Anisotropic Compositional Expansion and Chemical Potential of Lithiated SiO2 Electrodes: Multiscale Mechanical Analysis

2019 ◽  
Vol 11 (21) ◽  
pp. 19183-19190 ◽  
Author(s):  
Janghyuk Moon ◽  
Min-Sik Park ◽  
Maenghyo Cho
Keyword(s):  
Chemical Potential ◽  
Mechanical Analysis

scholarly journals Swelling and shrinking in prestressed polymer gels: an incremental stress–diffusion analysis

2019 ◽  
Vol 475 (2230) ◽  
pp. 20190174 ◽  
Author(s):  
Marco Rossi ◽  
Paola Nardinocchi ◽  
Thomas Wallmersperger
Keyword(s):  
Chemical Potential ◽  
Polymer Gels ◽  
Mechanical Analysis ◽  
Test Case ◽  
Diffusion Analysis ◽  
Stress Diffusion ◽  
Nonlinear Stress ◽  
Deformation State ◽  
Poroelastic Theory ◽  
Elastic Constraint

Polymer gels are porous fluid-saturated materials which can swell or shrink triggered by various stimuli. The swelling/shrinking-induced deformation can generate large stresses which may lead to the failure of the material. In the present research, a nonlinear stress–diffusion model is employed to investigate the stress and the deformation state arising in hydrated constrained polymer gels when subject to a varying chemical potential. Two different constraint configurations are taken into account: (i) elastic constraint along the thickness direction and (ii) plane elastic constraint. The first step entirely defines a compressed/tensed configuration. From there, an incremental chemo-mechanical analysis is presented. The derived model extends the classical linear poroelastic theory with respect to a prestressed configuration. Finally, the comparison between the analytical results obtained by the proposed model and a particular problem already discussed in literature for a stress-free gel membrane (one-dimensional test case) will highlight the relevance of the derived model.

Light-cured dental composites characterization by Electron Microscopy

1990 ◽  
Vol 48 (4) ◽  
pp. 428-429
Author(s):  
B. M. Culbertson ◽  
M. L. Devinev ◽  
E. C. Kao
Keyword(s):  
Electron Microscopy ◽  
Mechanical Analysis ◽  
Service Performance ◽  
Dental Composite ◽  
Dental Composites ◽  
Neat Resin ◽  
Scanning Calorimetry ◽  
Research Meeting ◽  
Formulation Variables

The service performance of current dental composite materials, such as anterior and posterior restoratives and/or veneer cements, needs to be improved. As part of a comprehensive effort to find ways to improve such materials, we have launched a broad spectrum study of the physicochemical and mechanical properties of photopolymerizable or visible light cured (VLC) dental composites. The commercially available VLC materials being studied are shown in Table 1. A generic or neat resin VLC system is also being characterized by SEM and TEM, to more fully understand formulation variables and their effects on properties.At a recent dental research meeting, we reported on the differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) characterization of the materials in Table 1. It was shown by DSC and DMA that the materials are substantially undercured by commonly used VLC techniques. Post curing in an oral cavity or a dry environment at 37 to 50°C for 7 or more hours substantially enhances the cure of the materials.

Chemical potential and dimensions of chain molecules in athermal environments

1996 ◽  
Vol 89 (6) ◽  
pp. 1733-1754 ◽  
Author(s):  
FERNANDO ESCOBEDO ◽  
JUAN DE PABLO
Keyword(s):  
Chemical Potential ◽  
Chain Molecules

Growth Kinetics of Quantum Size ZnO Particles

1998 ◽  
Vol 536 ◽  
Author(s):  
E. M. Wong ◽  
J. E. Bonevich ◽  
P. C. Searson
Keyword(s):  
Growth Kinetics ◽  
Ostwald Ripening ◽  
Chemical Potential ◽  
Colloidal Suspensions ◽  
Green Emission ◽  
Blue Shift ◽  
Constant Current ◽  
Mass Model ◽  
Zno Particles ◽  
Kinetics Of

AbstractColloidal chemistry techniques were used to synthesize ZnO particles in the nanometer size regime. The particle aging kinetics were determined by monitoring the optical band edge absorption and using the effective mass model to approximate the particle size as a function of time. We show that the growth kinetics of the ZnO particles follow the Lifshitz, Slyozov, Wagner theory for Ostwald ripening. In this model, the higher curvature and hence chemical potential of smaller particles provides a driving force for dissolution. The larger particles continue to grow by diffusion limited transport of species dissolved in solution. Thin films were fabricated by constant current electrophoretic deposition (EPD) of the ZnO quantum particles from these colloidal suspensions. All the films exhibited a blue shift relative to the characteristic green emission associated with bulk ZnO. The optical characteristics of the particles in the colloidal suspensions were found to translate to the films.

Rubber–Road Contact: Comparison of Physics-Based Theory and Indoor Experiments

2016 ◽  
Vol 44 (3) ◽  
pp. 150-173 ◽  
Author(s):  
Mehran Motamedi ◽  
Saied Taheri ◽  
Corina Sandu
Keyword(s):  
Rough Surface ◽  
Practical Importance ◽  
Surface Profile ◽  
Surface Characteristics ◽  
Mechanical Analysis ◽  
Friction Model ◽  
Rubber Friction ◽  
Optical Profilometer ◽  
Viscoelastic Modulus ◽  
Longitudinal Slip

ABSTRACT For tire designers, rubber friction is a topic of pronounced practical importance. Thus, development of a rubber–road contact model is of great interest. In this research, to predict the effectiveness of the tread compound in a tire as it interacts with the pavement, the physics-based multiscale rubber-friction theories developed by B. Persson and M. Klüppel were studied. The strengths of each method were identified and incorporated into a consolidated model that is more comprehensive and proficient than any single, existing, physics-based approach. In the present work, the friction coefficient was estimated for a summer tire tread compound sliding on sandpaper. The inputs to the model were the fractal properties of the rough surface and the dynamic viscoelastic modulus of rubber. The sandpaper-surface profile was measured accurately using an optical profilometer. Two-dimensional parameterization was performed using one-dimensional profile measurements. The tire tread compound was characterized via dynamic mechanical analysis. To validate the friction model, a laboratory-based, rubber-friction test that could measure the friction between a rubber sample and any arbitrary rough surface was designed and built. The apparatus consisted of a turntable, which can have the surface characteristics of choice, and a rubber wheel in contact with the turntable. The wheel speed, as well as the turntable speed, could be controlled precisely to generate the arbitrary values of longitudinal slip at which the dynamic coefficient of friction was measured. The correlation between the simulation and the experimental results was investigated.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

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