Search Results (547)

Oxygen diffusion in (Pu_xTh_1−x)O₂ mixed oxide crystals was investigated using molecular dynamics simulation. The model systems were isolated nanocrystals of 5460 and 15,960 particles, featuring a free surface. The oxygen diffusion coefficient D increased with decreasing thorium content, in accordance with the decrease in the melting temperature of (Pu_xTh_1−x)O₂ as x varied from 0 to 1. The temperature dependences D(T) exhibited non-linearity in the Arrhenius coordinates lnD = f(1/kT). The three linear segments of the plots corresponded to the superionic state, a transitional region, and the low-temperature crystalline phase. The transitional region was characterized by maximum values of the effective diffusion activation energy E_D(PuO₂) = 3.47 eV, E_D(ThO₂) = 5.24 eV and a complex collective mechanism of oxygen migration, which involved the displacement of anions into interstitial sites. At lower temperatures, an interstitialcy mechanism of oxygen diffusion was observed. The temperature dependence of D(PuO₂) showed quantitative agreement with low-temperature experimental data. Full article

Polyphenols are chemical compounds found in plants, and coffee is an important source of them. The objective of the study was to evaluate the kinetics of polyphenol degradation in a blend of specialty coffee (green, roasted and roasted–ground beans), packaged in eight different packages, under accelerated storage conditions. The samples were stored at 40, 50 and 60 °C for 12, 8 and 4 days, respectively. The degradation kinetics were modelled based on chemical kinetics and determination of the reaction order. Using the Arrhenius model, the rate constants (k) and activation energies (E_a) were estimated, which were then used to calculate and predict the half-life. The degradation followed zero-order kinetics. The rate constant (k) varied between 0.437 and 9.534 days⁻¹ (40–60 °C). The E_a ranged from 49.321 to 118.04 kJ*mol⁻¹. The average shelf life shows a direct correlation with the characteristics and barrier properties of the packaging, with the longest storage times for daily storage at 25 °C being for vacuum-packed green beans (27.16 months), vacuum-packed roasted beans (3.14 months) and roasted ground coffee in trilaminate foil with a valve (40.21 months). Polyphenol retention decreased significantly with increasing temperature. For green bean, roasted bean and roasted ground coffee, the packaging that showed the best protection for the coffee was vacuum packaging and trilaminate with valve respectively, being crucial for preserving these bioactive compounds. Full article

This work investigates a comprehensive temperature-dependent photoluminescence (PL) study (7–300 K) of β-Ga₂O₃ single crystals under 250 nm excitation. The emission consists of three competing bands at ~3.55 eV (J₁), ~3.37 eV (J₂), and ~3.07 eV (J₃), exhibiting a redshift, band broadening, and a crossover near ~140 K with increasing temperature. The novelty of this study lies in the first quantitative investigation of the temperature-dependent photoluminescence of undoped β-Ga₂O₃ single crystals, revealing activation, trap-release, and phonon-coupling parameters that define the competition between STE (Self-trapped exciton)- and DAP-related emission channels. A two-channel Arrhenius analysis of global thermal quenching at E_max (at maximum PL), J₁, and J₂ reveals a common shallow barrier (E₁ = 7–12 meV) alongside deeper, band-specific barriers (E₂ = 27 meV for J₁ and 125 meV for J₂). The J₃ band shows non-monotonic intensity (dip–peak–quench) reproduced by a trap-assisted generation model with a release energy E_rel = 50 meV. Linewidth analysis yields effective phonon energies (E_ph ≈ 40–46 meV), indicating strong electron–phonon coupling and a transition to multi-phonon broadening at higher temperatures. These results establish a coherent picture of thermally driven redistribution from near-edge STE-like states to deeper defect centers and provide quantitative targets (activation and phonon energies) for defect engineering in β-Ga₂O₃-based optoelectronic and scintillation materials. Full article

The rational design of advanced composite gels requires rigorous rheological analysis to decode their flow-deformation mechanisms, a prerequisite for optimizing performance in food and biomedical applications. However, systematic thermal analysis and rheological profiling of Salecan/Sanxan hydrogels remain unexplored, constituting a critical knowledge gap in this field. This study engineered Salecan/Sanxan hydrogels and systematically probed Salecan-dependent rheological and thermal properties. Through Power Law and Herschel–Bulkley model analyses, the hydrogels demonstrated composition-dependent rheological properties: yield stress (4.7–29.2 Pa), η₅₀ (342.6–3011.4 mPa·s), and Arrhenius equation fitting revealed tunable activation energy (14,688.3–30,997.1 J·mol⁻¹). Notably, when the gel was formulated with 3% Sanxan and 2% Salecan at a volume ratio of 1:2, its thermal-decomposition temperature rose by 9%, from 224.4 °C to 245.1 °C. Conversely, a 1:1 mixture of 2% Sanxan and 2% Salecan produced the lowest freezing point recorded (–18.3 °C), an 18% reduction compared with the control (–15.4 °C). These findings demonstrate the tunable rheological and thermal properties of Salecan/Sanxan hydrogels. By establishing that precise modulation of polymer mixing ratios can match the entire processing shear spectrum, this study not only fills a critical knowledge gap but also creates a versatile platform for designing tailor-made foods and biomedical matrices. Full article

