First-principles-informed energy span and microkinetic analysis of ethanol catalytic conversion to 1,3-butadiene on MgO

Kinetic modeling of single-step catalytic conversion of ethanol to 1,3-butadiene is necessary to inform accurate process design. This paper uses first-principles-informed energy span and microkinetic analysis to explore the reaction free energy landscapes and kinetic limitations of competing reaction pathways on a MgO (100) step-edge. Previous studies suggested mechanisms proceeding via both dehydrogenation and dehydration of ethanol, and highlighted sensitivity to conditions and catalyst composition. Here, we use the energy span concept to characterize the theoretical maximum turnover and degree of turnover frequency control for states in each reaction pathway, finding the dehydration route to be less active for 1,3-butadiene, and suggesting rate-determining states in the dehydrogenation, dehydration, and condensation steps. The influence of temperature on the relative rate contribution of each state is quantified and explained through the varying temperature sensitivity of the free energy landscape. A microkinetic model is developed to explore competition between pathways, interaction with gas-phase species, and surface coverage limitations. This suggests that the turnover may be significantly lower than predicted solely based on energetics. Turnover frequency determining states found to have high surface coverage include adsorbed ethanol and two longer, oxygenated hydrocarbons. The combined energy span and microkinetic analysis permits investigation of a complex system from two perspectives and helps elucidate conflicting observations of rate determining steps and product distribution by considering both energetic and kinetic limitations. The impact of uncertainty in the energy landscape is quantified using a correlated error model. While the range of predictions is large, the average performance and trends are similar

EFFECT OF ZEOLITE (CLINOPTILOLITE) ON CHEMICAL PARAMETERS OF RABBIT MEAT

This work is aimed to evaluate the effect of natural zeolite on selected parameters of the rabbit's meat chemical composition. The rabbits of the Castorex breed (n=12) were divided into two groups: Control group C (n=6) without the addition of zeolite in feed and the experimental group Z (n=6) in which animals received zeolite daily in a peroral form in concentration 0.2 g/kg of body weight. Determination of the nutritional composition of rabbit meat and analysis of individual amino acids and fatty acids was performed by the Nicolet 6700 using FT-IR method (fourier transform infrared spectroscopy) in the musculus Longissimus dorsi (MLD) and the musculus Vastus lateralis (MVL). The content of water in meat in group Z (73.630 ± 0.270 g * 100g-1) was significantly higher (P < 0.05) compared to group C (72.480 ± 0.530 g * 100g-1). Cysteine content in MLD in group Z (0.289 ± 0.007 g*100 g-1) was significantly (P < 0.05) higher than in group C (0.277 ± 0.011 g*100 g-1). Another noticeable difference in the statistical level p < 0.05 in favor of zeolite was found in linoleic acid content in MLD with mean values in group Z (0.324 ± 0.016 g*100g-1 FAME) against the group C (0.293 ± 0.009 g* 100 g-1 FAME). Therefore, the addition of zeolite to the rabbit feeding diet possibly increases the proportion of essential linoleic fatty acid and thereby may improve the nutritional value of the meat. Increased cysteine value can lead to a better degradation of heavy metals in meat

Non-invasive geophysical investigation and thermodynamic analysis of a palsa in Lapland, northwest Finland

Non-invasive geophysical prospecting and a thermodynamic model were used to examine the structure, depth and lateral extent of the frozen core of a palsa near Lake Peerajärvi, in northwest Finland. A simple thermodynamic model verified that the current climatic conditions in the study area allow sustainable palsa development. A ground penetrating radar (GPR) survey of the palsa under both winter and summer conditions revealed its internal structure and the size of its frozen core. GPR imaging in summer detected the upper peat/core boundary, and imaging in winter detected a deep reflector that probably represents the lower core boundary. This indicates that only a combined summer and winter GPR survey completely reveals the lateral and vertical extent of the frozen core of the palsa. The core underlies the active layer at a depth of ~0.6 m and extends to about 4 m depth. Its lateral extent is ~15 m x ~30 m. The presence of the frozen core could also be traced as minima in surface temperature and ground conductivity measurements. These field methods and thermodynamic models can be utilized in studies of climate impact on Arctic wetlands.Peer reviewe

Methanol Carbonylation over Acid Mordenite: Insights from Ab Initio Molecular Dynamics and Machine Learning Thermodynamic Perturbation Theory

In this work we present a detailed \textit{ab initio} study of the carbonylation reaction of methoxy groups in the zeolite mordenite, as it is the rate determining step in a series of elementary reactions leading to ethanol. For the first time we employ full molecular dynamics simulations to evaluate free energies of activation for the reactions in side pockets and main channels. Results show that the reaction in the side pocket is preferred and, when dispersion interactions are taken into account, this preference becomes even stronger. This conclusion is confirmed using multiple levels of density functional theory approximations with (PBE-D2, PBE-MBD, and vdW-DF2-B86R) or without (PBE, HSE06) dispersion corrections. These calculations, that in principle would require several demanding molecular dynamics simulations, were made possible at a minimal computational cost by using a newly developed approach that combines thermodynamic perturbation theory with machine learning.</div

Ab initio calculations of free energy of activation at multiple electronic structure levels made affordable: An effective combination of perturbation theory and machine learning

While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div

Improved Density Dependent Correction for the Description of London Dispersion Forces

International audienceThe Tkatchenko-Scheffler method for calculating dispersion correction to standard density-functional theory, which uses fixed neutral atoms as a reference to estimate the effective volumes of atoms-in-molecule and to calibrate their polarizabilities and dispersion coefficients, fails to describe the structure and the energetics of ionic solids. Here, we propose a more appropriate partitioning, based on the iterative Hirshfeld scheme, where the fractionally charged atomic reference state is determined self-consistently. We show that our new method extends the applicability of the original method in particular to study ionic systems and adsorption phenomena on surfaces of ionic solids

Many-body dispersion corrections for periodic systems: an efficient reciprocal space implementation

International audienceThe energy and gradient expressions for the many-body dispersion scheme (MBD@rsSCS) of Ambrosetti et al (2014 J. Chem. Phys. 140 18A508) needed for an efficient implementation of the method for systems under periodic boundary conditions are reported. The energy is expressed as a sum of contributions from points sampled in the first Brillouin zone, in close analogy with planewave implementations of the RPA method for electrons in the dielectric matrix formulation. By avoiding the handling of large supercells, considerable computational savings can be achieved for materials with small and medium sized unit cells. The new implementation has been tested and used for geometry optimization and energy calculations of inorganic and molecular crystals, and layered materials