Increasing use of rock materials like shale in building, roofing, embankment filling, brick manufacturing, and in other civil structure application makes it an important rock to consider in construction engineering. Knowledge of thermal and physical properties of shale as building material is required to predict the rock's strength and permanence against weathering. Inconsistent heat capacity of anisotropic rock can result in differential heat flow. This tendency can expand the building materials leading to reduction in strength and initiate disintegration. Authors have studied various thermo-physical properties of anisotropic shale from Tennessee, which is commonly used as building stones and bricks. Experiment was designed to measure the basic thermal property, 'heat capacity' of shale. Series of laboratory tests including durability, strength, specific gravity, moisture content, and porosity were conducted to determine the physical and mechanical behavior of the samples. Results indicated that properties like porosity, strength and heat capacity varied significantly within samples, where as specific gravity and moisture content yielded steady values. Multivariate regression analysis was performed to evaluate possible correlations among the tested properties. Strong positive relationship was evident between heat capacity, and porosity. Heat capacity and Unconfined Compressive Strength of shale were inversely related. This study emphasized that physical and thermal properties of shale are directly linked with strength and durability of the rock mass.

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Chemical substances are essential to all aspects of human life, and understanding their properties is essential for effective application. The properties of chemical species are usually measured by experimentation or computational calculation using theoretical methods. In this work, machine learning models (ML) for predicting entropy, S, and heat capacity, cp, were developed for alkanes, alkenes, and alkynes at 298.15 K. The data for entropy and heat capacity were collected from various sources. Commercial software (alvaDesc) then generated the molecular descriptors of all the hydrocarbons in the dataset used as input for the ML models. Support vector regression (SVR), v-support vector regression (v-SVR), and random forest regression (RFR) algorithms were trained with K-fold cross-validation on two levels. The first level assessed the models’ performance and the second level generated the final models. After a performance comparison of the three models, the SVR was chosen. To illustrate the advantage of using the ML approach, the SVR model was compared against Benson’s group additivity. Finally, a sensitivity analysis was performed.

Les saumures naturelles sont des ressources en eau de plus en plus convoitées et utilisées par les industriels, que ce soit pour la production d’eau potable (dessalement de l’eau de mer…) ou pour la récupération de substances valorisables (le lithium, le potassium, le magnésium, la silice …). Les saumures industrielles sont aussi souvent utilisées dans différents procédés comme fluides caloporteurs ou lors d’extraction de minerai (phosphore, alumine…). Cependant, ces solutions aqueuses complexes présentent des propriétés thermodynamiques qui s’écartent de celles des solutions aqueuses diluées (dites idéales). Des approches de calcul spécifiques sont alors nécessaires pour pouvoir déterminer ces propriétés. Cette étude s’intéresse au calcul des propriétés thermodynamiques d’excès (coefficient osmotique, capacité calorifique, densité …) de ces systèmes. Celles-ci dépendent toutes de la dérivée de l’énergie libre de Gibbs d’excès (G^ex) par rapport à la concentration en sels dissous, à la température ou à la pression. Suite à une revue bibliographique des différents modèles thermodynamiques permettant de calculer l’énergie libre de Gibbs d’excès, le modèle de Pitzer a été sélectionné pour décrire les propriétés d’excès d’un système contenant c cations, a anions et n espèces neutres. Les propriétés thermiques et volumiques ont été, dans un premier temps, établies pour un système contenant des espèces neutres avant d’être implémentées dans le logiciel PhreeqC, logiciel de