Molecular simulation and ablation property on the laser-induced metal surface

2019 ◽  
Author(s):  
Qingshun Bai ◽  
Yuhai Li ◽  
Rongqi Shen ◽  
Kai Zhang ◽  
Xinxiang Miao ◽  
...  
Keyword(s):  
Molecular Simulation ◽  
Metal Surface ◽  
Ablation Property

Electrochemical nucleation and growth of copper on iron and aluminum: A TEM study of industrial copper cementation

1978 ◽  
Vol 36 (1) ◽  
pp. 454-455
Author(s):  
L.E. Murr ◽  
V. Annamalai
Keyword(s):  
Metal Surface ◽  
Copper Deposit ◽  
Nucleation And Growth ◽  
16Th Century ◽  
Electrochemical Reactions ◽  
Mine Shaft ◽  
De Re ◽  
Copper Precipitation ◽  
Electrochemical Nucleation ◽  
Interesting System

Georgius Agricola in 1556 in his classical book, “De Re Metallica”, mentioned a strange water drawn from a mine shaft near Schmölnitz in Hungary that eroded iron and turned it into copper. This precipitation (or cementation) of copper on iron was employed as a commercial technique for producing copper at the Rio Tinto Mines in Spain in the 16th Century, and it continues today to account for as much as 15 percent of the copper produced by several U.S. copper companies.In addition to the Cu/Fe system, many other similar heterogeneous, electrochemical reactions can occur where ions from solution are reduced to metal on a more electropositive metal surface. In the case of copper precipitation from solution, aluminum is also an interesting system because of economic, environmental (ecological) and energy considerations. In studies of copper cementation on aluminum as an alternative to the historical Cu/Fe system, it was noticed that the two systems (Cu/Fe and Cu/Al) were kinetically very different, and that this difference was due in large part to differences in the structure of the residual, cement-copper deposit.

Rapid Freezing of Freeze-Etch Specimens

1980 ◽  
Vol 38 ◽  
pp. 752-755
Author(s):  
A. Elgsaeter ◽  
T. Espevik ◽  
G. Kopstad
Keyword(s):  
Low Temperature ◽  
Metal Surface ◽  
Relative Velocity ◽  
Thermal Contact ◽  
High Rate ◽  
Rapid Freezing ◽  
Temperature Decrease ◽  
Freeze Etching

The importance of a high rate of temperature decrease (“rapid freezing”) when freezing specimens for freeze-etching has long been recognized1. The two basic methods for achieving rapid freezing are: 1) dropping the specimen onto a metal surface at low temperature, 2) bringing the specimen instantaneously into thermal contact with a liquid at low temperature and subsequently maintaining a high relative velocity between the liquid and the specimen. Over the last couple of years the first method has received strong renewed interest, particularily as the result of a series of important studies by Heuser and coworkers 2,3. In this paper we will compare these two freezing methods theoretically and experimentally.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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