This study develops a multi-stage, Arrhenius-type reaction rate model for exothermic heat release during thermal runaway (TR) that depends on the local active material temperature, T_Cell, and the remaining reactant fraction, Y. Model parameters are identified from an accelerating rate calorimetry (ARC) test on an NMC721 pouch cell. Validation across other cell formats (cylindric and prismatic) and cathode chemistries (LCO, LMO, NCA, LFP) is left for future work. Model performance is evaluated in a 3D CFD (AVL FIRE™ M 2021.2) representation of the ARC assembly and benchmarked against Gaussian and polynomial one-step TR formulations that depend solely on T_Cell. The three TR models are further applied to a generic 4S4P pouch cell module under stagnant and actively cooled conditions to assess thermal propagation. In the ARC test, the Arrhenius-type model shows improved agreement with measured cell skin temperatures for the NMC721 cell; in the 4S4P module, it exhibits a trend toward higher thermal propagation rates relative to the Gaussian and polynomial models. Full article

Biofuels represent a sustainable alternative that supports global energy development without compromising environmental balance. This work introduces a novel hardware–software platform for the experimental characterization of biomass solid yield during the slow pyrolysis process, integrating physical experimentation with advanced computational modeling. The hardware consists of a custom-designed pyrolizer equipped with temperature and weight sensors, a dedicated control unit, and a user-friendly interface. On the software side, a two-step kinetic model was implemented and coupled with three optimization algorithms, i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Nelder–Mead (N-M), to estimate the Arrhenius kinetic parameters governing biomass degradation. Slow pyrolysis experiments were performed on wheat straw (WS), pruning waste (PW), and biosolids (BS) at a heating rate of 20 °C/min within 250–500 °C, with a 120 min residence time favoring biochar production. The comparative analysis shows that the N-M method achieved the highest accuracy (100% fit in estimating solid yield), with a convergence time of 4.282 min, while GA converged faster (1.675 min), with a fit of 99.972%, and PSO had the slowest convergence time at 6.409 min and a fit of 99.943%. These results highlight both the versatility of the system and the potential of optimization techniques to provide accurate predictive models of biomass decomposition as a function of time and temperature. Overall, the main contributions of this work are the development of a low-cost, custom MATLAB-based experimental platform and the tailored implementation of optimization algorithms for kinetic parameter estimation across different biomasses, together providing a robust framework for biomass pyrolysis characterization. Full article

Sintered nano-silver paste is widely used in electronic packaging due to its excellent thermal and electrical conductivity. A phenomenological variable-order fractional constitutive model has been developed to characterize the evolution of its mechanical properties, incorporating dependencies on both temperature and strain rate. Based on the Weissenberg number and classical Arrhenius equation, a formulation for relaxation time with temperature and strain rate dependence has been proposed. A temperature- and rate-sensitive fractional order is introduced to capture the coupled influences of thermal and strain rate effects. Furthermore, the effects of temperature and the strain rate on the elastic modulus and relaxation time are quantitatively described through established coupling criteria. Simulation results demonstrate that the proposed model offers high accuracy and strong predictive capability. Comparisons with the classical Anand model highlight the effectiveness of the variable-order fractional model, particularly at lower temperatures. Full article

The current study explores the applicability of a single constitutive equation, based on the Arrhenius hyperbolic sine model, to a wide range of chemical compositions and test conditions by using a unique approximation. To address this challenge, a mixed model is proposed, integrating a physical model with phenomenological expressions to capture the strain and strain rate hardening, forming temperature, dynamic recovery (DRV) and dynamic recrystallization (DRX). The investigation combines high-temperature mechanical testing with modeling in order to understand the hot deformation mechanisms. Hot torsion tests were conducted on ten different low-carbon steels with distinct microalloying additions to capture their responses under diverse initial austenite grain sizes, deformation temperatures and strain rate conditions (d₀ = 22–850 µm, T = 800–1200 °C and $\dot{\epsilon }$= 0.1–10 s⁻¹). The developed constitutive equation has resulted in a robust expression that effectively simulates the hot behavior of various alloys across a wide range of conditions. The application of an optimization tool has significantly reduced the need for adjustments across different alloys, temperatures and strain rates, showcasing its versatility and effectiveness in predicting the flow behavior in a variety of scenarios with excellent accuracy. Moreover, the model has been validated with experimental torsion data from the literature, enhancing the applicability of the developed expression to a broader spectrum of chemical compositions. Full article

