scholarly journals Computation of the chemical potential and solubility of amorphous solids

2021 ◽  
Vol 154 (12) ◽  
pp. 124502
Author(s):  
H. A. Vinutha ◽  
Daan Frenkel
1996 ◽  
Vol 89 (6) ◽  
pp. 1733-1754 ◽  
Author(s):  
FERNANDO ESCOBEDO ◽  
JUAN DE PABLO

1992 ◽  
Vol 89 ◽  
pp. 2073-2089 ◽  
Author(s):  
GP Johari
Keyword(s):  

1991 ◽  
Vol 01 (C7) ◽  
pp. C7-439-C7-442 ◽  
Author(s):  
N. V. GRUZDEV ◽  
E. G. SIL'KIS ◽  
V. D. TITOV ◽  
Yu. G. VAINER

1981 ◽  
Vol 42 (C6) ◽  
pp. C6-125-C6-127
Author(s):  
R. Vacher ◽  
J. Pelous
Keyword(s):  

1985 ◽  
Vol 46 (C8) ◽  
pp. C8-281-C8-286 ◽  
Author(s):  
C. Tête ◽  
D. Boumazouza ◽  
G. Marchal ◽  
Ph. Mangin ◽  
J. Bouillot ◽  
...  

MRS Advances ◽  
2020 ◽  
Vol 5 (61) ◽  
pp. 3141-3152
Author(s):  
Alma C. Chávez-Mejía ◽  
Génesis Villegas-Suárez ◽  
Paloma I. Zaragoza-Sánchez ◽  
Rafael Magaña-López ◽  
Julio C. Morales-Mejía ◽  
...  

AbstractSeveral photocatalysts, based on titanium dioxide, were synthesized by spark anodization techniques and anodic spark oxidation. Photocatalytic activity was determined by methylene blue oxidation and the catalytic activities of the catalysts were evaluated after 70 hours of reaction. Scanning Electron Microscopy and X Ray Diffraction analysis were used to characterize the catalysts. The photocatalyst prepared with a solution of sulfuric acid and 100 V presented the best performance in terms of oxidation of the dye (62%). The electric potential during the synthesis (10 V, low potential; 100 V, high potential) affected the surface characteristics: under low potential, catalyst presented smooth and homogeneous surfaces with spots (high TiO2 concentration) of amorphous solids; under low potential, catalyst presented porous surfaces with crystalline solids homogeneously distributed.


1998 ◽  
Vol 536 ◽  
Author(s):  
E. M. Wong ◽  
J. E. Bonevich ◽  
P. C. Searson

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.


2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu

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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