• The Journal of the Petrological Society of Korea
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• v.28 no.3
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• pp.157-169
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• 2019

Pseudotachylytes, produced by frictional heating during seismic slip, provide information that is critical to understanding the physics of earthquakes. We report the results of occurrence, structural characteristics, scanning electron microscopic observation and geochemical analysis of pseudotachylytes, which is presumed to have formed after the Late Cretaceous in outcrops of the Paleoproterozoic granitic gneiss on the Bulil waterfall of the Jirisan area, Yeongnam massif, Korea. Fault rocks, which are the products of brittle deformation under the same shear stress regime in the study area, are classified as pseudotachylyte and foliated cataclasite. The occurrences of pseudotachylyte identified on the basis of thickness and morphology are fault vein-type and injection vein-type pseudotachylyte. A number of fault vein-type pseudotachylytes occur as thin (as thick as 2 cm) layers generated on the fault plane, and are cutting general foliation and sheared foliation developed in granitic gneiss. Smaller injection vein-type pseudotachylytes are found along the fault vein-type pseudotachylytes, and appear in a variety of shapes based on field occurrence and vein geometry. At a first glance fault vein-type seudotachylyte looks like a mafic vein, but it has a chemical composition almost identical to the wall rock of granitic gneiss. Also, it has many subrounded clasts which consist predominantly of quartz, feldspar, biotite and secondary minerals including clay minerals, calcite and glassy materials. Embayed clasts, phenocryst with reaction rim, oxide droplets, amygdules, and flow structures are also observed. All of these evidences indicate the pseudotachylyte formed due to frictional melting of the wall rock minerals during fault slip related to strong seismic faulting events in the shallow depth of low temperature-low pressure. Further studies will be conducted to determine the age and mechanical aspect of the pseudotachylyte formation.

• Clean Technology
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• v.28 no.1
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• pp.24-31
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• 2022

Petroleum-based plastics are used for various purposes and pose a significant threat to the earth's environment and ecosystem. Many efforts have been taken globally in different areas to find alternatives. As part of these efforts, this study manufactured alginate-based polyvinyl alcohol (PVA) blended films by casting from an aqueous solution prepared by mixing 10 wt% petroleum-based PVA with biodegradable, marine biomass-derived alginate. Glutaraldehyde was used as a cross-linking agent, and cardanol, an alkyl phenol-based bio-oil extracted from cashew nut shell, was added in the range of 0.1 to 2.0 wt% to grant antibacterial activity to the films. FTIR and TGA were performed to characterize the manufactured blended films, and the tensile strength, degree of swelling, and antibacterial activity were measured. Results obtained from the FTIR, TGA, and tensile strength test showed that alginate, the main component, was well distributed in the PVA by forming a matrix phase. The brittleness of alginate, a known weakness as a single component, and the low thermal durability of PVA were improved by cross-linking and hydrogen bonding of the functional groups between alginate and PVA. Addition of cardanol to the alginate-based PVA blend significantly improved the antibacterial activity against S. aureus and E. coli. The antibacterial performance was excellent with a death rate of 98% or higher for S. aureus and about 70% for E. coli at a contact time of 60 minutes. The optimal antibacterial activity of the alginate-PVA blended films was found with a cardanol content range between 0.1 to 0.5 wt%. These results show that cardanol-containing alginate-PVA blended films are suitable for use as various antibacterial materials, including as food packaging.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.