solvation by a polar aprotic solvent

A series of strong H-bonded complexes of trimethylglycine, also known as betaine, with acetic, chloroacetic, dichloroacetic, trifluoroacetic and hydrofluoric acids as well as the homo-conjugated cation of betaine with trifluoroacetate as the counteranion were investigated by low-temperature (120–160 K) liquid-state NMR spectroscopy using CDF3/CDF2Cl mixture as the solvent. The temperature dependencies of 1H NMR chemical shifts are analyzed in terms of the solvent–solute interactions. The experimental data are explained assuming the combined action of two main effects. Firstly, the solvent ordering around the negatively charged OHX region of the complex (X = O, F) at low temperatures, which leads to a contraction and symmetrisation of the H-bond; this effect dominates for the homo-conjugated cation of betaine. Secondly, at low temperatures structures with a larger dipole moment are preferentially stabilized, an effect which dominates for the neutral betaine–acid complexes. The way this second contribution affects the H-bond geometry seems to depend on the proton position. For the Be+COO−⋯HOOCCH3 complex (Be = (CH3)3NCH2–) the proton displaces towards the hydrogen bond center (H-bond symmetrisation, O⋯O contraction). In contrast, for the Be+COOH⋯−OOCCF3 complex the proton shifts further away from the center, closer to the betaine moiety (H-bond asymmetrisation, O⋯O elongation). Hydrogen bond geometries and their changes upon lowering the temperature were estimated using previously published H-bond correlations

Dynamical and quasistatic structural relaxation paths in Pd_(40)Ni_(40)P_(20) glass

By sequential heat treatment of a Pd_(40)Ni_(40)P_(20) metallic glass at temperatures and durations for which α-relaxation is not possible, dynamic, and quasistatic relaxation paths below the glass transition are identified via ex situ ultrasonic measurements following each heat treatment. The dynamic relaxation paths are associated with hopping between nonequilibrium potential energy states of the glass, while the quasistatic relaxation path is associated with reversible β-relaxation events toward quasiequilibrium states. These quasiequilibrium states are identified with secondary potential energy minima that exist within the inherent energy minimum of the glass, thereby supporting the concept of the sub-basin/metabasin organization of the potential-energy landscape

INTERMEDIATE SUMS ON POLYHEDRA: COMPUTATION AND REAL EHRHART THEORY

We study intermediate sums, interpolating between integrals and discrete sums, which were introduced by A. Barvi-nok [Computing the Ehrhart quasi-polynomial of a rational simplex, Math. Comp. 75 (2006), 1449–1466]. For a given semi-rational polytope p and a rational subspace L, we integrate a given polyno-mial function h over all lattice slices of the polytope p parallel to the subspace L and sum up the integrals. We first develop an al-gorithmic theory of parametric intermediate generating functions. Then we study the Ehrhart theory of these intermediate sums, that is, the dependence of the result as a function of a dilation of the polytope. We provide an algorithm to compute the resulting Ehrhart quasi-polynomials in the form of explicit step polynomi-als. These formulas are naturally valid for real (not just integer) dilations and thus provide a direct approach to real Ehrhart theory

Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models

(Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. The new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions can be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-explaining approach, which interprets neural networks trained on mechanical data a posteriori. This proof-of-concept explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials

Desformylgramicidin: A Model Channel with an Extremely High Water Permeability

AbstractThe water conductivity of desformylgramicidin exceeds the permeability of gramicidin A by two orders of magnitude. With respect to its single channel hydraulic permeability coefficient of 1.1·10−12cm3s−1, desformylgramicidin may serve as a model for extremely permeable aquaporin water channel proteins (AQP4 and AQPZ). This osmotic permeability exceeds the conductivity that is predicted by the theory of single-file transport. It was derived from the concentration distributions of both pore-impermeable and -permeable cations that were simultaneously measured by double barreled microelectrodes in the immediate vicinity of a planar bilayer. From solvent drag experiments, approximately five water molecules were found to be transported by a single-file process along with one ion through the channel. The single channel proton, potassium, and sodium conductivities were determined to be equal to 17pS (pH 2.5), 7 and 3pS, respectively. Under any conditions, the desformyl-channel remains at least 10 times longer in its open state than gramicidin A

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Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109838/1/cptclpt2006257.pd

Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10$^{-3}$ S cm$^{-1}$ is discovered within several iterations. In order to further understand the mechanisms governing the high ionic conductivity of these samples, the resulting phase compositions and crystal structures are studied with X-ray diffraction, while the microstructures of sintered pellets are investigated by scanning electron microscopy. Our studies demonstrate the advantages of applying machine learning in designing experimental conditions by the synthesis of desired materials, which can effectively help researchers to reduce the number of required experiments

Microscopic Model for Granular Stratification and Segregation

We study segregation and stratification of mixtures of grains differing in size, shape and material properties poured in two-dimensional silos using a microscopic lattice model for surface flows of grains. The model incorporates the dissipation of energy in collisions between rolling and static grains and an energy barrier describing the geometrical asperities of the grains. We study the phase diagram of the different morphologies predicted by the model as a function of the two parameters. We find regions of segregation and stratification, in agreement with experimental finding, as well as a region of total mixing.Comment: 4 pages, 7 figures, http://polymer.bu.edu/~hmakse/Home.htm

Regional vesicular acetylcholine transporter distribution in human brain: A [18F]fluoroethoxybenzovesamicol positron emission tomography study

Prior efforts to image cholinergic projections in human brain in vivo had significant technical limitations. We used the vesicular acetylcholine transporter (VAChT) ligand [18F]fluoroethoxybenzovesamicol ([18F]FEOBV) and positron emission tomography to determine the regional distribution of VAChT binding sites in normal human brain. We studied 29 subjects (mean age 47 [range 20–81] years; 18 men; 11 women). [18F]FEOBV binding was highest in striatum, intermediate in the amygdala, hippocampal formation, thalamus, rostral brainstem, some cerebellar regions, and lower in other regions. Neocortical [18F]FEOBV binding was inhomogeneous with relatively high binding in insula, BA24, BA25, BA27, BA28, BA34, BA35, pericentral cortex, and lowest in BA17–19. Thalamic [18F]FEOBV binding was inhomogeneous with greatest binding in the lateral geniculate nuclei and relatively high binding in medial and posterior thalamus. Cerebellar cortical [18F]FEOBV binding was high in vermis and flocculus, and lower in the lateral cortices. Brainstem [18F]FEOBV binding was most prominent at the mesopontine junction, likely associated with the pedunculopontine–laterodorsal tegmental complex. Significant [18F]FEOBV binding was present throughout the brainstem. Some regions, including the striatum, primary sensorimotor cortex, and anterior cingulate cortex exhibited age‐related decreases in [18F]FEOBV binding. These results are consistent with prior studies of cholinergic projections in other species and prior postmortem human studies. There is a distinctive pattern of human neocortical VChAT expression. The patterns of thalamic and cerebellar cortical cholinergic terminal distribution are likely unique to humans. Normal aging is associated with regionally specific reductions in [18F]FEOBV binding in some cortical regions and the striatum.Using [18F]FEOBV PET, we describe the distribution of cholinergic terminals in human brain. The distribution of cholinergic terminals is similar to that found in other mammals with some distinctive features in cortex, thalamus, and cerebellum. There are regionally specific age‐related changes in cholinergic terminal density.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146604/1/cne24541.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146604/2/cne24541_am.pd