géochimie qui permettait déjà le calcul du coefficient osmotique, de l’activité de l’eau et du coefficient d’activité. Le logiciel issu de cette modification, PhreeSCALE, permet désormais, lorsque les paramètres d’interaction de Pitzer sont connus, de calculer les propriétés d’excès telles que le coefficient osmotique, la capacité calorifique ou la densité d’une saumure en tenant compte de la spéciation exacte de la solution. Dans le cas où les paramètres d’interaction sont à déterminer, PhreeSCALE peut être couplé à des logiciels d’optimisations pour établir de nouveaux jeux de paramètres, calés sur les propriétés mesurées des solutions. Les applications de cette étude s’appuient sur plusieurs systèmes qui sont soit des saumures industrielles, soit des saumures naturelles. Le système NaOH-H2O a été sélectionné en raison des salinités élevées dans l’eau (jusqu’à 29 mol.kgw-1 à 25°C). Pour représenter au mieux l’ensemble des propriétés sur toute la gamme de concentrations, la dissociation partielle de l’espèce NaOH a dû être prise en compte. Les autres systèmes étudiés sont des saumures chlorurées, plus caractéristiques des saumures naturelles. Une approche par étape a permis d’établir les paramètres d’interaction pour cinq systèmes binaires (NaCl, KCl, CaCl2, MgCl2 et BaCl2). Puis, des systèmes ternaires et un système quinquénaire composés de ces cinq électrolytes, ont été étudiés. Dans chaque cas, la capacité calorifique et la densité ont été déterminées. Finalement des abaques, tenant compte des conditions de température et de pression, ont pu être tracées pour le système NaCl-H2O. / Natural brines are water resources that are increasingly sought and used by industrialists both to produce drinking water (e.g. seawater desalinisation) or retrieve economically exploitable substances (lithium, potassium, magnesium, silica, etc.). Industrial brines are often used in various processes as coolants or in ore processing (phosphorus, alumina, etc.). However, the thermodynamic properties of these complex aqueous solutions differ somewhat from those of so-called "ideal" diluted aqueous solutions. Specific calculation methods must therefore be used to determine these properties. This study focuses on calculating the thermodynamic excess properties of these systems (osmotic coefficient, heat capacity, density, etc.). All of these depend on the derivative of the excess Gibbs free energy (G^ex) in relation to the concentration of dissolved salt, temperature or pressure. A literature survey of thermodynamic models capable of calculating excess Gibbs free energy was done and the Pitzer model was chosen to describe the excess properties of a system containing c cations, a anions and n neutral species. Thermal and volumetric properties were determined for a system containing neutral species and these were then added to PhreeqC, a geochemical model that makes it possible to calculate the osmotic coefficient, water activity, and the activity coefficient. The resulting model, PhreeSCALE, now makes it possible, when the Pitzer interaction parameters are known, to calculate excess properties such as the osmotic coefficient, the heat capacity, and the density of a brine, taking into account the precise speciation of the solution. If the interaction parameters must be determined, PhreeSCALE can be coupled with optimisation software to determine new parameter sets based on properties measured in solution. The applications of this study are based on several systems that are either industrial or natural brines. The NaOH-H2O system was chosen because of its high salinities in water (up to 29 mol.kgw-1 at 25 °C). To best represent all of the properties over the entire range of concentrations, the partial dissociation of the NaOH species had to be considered. The other systems studied are chloride brines, which are more like natural brines. A multi-step approach made it possible to determine the interaction parameters for five binary systems (NaCl, KCl, CaCl2, MgCl2, and BaCl2). Ternary systems and one quinary system made up of all five electrolytes were then studied. In each case, the heat capacity and the density were determined. Charts taking into account temperature and pressure conditions were drawn for the NaCl-H2O system.

