cooling and preheating efficiency for Li-ion battery compared to commercial counterpart products. This work provides a promising solution to create high-performance thermal management polymeric nanocomposite films, which hold great potential for BTM systems.

High strength, high thermal conductivity Mg–Zn–Ce alloys prepared by rapid solidification and hot extrusion

This study investigates the thermo-fluidic dynamics of a magnetised hybrid Ag-TiO₂/EG-water nanofluid over a bi-directionally stretching/shrinking surface, a configuration relevant to advanced thermal management in biomedical, aerospace, and electronics cooling. To address the limitations of traditional numerical approaches in multi-parameter optimization, novel Artificial Neural Network (ANN) optimized with the Levenberg–Marquardt algorithm (LMA) is developed. The model incorporates realistic physical effects, including temperature-dependent viscosity and thermal conductivity, surface suction, and Joule heating. A high-fidelity dataset was generated by solving the transformed governing equations using MATLAB's bvp4c solver, covering the parameter ranges: magnetic parameter (1 ≤ M ≤ 5), mixed convection (−1 ≤ λ ≤ 6), variable viscosity (0.1 ≤ a ≤ 1.5), thermal conductivity (0.1 ≤ b ≤ 0.5), stretching ratio(0.1 ≤ ε ≤1), suction parameter (−1 ≤ S ≤ 1), and nanoparticle volume fraction (0.01 ≤ ϕ ≤ 0.1). The velocity and temperature data, varying with M , ε , λ , a , and b , were divided into 70% training, 15% validation, and 15% testing sets for ANN-LMA modelling. The framework achieved absolute 10 −3 –10 −9 and MSE between 10 −10 –10 −7 , demonstrating high predictive accuracy. Results reveal that the magnetic field enhances vertical velocity in shrinking flows but reduces it in stretching flows, while horizontal velocity is suppressed in both cases. Temperature rises with magnetic and mixed convection effects, and variable conductivity causes hybrid nanofluids to exhibit up to 45% higher thermal elevation than mono nanofluids. Notably, a 10% Ag + TiO₂ mixture enhances heat transfer by 45%, compared to 18.6% for 10% Ag alone. The novelty of this work lies in its integrated AI-driven framework that accurately captures coupled multiphysics interactions, providing a rapid and reliable predictive tool for the design of advanced thermal-MHD systems. • A high-fidelity computational framework is developed for MHD hybrid nanofluid flow over a bi-directional deformable surface. • Variable viscosity, thermal conductivity, Joule heating, suction, and mixed convection effects are incorporated for realistic modelling. • A data-assisted ANN–LMA model accurately predicts thermo-fluidic behaviour with absolute errors as low as 10 −9 . • Hybrid Ag–TiO₂ nanofluid exhibits significantly enhanced thermal performance compared to conventional nanofluids. • Heat transfer is improved by 18.6% with Ag nanoparticles and by 45% using Ag–TiO₂ hybrid nanoparticles.

Incremental analysis of thermal conductivity optimization model for polymer carbon nanotube composite materials considering branch heat conduction

Efficient thermal management is essential for aluminum components operating in environments with fluctuating thermal loads, especially when protective surface treatments are required, like in heat exchangers working in phase change material (PCM) environments. Plasma electrolytic oxidation (PEO) provides excellent wear and corrosion resistance but typically produces ceramic layers with low intrinsic thermal conductivity, potentially limiting heat-transfer performance. This study systematically evaluates how PEO processing mode (unipolar vs. bipolar) and nanoparticle additives influence coating microstructure, phase composition, porosity, and the effective thermal conductivity of coated AlMg3 substrates. Coatings were produced using a standard alkaline silicate-phosphate electrolyte with and without ZnO, WS 2 , TiC, TiN, and BN particles. Microstructural and compositional analyses were performed via SEM/EDS and GIXRD measurements, while thermal conductivity of the full coated system was determined using laser flash analysis (LFA). Across all conditions, PEO coatings reduced thermal conductivity relative to bare alloy (129.78 W/(m·K)) but only moderately, with most 5-min treatments remaining within ∼20% loss of the substrate conductivity (111.43 W/(m·K) for particle-free unipolar mode coatings). Particle additions resulted only in limited improvements; the best performing additive, 3 g/L ZnO in unipolar mode, yielded slight conductivity enhancement associated with more uniform particle incorporation (119. 03 W/(m·K)). Overall, results highlight that PEO coatings can deliver protective functionality while maintaining acceptable thermal performance.

Evaluation of sustainable longan shell biochar energized commercial organic phase change material for low temperature thermal regulation

High thermal conductivity biphenyl-type polyester with photochromism, thermo-responsive, and shape memory performance.