Xylenes are important components of gasoline fuels, and their hydrogen abstraction reactions are crucial in the consumption pathways of combustion processes. In existing models, rate constants for these reactions are commonly derived by estimation, which can introduce large uncertainties into models and lead to prediction deviations. In this study, the hydrogen abstraction reactions of three xylene isomers (p-xylene, m-xylene, and o-xylene) with hydrogen and hydroxyl radicals were investigated using quantum chemical methods. The high-precision CBS-QB3 method was used to perform a series of calculations, including structure optimization, frequency analysis, and energy calculations. Rate constants for all reactions were obtained using transition state theory with tunneling corrections and fitted to the three-parameter Arrhenius expression. The kinetic parameters of these reactions were updated in existing models of xylene. The integration of the updated rate constants into combustion models generally improves predictive accuracy, particularly for ignition delay times, CO₂ formation, and laminar flame speeds, although discrepancies remain for some species such as CO. Full article

The hot deformation discipline of typical 45CrNi steel under a strain rate ranging from 0.01 s⁻¹ to 1 s⁻¹ and deformation temperature between 850 °C and 1200 °C was investigated through isothermal hot compression tests. The activation energy involved in the high-temperature deformation process was determined to be 361.20 kJ·mol⁻¹, and a strain-compensated constitutive model, together with dynamic recrystallization (DRX) kinetic models, was successfully established based on the Arrhenius theory. An improved second-phase (SP) cellular automaton (CA) model considering the influence of the pinning effect induced by SP particles on the DRX process was developed, and the established SP-CA model was further utilized to predict the evolution behavior of parent austenite grain in regard to the studied 45CrNi steel. Results show that the average absolute relative error (AARE) associated with the austenite grain size and the DRX volume fraction achieved through the simulation and experiment was overall below 5%, indicating good agreement between the simulation and experiment. The pinning force intensity could be controlled by regulating the size and volume fraction of SP particles involved in the established SP-CA model, and the DRX behavior and the average grain size of the studied 45CrNi steel treated by high-temperature compression could also be predicted. The established SP-CA model exhibits significant potential for universality and is expected to provide a powerful simulation tool and theoretical foundation for gaining deeper insights into the microstructural evolution of metals or alloys during high-temperature deformation. Full article

To investigate the hot deformation behavior of nitrogen-containing 2Cr13 (2Cr13N) corrosion-resistant plastic mold steel, uniaxial compression tests were conducted at temperatures ranging from 850 to 1200 °C and strain rates between 0.01 and 10 s⁻¹. The results indicate that the flow stress exhibits pronounced peak characteristics under conditions of low strain rate and high temperature, with peak stress decreasing as deformation temperature increases and strain rate decreases. Using the Arrhenius model, a hot deformation equation was established, and activation energy for deformation was 454.85 kJ/mol. The processing diagram was constructed based on the dynamic material model (DMM) theory. The optimal hot working window was at 1050–1150 °C with a strain rate less than 0.05 s⁻¹ and at 1150–1200 °C with a strain rate greater than 2 s⁻¹, with excellent efficiency of power dissipation (η > 0.32) and lower values of Kernel Average misorientation (KAM) (1.2386 and 1.3095, respectively). Full article

In the polyurethane industry, catalytically generated carbodiimides can modify the properties of isocyanate and, thus, the resulting foams. In this work, a kinetic reaction study was carried out to investigate the formation of a simple, bifunctional carbodiimide from a widely used polyurethane raw material: 4,4′-methylenediphenyl diisocyanate (MDI). The experimental section outlines a catalytic process, using a 3-methyl-1-phenyl-2-phospholene-1-oxide (MPPO) catalyst in ortho-dichlorobenzene (ODCB) solvent, to model industrial circumstances. The reaction produces carbon dioxide, which was observed using gas volumetry at between 50 and 80 °C to obtain kinetic data. A detailed regression analysis with linear and novel nonlinear fits showed that the initial stage of the reaction is second-order, and the temperature dependence of the rate constant is $k(T)=(3.4\pm 3.8)\mathrm{‧}{10}^6\cdot e^{{\displaystyle \frac{-7192\pm 389}{T}}}$. However, the other isocyanate group of MDI reacts with new isocyanate groups and the reaction deviates from the second-order due to oligomer (polycarbodiimide) formation and other side reactions. A linearized Arrhenius equation was used to determine the activation energy of the reaction, which was E_a = 60.4 ± 3.0 kJ mol⁻¹ at the applied temperature range, differing by only 4.6 kJ mol⁻¹ from a monoisocyanate-based carbodiimide. In addition to experimental results, computationally derived thermochemical data (from simplified DFT and IRC calculations) were applied in transition state theory (TST) for a comprehensive prediction of rate constants and Arrhenius parameters. As a result, it was found that the activation energy of the carbodiimide bond formation reaction from theoretical and experimental results was independent of the number and position of isocyanate groups, which is consistent with the principle of equal reactivity of functional groups. Full article

