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Parametric Study of Methanol Chemical Vapor Deposition Growth for Graphene
Hyunjin Cho,Changhyup Lee,In Seoup Oh,Sungchan Park,Hwan Chul Kim,Myung Jong Kim 한국탄소학회 2012 Carbon Letters Vol.13 No.4
Methanol as a carbon source in chemical vapor deposition (CVD) graphene has an advantage over methane and hydrogen in that we can avoid optimizing an etching reagent condition. Since methanol itself can easily decompose into hydrocarbon and water (an etching reagent) at high temperatures [1], the pressure and the temperature of methanol are the only parameters we have to handle. In this study, synthetic conditions for highly crystalline and large area graphene have been optimized by adjusting pressure and temperature; the effect of each parameter was analyzed systematically by Raman, scanning electron microscope, transmission electron microscope, atomic force microscope, four-point-probe measurement, and UV-Vis. Defect density of graphene, represented by D/G ratio in Raman, decreased with increasing temperature and decreasing pressure; it negatively affected electrical conductivity. From our process and various analyses, methanol CVD growth for graphene has been found to be a safe, cheap, easy, and simple method to produce high quality, large area, and continuous graphene films.
Data-driven inverse modeling with a pre-trained neural network at heterogeneous channel reservoirs
Ahn, Seongin,Park, Changhyup,Kim, Jaejun,Kang, Joe M. Elsevier 2018 Journal of petroleum science & engineering Vol.170 No.-
<P><B>Abstract</B></P> <P>This paper develops a reliable and efficient data-integration method, based on artificial neural networks (ANN) incorporated with a stacked autoencoder (SAE) in a deep neural network's framework. To handle scale-different static and dynamic data of heterogeneous channel reservoirs, the workflow suggests an unsupervised pre-training process coupled with ANN-based inverse modeling. The performances of the proposed neural network, i.e. the training efficiency, the predictability of future production rates and the computing time, are compared to those with an optimal ANN and the impact of hidden neurons are discussed. The pre-trained neural network demonstrates a reliable estimation of reservoir properties with the spatial characteristics of a true channel reservoir while the ANN fails with respect to the spatial heterogeneity. The pre-trained neural network decreases the mean absolute error of future oil production rates up to 9.1% which is less than 25% of the comparison case's level, i.e. the optimal ANN model. Its efficiency is validated by a computing time 14 times faster than that of the optimal ANN workflow. The pre-trained neural network evaluates the spatial characteristics of reservoir properties and facies models in a reasonable manner and is thus able to predict the water production rates and also the breakthrough time accurately. This pre-trained neural network manifests its applicability with robustness as an efficient method to integrate static and dynamic data.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed an efficient inverse model with unsupervised pre-training process. </LI> <LI> We confirmed its reliability comparing the performances with an optimum ANN method. </LI> <LI> This method preserved the geological realism at heterogeneous channel reservoirs. </LI> </UL> </P>