Thermochimica Acta最新文献

In this work, the pyrolysis behaviors and kinetics of two bamboo (Bambusa texlitis) wastes were investigated via thermogravimetric analysis. The weight loss behaviors of pyrolysis were studied by curvature of the conversion (α) curves. The curvature of α can depict the characteristic temperatures of pseudo component (hemicellulose, cellulose and lignin) pyrolysis. Model-free model was applied to estimate the apparent activation energies of the pure components for comparison with other kinetic models. Three parallel reaction (TPR) and parallel finite number first-order reaction (FNFOR) models were used for kinetic studies. The first-order TPR model underestimates the apparent activation energies of pseudo components. The nth-order TPR model underestimates the apparent activation energy of lignin and obtains characteristic temperatures that are inconsistent with those of pure lignin. The parallel FNFOR model is more reliable for simulating the kinetics of lignocellulosic biomass pyrolysis. The apparent activation energies of pseudo components depend on the variety of lignocellulosic biomass.

Non-isothermal thermogravimetry was used to study the thermal decomposition of the nifedipine drug. The dominant process of thermal molecular degradation (as confirmed by Raman spectroscopy) starts very slowly at ∼ 150 °C, which is below the melting point of T_m ≈ 170 °C. The decomposition kinetics was described in terms of the autocatalytic Šesták-Berggren kinetics; a novel approach denoted as single-curve multivariate kinetic analysis (sc-MKA) was used to determine the following set of parameters: activation energy of decomposition E_d = 115.5 ± 2.4 kJ mol^-1, decomposition pre-exponential factor log(A_d/s^-1) ≈ 8.5–8.9, Šesták-Berggren kinetic exponents M ≈ 0–0.2 and N ≈ 0.3–0.5. The consequent kinetic predictions based on these results have confirmed the good thermal stability of nifedipine, which allows for the melt-quenching preparation of the amorphous phase as well as for the processing via hot melt extrusion and 3D printing. An in-depth analysis of the relevant performance of the sc-MKA approach was done based on the akin literature data for indomethacin, nimesulide, and griseofulvin. The comparison has revealed more than sufficient performance of the sc-MKA method with regard to its application to the thermal decomposition data of active pharmaceutical substances. The most critical aspect of the sc-MKA procedure was demonstrated to be the accurate determination of the model-free parameters (activation energy E_d and pre-exponential factor A_d) and their temperature trends. Considering the specificity of the sc-MKA method, i.e., the fixed value of E_d(T), the determination of the temperature dependence of A_d(T) is of utmost importance and worth extensive repeatability checks.

This study investigated the kinetics of the multistep thermal dehydration/decomposition of the metakaolin-based geopolymer paste. The component two reaction steps were characterized by the evolution of water vapor and the simultaneous evolution of water vapor and CO₂, respectively. In a stream of dry N₂, the kinetics of the first and second reaction steps were characterized by the apparent activation energy (E_a) values of 92 and 166 kJ mol⁻¹, respectively. Both reaction steps exhibited a diffusion-controlled rate behavior. In a stream of wet N₂, the mass loss curves systematically shifted to higher temperatures with an increase in the water vapor pressure (p(H₂O)). The first reaction step was significantly influenced by p(H₂O), and the apparent E_a increased to 175 kJ mol⁻¹ at p(H₂O) = 11.4 kPa. The second reaction step was less sensitive to the atmospheric water vapor, as characterized by its E_a of ∼165 kJ mol⁻¹, irrespective of the p(H₂O).

The dynamic thermogravimetric analysis of a new series of aliphatic, cycloaliphatic, and aromatic trimellitimide-based epoxy-imide (EI) resins, compared to diglycidyl ether of bisphenol-A (DGEBA) and triglycidyl isocyanurate (TGIC) resins, in uncured and cured states, revealed that the EI resins/ systems, unlike DGEBA ones, have a multistep thermal degradation process with two chief steps corresponding to the degradation of the DGEBA and the imide moieties. Evaluating the influences of isothermal curing temperature (T_Cure) on their degradation behavior ascertained that, generally, the 1^st stage of the thermal degradation processes is more affected by T_Cure. Furthermore, the competition between epoxide-amine reactions with etherification/ homopolymerization plays the most crucial role in the impact of T_Cure on the systems' stability and decomposition behavior. Therefore, the most considerable changes can be observed in the thermograms of the cycloaliphatic system due to the highest difference between the melting temperature of its resin and T_Cure.

This study introduces a model-based technique to evaluate the effective pre-exponential factor, conversion function and rate constant of reactions using differential isoconversional methods. In complex reactions, the effective pre-exponential factor depends on conversion and temperature and can be calculated using the modified Friedman method and compensation effect relationship. The innovative approach facilitates the computation of how pre-exponential factor varies with temperature across diverse heating rates, providing a valuable improvement in isoconversional analysis. The calculations revealed that the effective rate constant might be solely influenced by temperature, irrespective of the degree of conversion. The approach suggested in this research can also be applied in conjunction with the results of the traditional Friedman method. The technique was validated by applying it to kinetic data from various simulated reactions and experimental data on the thermal degradation of polyethylene.