Vapor pressure (Pvap), heat of vaporization (ΔHvap), liquid heat capacity (Cpl), and ideal gas heat capacity (Cpig) are important properties for process design and optimization. This work focuses on improving the thermodynamic consistency and accuracy of the aforementioned properties since these can drastically affect the reliability, safety, and profitability of chemical processes. They can be measured for pure organic compounds from the triple point, through the normal boiling point, and up to the critical point. Additionally, ΔHvap is proportional to the derivative of vapor pressure with respect to temperature through the Clapeyron equation, and the difference between Cpl and Cpig is proportional to the derivative of heat of vaporization with respect to temperature. In order to improve temperature-dependent correlations, all the properties were analyzed simultaneously. First, a temperature-dependent error model was developed using several versions of the Riedel and Wagner Pvap correlations. The ability of each correlation to match Cpl data was determined for 5 well-known compounds. The Riedel equation performed better than the Wagner equation when the best form was used. Second, the Riedel equation form was further modified, and the best correlation form was found for about 50 compounds over 7 families. This led to the development of a new vapor pressure prediction method using different Riedel equation forms to fit Pvap, ΔHvap, and Cpl data simultaneously. Seventy compounds were tested, and the error compared to liquid heat capacity data dropped from 10% with previous methods to 3% with this new prediction method. Additionally a differential scanning calorimeter (DSC) was purchased, and melting points (Tm), enthalpies of fusion (ΔHfus), and liquid heat capacities (Cpl) were measured for over twenty compounds. For many of these compounds, the vapor pressure data and critical constants were re-evaluated, and new vapor pressure correlations were recommended that were thermodynamically consistent with measured liquid heat capacity data. The Design Institute for Physical Properties (DIPPR) recommends best constants and temperature-dependent values for pure compounds. These improvements were added to DIPPR procedures, and over 200 compounds were re-analyzed so that the temperature-dependent correlations for Pvap, ΔHvap, Cpig, and Cpl became more internally consistent. Recommendations were made for the calculation procedures of these properties for the DIPPR database.

Low-temperature heat capacity data contain information on the physical properties of materials, and new models continue to be developed to aid in the analysis and interpretation of heat capacity data into physically meaningful properties. This work presents the development of two such models and their application to real material systems. Equations describing low-energy vibrational modes with a gap in the density of states (DOS) have been derived and tested on several material systems with known gaps in the DOS, and the origins of such gaps in the DOS are presented. Lattice vacancies have been shown to produce a two-level system that can be modeled with a sum of low-energy Schottky anomalies that produce an overall linear dependence on temperature in the low-temperature heat capacity data. These two models for gaps in the vibrational DOS and the relationship between a linear heat capacity and lattice vacancies and many well-known models have been applied to several systems of materials to test their validity and applicability as well as provide greater information on the systems themselves. A series of bulk and nanoscale Mn-Fe and Co-Fe spinel solid solutions were analyzed using the entropies derived from heat capacity data, and excess entropies of mixing were determined. These entropies show that changes in valence, cation distribution, bonding, and the microstructure between the mixing ions is non-ideal, especially in the nanoparticles. The heat capacity data of ten Al doped TiO2 anatase nanoparticle samples have also been analyzed to show that the Al3+ dopant ions form small regions of short-range order, similar to a glass, within the TiO2 particles, while the overall structure of TiO2 remains unchanged. This has been supported by X-ray diffraction (XRD) and electron energy-loss spectroscopy and provides new insights to the synthesis and characterization of doped materials. The final investigation examines nanocrystalline CuO using heat capacities, magnetization, XRD, and electron microscopy and compares the findings to the known properties of bulk CuO. All of these measurements show transitions between antiferromagnetic and paramagnetic states in the temperature range of about 150-350 K that are greater in number and higher in temperature than the transitions in bulk CuO. These changes are shown to cause an increase in the temperature range of multiferroicity in CuO nanoparticles.

