• Composites Research
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• v.12 no.4
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• pp.71-78
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• 1999

To determine the effect of chemical structure of linear amine curing agents on thermal and mechanical properties, standard epoxy resin DGEBA was cured with diaminodiphenyl methane (DDM), diaminodiphenyl sulphone (DDS) in a stoichiometrically equivalent ratio. From this work, the effect of aromatic amine curing agents. In contrast, the results show that the DGEBA/DDS cure system having the sulfone structure between the benzene rings had higher values in the conversion of epoxide, density, shrinkage (%), glass transition temperature, tensile modulus and strength, flexural modulus and strength than the DGEBA/DDM cure system having methylene structure between the benzene rings, whereas the DGEBA/DDM cure system presented higher values in the maximum exothermic temperature, thermal expansion coefficient, and thermal stability. These results are caused by the relative effects of sulfone group having strong electronegativity and methylene group having (+) repulsive property and stem from the effect of the conversion ratio of epoxide group. The result of fractography shows that the each grain size of the DDM/DGEBA system with feather-like structure is larger than that of the DDS/DGEBA system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.1
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• pp.39-46
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• 2021

The quantitative measurement of interfacial adhesion energy (Gc) of multilayer thin films for Cu interconnects was investigated using a double cantilever beam (DCB) and 4-point bending (4-PB) test. In the case of a sample with Ta diffusion barrier applied, all Gc values measured by the DCB and 4-PB tests were higher than 5 J/㎡, which is the minimum criterion for Cu/low-k integration without delamination. However, in the case of the Ta/Cu sample, measured Gc value of the DCB test was lower than 5 J/㎡. All Gc values measured by the 4-PB test were higher than those of the DCB test. Measured Gc values increase with increasing phase angle, that is, 4-PB test higher than DCB test due to increasing plastic energy dissipation and roughness-related shielding effects, which matches well interfacial fracture mechanics theory. As a result of the 4-PB test, Ta/Cu and Cu/Ta interfaces measured Gc values were higher than 5 J/㎡, suggesting that Ta is considered to be applicable as a diffusion barrier and a capping layer for Cu interconnects. The 4-PB test method is recommended for quantitative adhesion energy measurement of the Cu interconnect interface because the thermal stress due to the difference in coefficient of thermal expansion and the delamination due to chemical mechanical polishing have a large effect of the mixing mode including shear stress.