Journal of Thermal Analysis and Calorimetry最新文献

Dachengzi oil shale samples were retorted over Fe₂O₃ and CaCO₃ in a small reactor to evaluate their catalytic effects on oil yield and quality. The results indicated two catalysts enhanced kerogen reactivity and secondary reactions of pyrolysis products, which slightly improved the shale oil yield but significantly upgraded pyrolysis volatiles. Adding catalysts facilitated the cracking reactions of heteroatomic compounds, increased the selectivity of aromatic hydrocarbons and promoted olefin aromatization. Fe₂O₃ produced shale oil with lower 1-olefins, aromatics, N and S contents, which more obviously catalyzed kerogen decomposition to improve the heavy fractions and asphaltenes. The total n-paraffins content was about twice that of total 1-olefins in the derived shale oils which mainly contained bicyclic and tricyclic aromatic compounds. Using catalysts increased the total n-paraffins content and PACs with high ring numbers. Catalytic cracking of naphthalenes more easily occurred over Fe₂O₃. Fe₂O₃ was more favorable to obtain high shale oil yield and quality than CaCO₃.

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The knowledge of thermal properties such as thermal conductivity, specific heat, and latent heat of melting is essential for the development of phase change materials (PCMs) for latent heat energy storage applications. Due to their good properties, aluminum-based eutectic alloys have become the most widely studied metal-based PCMs. In the present study, microstructure, thermal diffusivity, thermal conductivity, specific heat capacity, and latent heat of melting of the Al-33.6 mass% Cu eutectic alloy were examined using scanning electron microscopy, energy dispersion spectroscopy, differential scanning calorimetry, and light flash method. The results show that the microstructure of the investigated alloy consists of fine and coarse (Al) + Al₂Cu eutectic regions. Specific heat, thermal diffusivity, and thermal conductivity increase with increasing temperature in the temperature range 25–400 °C. The thermal conductivity of the studied alloy at room temperature is 134.3 Wm⁻¹ K⁻¹. The measured latent heat is 319.5 Jg⁻¹. The obtained results indicate that the Al–Cu eutectic alloy has considerable potential for application in the field of phase change energy storage materials.

A constructed design-based model is employed to analyze the multiple enhancements in viscosity and thermal conductivity within a porous disk filled with Ag-water nanofluid. For the first time, we are applying the concept of utilizing diverse thermophysical properties, including viscosity and thermal conductivity impediments, to analyze entropy generation within a system. The energy equation incorporates a binary chemical reaction and Arrhenius activation energy. We utilize a system of nonlinear partial differential equations to establish the mathematical framework governing the flow. This is subsequently transformed into a nondimensional partial differential form via dimensionless variables. Numerical investigations employ a finite difference scheme, exploring diverse values of the related physical parameters. A finite difference scheme implemented in MATLAB is used to obtain numerical and graphical results, highlighting the impact of various parameters on the 2D and 3D profiles of velocity, temperature, concentration, entropy generation, skin friction coefficient, and Nusselt number for different non-dimensional parameters. The obtained output shows that boosting the values solvent fraction factor ((beta _1)) and relaxation time ratio ((beta _2)) enhances the velocity profile of NS&LVPC nanofluid in momentum boundary layer thickness on both porous disk, outperforming S&EVPC and NS&VPC configurations. Raising the temperature difference ((gamma _{*})) and activation energy (E) reduces the heat and mass transfer in the nanofluid flow over a lower porous disk. Higher values of Eckert number (Ec), magnetic parameters (M), thermal radiation (Rd), and concentration ratio parameter ((T_text{c})) result in increased entropy generation profiles in both porous disks. As the volume fraction increases, the heat transfer rate exhibits an inverse trend in the Nusselt number for both porous disks. However, the nanolayer thermal conductivity ((Nu_3)) performs much better than spherical ((Nu_1)) and non-spherical thermal conductivity ((Nu_2)). Our findings indicate that flow performance is significantly better with nanolayer thermal conductivity and low viscosity particle concentration (NS&LVPC) compared to spherical thermal conductivity with effective viscosity particle concentration (S&EVPC) and nonspherical thermal conductivity with low viscosity particle concentration (NS&VPC).

Lithium-ion batteries are susceptible to thermal runaway during thermal abuse, potentially resulting in safety hazards such as fire and explosion. Therefore, it is crucial to investigate the internal thermal stability and characteristics of thermal runaway in battery pouch cells. This study focuses on dismantling a power lithium-ion battery, identified as Ni-rich LiNi_xCo_yMn_1-x-yO₂ (NCM811, LiNi_0.83Co_0.12Mn_0.05O₂) lithium-ion battery pouch cell through material characterization methods. The authors delve into the stability of the main component materials of lithium-ion cells and the mechanism of the thermal runaway induced by the cells. In addition, thermal runaway experiments are conducted under overheating conditions to analyze the effect of different states of charge (SOC) levels on battery cell temperature and gas changes. This information can serve as an active safety warning signal and allow for an extended window for passive safety measures. In conclusion, (i) uniform internal porosity facilitates efficient Li-ion diffusion. (ii) Thermal stability hierarchy: cathode > anode > separator. (iii) The elevated SOC levels expand risks, necessitating integrated monitoring of temperature, thermal ramp rate, and CO evolution for precise hazard alerts.