The objective of this study is to synthesize nanomaterials by mixing molten salt (alkali nitrate salt eutectics) with inorganic nanoparticles. The thermo-physical properties of the synthesized nanomaterials were characterized experimentally. Experimental results allude to the existence of a distinct compressed phase even for the solid phase (i.e., in the nanocomposite samples). For example, the specific heat capacity of the nanocomposites was observed to be enhanced after melting and re-solidification - immediately after their synthesis; than those of the nanocomposites that were not subjected to melting and re-solidification. This shows that melting and re-solidification induced molecular reordering (i.e., formation of a compressed phase on the nanoparticle surface) even in the solid phase - leading to enhancement in the specific heat capacity. Numerical models (using analytical and computational approaches) were developed to simulate the fundamental transport mechanisms and the energy storage mechanisms responsible for the observed enhancements in the thermo-physical properties. In this study, a simple analytical model was proposed for predicting the specific heat capacity of nanoparticle suspensions in a solvent. The model explores the effect of the compressed phase – that is induced from the solvent molecules - at the interface with individual nanoparticles in the mixture. The results from the numerical simulations indicate that depending on the properties and morphology of the compressed phase – it can cause significant enhancement in the specific heat capacity of nanofluids and nanocomposites. The interfacial thermal resistance (also known as Kapitza resistance, or “Rk”) between a nanoparticle and the surrounding solvent molecules (for these molten salt based nanomaterials) is estimated using Molecular Dynamics (MD) simulations. This exercise is relevant for the design optimization of nanomaterials (nanoparticle size, shape, material, concentration, etc.). The design trade-off is between maximizing the thermal conductivity of the nanomaterial (which typically occurs for nanoparticle size varying between ~ 20-30nm) and maximizing the specific heat capacity (which typically occurs for nanoparticle size less than 5nm), while simultaneously minimizing the viscosity of the nanofluid. The specific heat capacity of nitrate salt-based nanomaterials was measured both for the nanocomposites (solid phase) and nanofluids (liquid phase). The neat salt sample was composed of a mixture of KNO3: NaNO3 (60:40 molar ratio). The enhancement of specific heat capacity of the nanomaterials obtained from the salt samples was found to be very sensitive to minor variations in the synthesis protocol. The measurements for the variation of the specific heat capacity with the mass concentration of nanoparticles were compared to the predictions from the analytical model. Materials characterization was performed using electron microscopy techniques (SEM and TEM). The rheological behavior of nanofluids can be non-Newtonian (e.g., shear thinning) even at very low mass concentrations of nanoparticles, while (in contrast) the pure undoped (neat) molten salt may be a Newtonian fluid. Such viscosity enhancements and change in rheological properties of nanofluids can be detrimental to the operational efficiencies for thermal management as well as energy storage applications (which can effectively lead to higher costs for energy conversion). Hence, the rheological behavior of the nanofluid samples was measured experimentally and compared to that of the neat solvent (pure molten salt eutectic). The viscosity measurements were performed for the nitrate based molten salt samples as a function of temperature, shear rate and the mass concentration of the nanoparticles. The experimental measurements for the rheological behavior were compared with analytical models proposed in the literature. The results from the analytical and computational investigations as well as the experimental measurements performed in this proposed study – were used to formulate the design rules for maximizing the enhancement in the thermo-physical properties (particularly the specific heat capacity) of various molten salt based inorganic nanomaterials. The results from these studies are summarized and the future directions are identified as a conclusion from this study.

The aim of this study is to further our understanding of the forces that contribute to protein stability and to investigate how site-directed mutagenesis might be used for increasing protein stability. Eleven proteins ranging from 36 to 370 residues have been studied here. A 36-residue VHP and a 337-residue VlsE were used as model systems for studying the contribution of the hydrophobic effect on protein stability. Mutations were made in both proteins which replaced bulky hydrophobic side chains with smaller ones. All variants were less stable than their wild-type proteins. For VHP, the destabilizing effects of mutations were smaller when compared with similar mutations reported in the literature. For VlsE, a similarity was observed. This different behavior was investigated and reconciled by the difference in hydrophobicity and cavity modeling for both proteins. Therefore, the stabilizing mechanism of the hydrophobic effect appears to be similar for both proteins. Eight proteins were used as model systems for studying the effects of mutating non-proline and non-glycine residues to statistically favored proline and glycine residues in ?-turns. The results suggest that proline mutations generally increase protein stability, provided that the replaced residues are solvent exposed. The glycine mutations, however, only have a stabilizing effect when the wild-type residues have ?, ? angles in the L? region of Ramachandran plot. Nevertheless, this strategy still proves to be a simple and efficient way for increasing protein stability. Finally, using a combination of eight previously identified stabilizing mutations; we successfully designed two RNase Sa variants (7S, 8S) that have both much higher Tms and conformational stabilities than wild-type protein over the entire pH range studied. Further studies of the heat capacity change upon unfolding (?Cps) for both proteins and their variants suggest that residual structure may exist in the denatured state of the 8S variant. An analysis of stability curves for both variants suggests that they achieve their stabilization through different mechanisms, partly attributed to the different role of their denatured states. The 7S variants may have a more rigid denatured state and the 8S variant may have a compact denatured state in comparison with that of wild-type RNase Sa.

