• Journal of Adhesion and Interface
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• v.20 no.1
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• pp.9-14
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• 2019

The change of three different reagents for electrical conductive paint using aircraft coating with elapsing time of exposure to different condition was investigated. Three different reagents were poured into the vial bottles, stored in air condition and room temperature and observed with elapsing days. In addition, adhesion property of paint was tried using cross cut tape test after storage of $N_2$ atmosphere. The weight of each different reagent was measured along with elapsing time. To confirm the change of chemical component with exposure of air atmosphere, FT-IR was performed. The weight of part A and Part B decreased slightly whereas the weight of part C decreased rapidly and the precipitation was remained. The part B was cured after exposure of $N_2$ atmosphere and the 2250 cm-1 from FT-IR peak decreased slowly at the same time. It was considered that the water contained in air accelerated the reaction of -NCO functional groups and it caused the curing whereas $N_2$ atmosphere not contained water and it resulted in the retardancy of curing.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.259-272
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• 2022

The branch of electronics that uses an organic solar cell or conductive organic polymers in order to yield electricity from sunlight is called photovoltaic. Regarding this crucial issue, an artificial intelligence-based predictor is presented to investigate the vibrational behavior of the organic solar cell. In addition, the generalized differential quadrature method (GDQM) is utilized to extract the results. The validation examination is done to confirm the credibility of the results. Then, the deep neural network with fully connected layers (DNN-FCL) is trained by means of Adam optimization on the dataset whose members are the vibration response of the design-points. By determining the optimum values for the biases along with weights of DNN-FCL, one can predict the vibrational characteristics of any organic solar cell by knowing the properties defined as the inputs of the mentioned DNN. To assess the ability of the proposed artificial intelligence-based model in prediction of the vibrational response of the organic solar cell, the authors monitored the mean squared error in different steps of the training the DNN-FCL and they observed that the convergency of the results is excellent.