This work developed a macro-thermogravimetric analyzer (macro-TGA) of maximal 500 g sample capacity as the mini-pilot system to evaluate the thermochemistry process for industrial bulk samples, which is the first report about the lever-equal beam balance-based wide-range/variations (500 g) macro-TGA with high precision, excellent reliability and low cost. The macro-TGA adopts many novel designs to become one of the most advanced macro-TGA, including eight groups of reinforced parallel coils and magnetic cores as the electronic mass with enough force moment weighing 500 g mass, elaborately arranged blast fences and gas distribution tube with minimum disturbance from gas flow, as well as a all-sided ventilated ceramic crucible coupling a mobile thermocouple with minimal influence of mass/heat transfer from apparatus itself. As a typical application case, the thermal decomposition behaviors and apparent kinetics of industrial bulk limestone were evaluated by this macro-TGA. Compared with results from micro-TGA, the industrial bulk limestone in macro-TGA exhibits completely different thermal decomposition behaviors with obviously elevated initial thermal decomposition temperature, significantly prolonged thermal decomposition time as well as different apparent activation energies, which can be attributed to significant differences between bulk and fine limestone particles in terms of heat transfer resistance and inherent structure. The demonstrated wide-range/variations lever-equal beam balance-based macro-TGA with good reliability will become a powerful mini-pilot instrument to evaluate the thermal decomposition behaviors and apparent kinetics of industrial bulk samples with large mass/size.

In the present study, systems efficiency, heat transfer rate, exergy destruction, entropy generation number, exergetic efficiency, liquid fraction, and solidification temperature contours are determined for double-tube thermal energy storage (DT-TES) and triple-tube thermal energy storage (TT-TES) systems using MXene-based nano-enhanced phase changes material (NEPCM). The findings showed that the DT-TES using pure beeswax PCM in pure solidification has a discharge exergy 14.76% lower than that of MXene-based NEPCM. Using the TT-TES system, pure PCM and MXene NEPCM produced 2.47% and 3.62% less entropy at 2400 s than pure beeswax. Over 2400 s, DT-TES using pure beeswax discharged more effectively. Because of the superior thermophysical characteristics of MXene nanoparticles, the TT-TES system solidified beeswax PCM 18.53% faster than pure PCM. Consequently, under TT-TES latent heat, MXene-based nano-enhanced beeswax PCM solidifies more quickly per volume.

The fluororubber F2314 served as the binder; while, nano-LLM-105 acted as the insensitive agent. The DAP-4@LLM-105 energetic composites were prepared by coating with different contents of nanoscale LLM-105 using the solvent evaporation method to improve the safety performance of DAP-4. The morphologies, structures, and thermal properties of the DAP-4@LLM-105 energetic composites were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and differential scanning calorimetry (DSC), respectively. Additionally, the sensitivities were tested and analyzed. The combustion performance was also evaluated. The results show that the crystal structures of DAP-4 remain unchanged. When the content of LLM-105 is 20% by mass, the surface of DAP-4 can be uniformly and densely coated. Nanoscale LLM-105 could promote the thermal decomposition of DAP-4. The amount of nano-LLM-105 significantly influences the impact sensitivity and electrostatic sensitivity of DAP-4. As the content of nano-LLM-105 increases, the sensitivity of the DAP-4@LLM-105 micro-nano core–shell structures progressively decreases. Furthermore, the combustion time of the micro-nano core–shell structure is extended.

This study focuses on the prediction of the tensile modulus of polyethylene based on a previously developed empirical model. Just as in earlier works, the stiffness is estimated from the data evaluated from single calorimetric curves. During the work, three types of polyethylene (LDPE, MDPE and HDPE) were used. Calorimetry was employed to measure the degree of crystallinity and melting characteristics of the polymers investigated in this study, while the mechanical properties were evaluated through tensile tests conducted on standard-shaped specimens. The specimens intended for the tests were produced by injection molding and later annealed at various temperatures. The stiffness of the perfectly crystalline polymer was determined based on the propagation velocity of longitudinal sound waves in the material. The predicted and experimentally determined modulus values demonstrated a reasonably good agreement. These results verify the effectiveness of our prediction method in accurately estimating the tensile modulus of PE.

The study explores the use of neural networks to analyze the behavior of Prandtl nanofluid near an extending surface, considering crude oil as the base fluid and copper nanoparticles. It examines the combined effects of thermal and concentration gradients on fluid flow and heat transfer characteristics through advanced computational techniques. The research focuses on double diffusion in the flow of Prandtl nanofluid near a stretching surface (DD-PNSS), utilizing the Levenberg–Marquardt scheme with artificial neural networks (LMS-ANNs). By applying similarity variables, the nonlinear partial differential equations are transformed into nonlinear ordinary differential equations. Through the application of the Lobatto IIIa formula in a three-stage process, various data sets are generated for the LMS-ANNs by varying parameters such as the Prandtl fluid parameter ((alpha)), Prandtl number (Pr), Brownian motion parameter (Nb), thermophoresis parameter (Nt), and Dufour-solutal Lewis number (Ld). The proposed LMS-ANNs model is meticulously tested, validated, and trained using a multi-stage approach, and its performance is compared to established references to ensure its reliability. The effectiveness of the suggested LMS-ANNs model is further affirmed through regression analysis, Mean Squared Error (MSE) evaluation, and histogram studies, showcasing an exceptional accuracy level ranging from (1{0}^{-08}) to (1{0}^{-10}), setting it apart from alternative approaches and reference models. The study has application in optimizing heat and mass transfer processes. It is useful for catalytic reaction enhancement, energy transfer improvement in nanofluids, and efficient cooling system design. The growth of thermal management systems is supported by the incorporation of AI-driven algorithms, which provide accurate forecasts of heat and chemical transport phenomena under a variety of circumstances.