This paper presents the results of determining the parameters of tritium transfer processes in lithium ceramics Li₂TiO₃ under reactor irradiation conditions. Analysis of sections with a short-term decrease in reactor power allowed numerical determination of the Arrhenius parameters of tritium diffusion (pre-exponential factor and activation energy) based on comparison with in situ experimental data. The obtained values of activation energy (70.2–74.7 kJ/mol) and pre-exponential factor (0.9–2.1 × 10⁻⁸m²/s) demonstrate growth with increasing fluence, which is explained by the accumulation of radiation defects in ceramics. A linear dependence was established between D₀ and E_a, corresponding to the Mayer–Noldel rule. Unlike previously conducted studies based on a phenomenological approach to assessing only the activation energy of diffusion, in this study, a complex model that takes into account temperature gradients, tritium generation, its diffusion, and release from the surface was used. The applicability of such an integrated approach to the analysis of in situ reactor experiments with lithium ceramics was confirmed, and allowed us to estimate changes in the tritium transfer parameters in lithium ceramics Li₂TiO₃ depending on the irradiation time. Full article

The thermal deformation behaviour of a spray-formed 7055 as-forged aluminium alloy was studied using isothermal hot-press tests under different deformation conditions (strain rates of 0.01, 0.1, 1, and 10 s⁻¹, temperatures of 340, 370, 400, 430, and 460 °C). An Arrhenius constitutive model was developed using flow stress data corrected for friction and temperature, yielding a correlation coefficient (R) of 0.9877, an average absolute relative error (AARE) of 4.491%, and a deformation activation energy (Q) of 117.853 kJ/mol. Processing maps integrating instability criteria and power dissipation efficiency identified appropriate processing parameters at 400–460 °C/0.08–0.37 s⁻¹. Furthermore, this study investigated how strain rate and temperature influence microstructural evolution. Microstructural characterization revealed that both dynamic recovery (DRV) and dynamic recrystallization (DRX) occur simultaneously during thermal deformation. At low temperatures (≤400 °C), DRV and continuous dynamic recrystallization (CDRX) dominated; at 430 °C, deformation microstructures and recrystallized grains coexisted, whereas abnormal grain growth prevailed at 460 °C. The prevailing mechanism of dynamic softening was influenced by the applied strain rate. At lower strain rates (≤0.1 s⁻¹), discontinuous dynamic recrystallization (DDRX) was the primary mechanism, whereas CDRX became dominant at higher strain rates (≥1 s⁻¹), and dislocation density gradients developed within adiabatic shear bands at 10 s⁻¹. Full article

Ensuring the safety, reliability, and longevity of Lithium-ion (Li-ion) batteries is crucial for sustainable integration of Electric Vehicles (EVs) within Intelligent Transportation Systems (ITSs). However, thermal stress and degradation-induced anomalies can cause sudden performance failures, posing critical operational and safety risks. Capturing complex, non-linear, and high-dimensional patterns remains challenging for traditional Machine Learning (ML) models. We propose a hybrid anomaly detection method that incorporates a Variational Quantum Neural Network (VQNN), which uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to learn complex non-linear patterns. The VQNN is integrated with Isolation Forest (IF) and a Median Absolute Deviation (MAD)-based spike characterisation method to form a Quantum Anomaly Detector (QAD). This method distinguishes between normal and anomalous spikes in battery behaviour. Using an Arrhenius-based model, we simulate how the State of Health (SoH) and voltage of a Li-ion battery reduce as temperatures increase. We perform experiments on NASA battery datasets and detect abnormal spikes in 14 out of 168 cycles, corresponding to $8.3$% of the cycles. The QAD achieves the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.9820$, outperforming the baseline IF model by $7.78\%$. We use ML to predict the SoH and voltage changes when the temperature varies. Gradient Boosting (GB) achieves a voltage Mean Squared Error (MSE) of $0.001425$, while Support Vector Regression (SVR) achieves the highest $R^2$ score of $0.9343$. These results demonstrate that Quantum Machine Learning (QML) can be applied for anomaly detection in Battery Management Systems (BMSs) within intelligent transportation ecosystems and could enable EVs to autonomously adapt their routing and schedule preventative maintenance. With these capabilities, safety will be improved, downtime minimised, and public confidence in sustainable transport technologies increased. Full article