Owing to their excellent infrared transmittance and good rheological properties, selenium based chalcogenide glasses have been materials of choice for a number of technological applications. However, chalcogenide glasses can undergo substantial structural relaxation even at room temperature due to their low glass transition temperatures. The origins of these dynamic processes and their correlation to the glass structure is therefore of fundamental and practical interest. In particular, a deep understanding of the dynamic response near the glass transition region could help elucidate the mechanism of these structural relaxation processes. The correlation between structure and dynamic properties of selenium based glass systems were therefore investigated. NMR and Raman spectroscopy measurements reveal that the structure of AsₓSe₁₋ₓ glass follow the chain crossing model in selenium-rich glasses but contain increasing amounts of cage molecules in arsenic-rich compositions. This structural pattern leads to systematic extrema in physical properties at the stoichiometric composition As₄₀Se₆₀.The dynamic response of AsₓSe₁₋ₓ glasses investigated by heat capacity spectroscopy shows two minima in melt fragility as a function of composition which correlate well with the dimensionality of the glassy network. The structure evolves from 2D to 3D during crosslinking of selenium chains by arsenic but reduces into a 2D layer-like structure at the stoichiometric composition. Upon precipitation of arsenic-rich cages the network first reverts back to 3D and eventually becomes a mix of 2D and 0D structural units. The presence of molecular clusters in the network is evidenced by a strong bimodal dynamic response at high arsenic contents. NMR and Raman spectroscopy measurements of GeₓSe₁₋ₓ glasses suggest a structure composed of aggregated tetrahedral units and long selenium chains with little or no connectivity. Distinct dynamic responses of these two separated structural motifs are revealed by heat capacity spectroscopy. A non-Gaussian distribution of the imaginary heat capacity peak provided further evidence for the structural heterogeneity. This behavior is consistent with high temperature NMR measurements which show that the dynamic response of floppy selenium chains is distinct from that of rigid tetrahedral units. Finally, heat capacity spectroscopy applied to pure selenium provides strong evidence for the microscopic origin of the non-exponential structural relaxation, a universal feature of fragile glasses. The evolution of the imaginary heat capacity peak shape during annealing shows a non-monotonic trend which remarkably matches model predictions based on the enthalpy landscape. These results indicate that the non-exponential character of the relaxation process is linked to density fluctuations in the glass.

The heat capacity and the enthalpy of crystallization of the crystalline phases at the end of cooling must be known in order to determine the excess energy of mixing two pure triglycerides, trilaurin and trimyristin, cooled at different cooling rates. The present investigation was carried out using Differential Scanning Calorimetry, DSC, Modulated Differential Scanning Calorimetry, MDSC®, and Thermal Relaxation (in a Physical Properties Measurement System, PPMS). It was found that enthalpy of crystallization values can be measured to within ? 2% (SE) with DSC Q100 TA Instruments. To achieve this, an experimental procedure and a data analysis method are proposed. It was not possible in this study to obtain accurate and reproducible heat capacity values using a DSC Q100 instrument. The values were shown to be significantly by the position of the sample pan in the measuring sensor. PPMS Cp values were within the literature values.