In this study, we investigate the melting behavior and crystallization of nanocomposites of poly(ε-caprolactone) (PCL), a biodegradable polymer, with pristine graphene, an economically feasible filler widely available from natural sources. Nanocomposites with pristine graphene loads between 0.01 and 5 wt% were prepared via solvent casting and primarily probed by Differential Scanning Calorimetry (DSC). Conventional DSC shows that the presence of graphene increases PCL crystallinity. Non-isothermal crystallization was studied using Mo's model, whereas other parameters as Activation Energy and Nucleation Activity were obtained. It is shown that graphene increases crystallization rates acting as nucleant, with no signatures of retardant effects. Compared with other classic nano-loads, like ad-hoc modified bentonite also analyzed for comparison, pristine graphene is more effective as nucleant, which indicates that it is better dispersed in PCL. Analysis by Self Successive Annealing (SSA), also carried out by DSC, reveals that graphene hinders the formation of crystals with lamellar thickness above 7.3 nm, as found in regular PCL. It may indicate that molecular interactions between PCL and pristine graphene disrupts the movement of polymeric chains, consequently limiting lamellar growth. Evidence of such interaction is found by Infrared and Raman spectroscopies that reveal broadening of the carbonyl peak of PCL and alteration of G and D' bands of graphene.

The solubility of 3, 4-O-isopropylidene clindamycin (OIC) was determined in twelve solvents at p = 101.3 kPa with the temperature from 273.15 K to 313.15 K. The solubility of OIC increases with the raise temperature. Among the selected solvents, 2-butanol showed the best dissolving ability for OIC, while acetonitrile showed the worst dissolving ability. The obtained data are neatly correlated with five models, including van't Hoff, Yaws, λh, Wilson, and nonrandom two-liquid interaction model (NRTL), with Yaws equation yielding the most accurate predicted results. Moreover, the Conductor-like Screening Model for Real solvents (COSMO-RS) provides better calculated results for the protic solvents than the aprotic ones except for acetonitrile. The dissolving mechanism of OIC was investigated with intermolecular interaction between OIC and solvents, and the analyses indicate that the dissolving process is affected by intermolecular interaction, molecular shape, and size of both solvents and the solute. The apparent thermodynamics analysis shows that the dissolving process of OIC before reaching equilibrium in the twelve solvents is spontaneous, endothermic, and enthalpy driven.

The thermal behavior of ultra-high molecular weight polyethylene (UHMWPE) with dioctyl adipate (DOA) was investigated by the differential scanning calorimetry (DSC) method. Capillary-porous bodies were obtained via thermally induced phase separation of DOA mixtures with polyethylenes of different molecular weight at different cooling rates. Analysis of the morphology of the prepared porous bodies together with DSC data and results of cloud point estimation revealed an unexpected effect of a cooling rate on the type of phase separation occurring in the mixture during its cooling from homogeneous state. Particularly it was shown that increase in the cooling rate reduces the probability of realization of liquid – liquid phase separation in the mixture. It was argued that such behavior, different from the standard semicrystalline polymers, is a result of kinetic hindrance of liquid – liquid phase separation in mixtures containing a polymer with such a high molecular weight.

Duplex RNA stabilization is important for the application in the artificial silencing of gene expression. Excess usage of cationic molecules with low binding affinity to duplex RNA may cause cytotoxicity due to nonspecific binding. Cationic molecules specifically binding to duplex RNA with high binding affinity are necessary for duplex RNA stabilization. Here, UV melting and isothermal titration calorimetric analyses revealed that our previously designed cationic oligodiaminogalactose 4 mer (ODAGal4) specifically stabilized duplex RNA by binding to it with approximately 10⁵ M⁻¹ binding constant, without binding to duplex DNA. Temperature dependence of thermodynamic parameters, including negative enthalpy and entropy changes, revealed that the magnitude of negative heat capacity change was quite large for small molecules binding to duplex RNA, suggesting the influence of the hydrophobic effect on the binding process. Our results suggest the therapeutic application of ODAGal4 as a key molecule for duplex RNA-binding specificity to prevent any nonspecific binding.

Nanofluids are considered as excellent coolants to optimize thermal management of electronic devices, where the nanoparticle morphology and the addition of surfactants can affect the thermal transport performance of nanofluids. Due to the limitations of high economic and computational cost in previous experimental and numerical simulation methods, the design of nanofluids urges for more efficient approaches. In this work, a novel machine learning framework coupled with molecular dynamics methods was proposed to model the multi-component mixing nanofluidic systems and explore the deep heat transfer mechanisms. Multi-input attribute point cloud dataset, dual channel sampling network and multi-nanoscale optimization scheme were used to improve the prediction performance of machine learning. The computational cost of the machine learning method is shortened by 36000 times compared with simulation methods. Moreover, our work can achieve up to 90 % prediction accuracy for surfactant adsorption properties. Furthermore, algorithm optimization strategy can improve the prediction accuracy of nanofluidic heat transfer performance by 40 %. The proposed framework has the potential to shorten the development cycle of nanofluidic design.