High-entropy engineering in (Sr,Ba,RE)Nb2O6-δ tungsten bronze oxides: achieving ultralow thermal conductivity and enhanced thermoelectric performance

Dual surface modification of MXene with bio-derived manganese phytate and carbon microspheres for polylactic acid: Synergistic enhancements in flame retardancy, smoke suppression, and thermal conductivity

A fast inverse heat conduction model (IHCM) is developed for estimating unknown properties of multi-layer structures considering internal heating. The model leverages a closed-form analytical forward solution to enable efficient inverse computations. It requires only a single internal temperature measurement as input, with unknown parameters estimated by minimizing an objective function using an interior-point optimization algorithm. The IHCM accurately identifies thermal properties such as thermal conductivity, specific heat capacity, density, and heat transfer coefficient in a high-precision linear motor. It also detects internal geometric variations, including the location and severity of delamination caused by thermal expansion. These predictions are validated against finite element (FE) simulations. Furthermore, a sensorless strategy is proposed, enabling non-invasive parameter estimation based on electrically inferred temperature data. The feasibility, sensitivity, and limitations of the IHCM are assessed across various scenarios. Results demonstrate its strong potential for real-time diagnostics, online defect detection, and thermal performance monitoring in multi-layer composite systems with internal heat generation, such as electrical machines. • Fast inverse parameter estimation in multilayer composites with internal heating. • Diagnoses thermal aging and detects global and local delamination. • Flexible analytical forward framework for multiple non-homogeneities. • High computational speed enables real-time, lightweight thermal diagnosis. • Noninvasive, sensorless estimation through electrically inferred temperature.

Bioconvective flow and heat transport analysis of water-based nanoparticles with variable viscosity and Non-Fourier-Non-Fick effects.

Thermal conductivity and interfacial engineering of reaction-bonded porous SiC/diamond composites

Interfacial and thermophysical engineering for enhanced thermal performance of boron carbide-modified HITEC nanocomposites in high-temperature thermal energy storage applications

Cellulose microtubes/phenolic resin foam for stabilizing phase change materials and enhancing phase change performances.

Computational discovery of Li-based tetragonal LiFeH4 and LiCoH4 complex hydrides as efficient solid-state hydrogen storage materials for practical applications

The purpose of current study is to analyse the flow and thermal behaviour of Sutterby hybrid nanofluids, with particular attention given to an inclined magnetic field, viscous dissipation, heat source, Cattaneo-Christov heat flux, and thermal radiation model. In this work, we consider the Ag−Au/Blood based hybrid nanoparticle in the non-Newtonian Sutterby fluid in order to investigate how much they create the enhancement in the thermal conductivity. As the inclined magnetic field interacts with the electrically conducting fluid, its impacts on flow properties are examined. A more precise description of heat flow is obtained by using the Cattaneo-Christov model, which considers the speed limitations of heat conduction. The thermal transport analysis are investigated by using the thermal radiation, viscous dissipation and heat source or sink. The physical phenomena stated above have an impact on the formulation of the governing equations of mass, momentum and energy transfer. The model is established in the form of dimensional PDEs and suitable transformations is applied to translate the PDEs into non-dimensional ODEs. A mathematical model is created, and suitable numerical technique (Runge-Kutta 4th order) are used to find solutions. The findings demonstrate the combined impact of these variables on the fluid flow's temperature distribution, velocity profiles, and heat transfer rates. The results reveals that Lorentz force or retardation force causes the velocity profile to decrease as the magnetic field increases. When comparing the Ag/blood case to the Ag-Au/blood case based on the Sutterby Deborah number, the depth of the thermal boundary layer rapidly drops. The hybrid nanofluid's temperature rises while its velocity decreases due to the nanoparticle volume percentage parameter. The friction drag heightens along with the Sutterby Deborah number and Power-law index estimates.

With the growing demand for electric mobility, copper is increasingly used in critical electric vehicle components. Laser welding is essential for joining these components but is challenged by copper’s high reflectivity and thermal conductivity. Molten pool simulations of laser welding are valuable to understand the complex interactions between the laser beam and material but the literature models are limited to linear paths. Moreover, there exists a significant variation in the laser absorption values in the literature, leading to uncertainty in prediction. In this study, laser absorptivity of copper is calculated from temperature- and angle-dependent Fresnel relations. The absorption values are implemented in an existing computational fluid dynamics solver that employs Volume of Fluid method to track melt pool surface and a ray tracing approach to account for multiple reflections within the keyhole. Two ring-core beam profiles are investigated experimentally for keyhole mode welding of a hairpin, with the laser traveling along an elliptical path on the hairpin top surface over multiple passes. Simulation results are extensively validated against experimental data including in-situ high speed videos and weld cross-sections at different locations along the non-linear path. The validation is essential to better quantify the uncertain parameters in laser welding including laser absorptivity, recoil pressure, and extent of turbulence. In addition to demonstrating the necessity of incorporating both temperature- and angle-dependent absorptivity for accurate process modeling of laser welding of copper, the simulation results reveal significant differences in laser absorption behavior and melt pool and keyhole evolution for the non-linear scan than a linear scan. • Angle- and temperature-dependent absorptivity derived from physical relation. • Extensive experimental validation includes in-situ high-speed video & cross section. • Repeated non-linear scans increase melt-pool volume, requiring turbulence modeling. • Elliptical paths alter absorptivity along major-minor axes by up to 15%. • Solidified weld in non-linear path is dome-shaped due to dominant surface tension.

Effect of electroshock treatment on thermal conductivity and mechanical properties of laser melting deposited (TiC+Ti-6Al-4V)/CuCrZr composites

Integrated NIR-II light sources via Cr4+-doped wafer-scale composite ceramics for enhanced image sensing

Tailoring the thermal and electrical conductivity of powder metallurgy porous copper for thermal management through pore structure modification