본 연구는 동결융해 및 철근 부식으로 복합열화된 철근콘크리트 보를 탄소섬유 복합재료로 보강한 경우의 휨 거동을 평가하 기 위해 층상화 단면해석 모델을 제안하고 그 유효성을 실험적으로 검증하였다. 해석 모델은 열화에 따른 재료물성 저하와 CFRP 보강 효과를 통합적으로 고려하여 휨 거동을 예측하도록 구성되었다. 제안된 모델의 해석 결과, 열화 및 CFRP 보강 RC 보의 항복휨모멘트 와 최대휨모멘트 예측값은 실험값과 평균 1.01∼1.16의 비율을 보여 휨 성능을 매우 높은 신뢰도로 예측함을 확인하였다. 그러나 휨모 멘트-변위 관계에서는 일부 상이한 경향이 관찰되었다. 항복 이전 구간에서는 해석 모델의 휨 강성이 실험 결과보다 높게 평가되었는 데, 이는 해석 모델이 콘크리트의 초기 미세균열과 같은 비선형적 거동을 완벽히 반영하지 못하기 때문으로 분석된다. 반면, CFRP로 보강된 보의 항복 이후 구간에서는 해석 모델의 강성이 실험값보다 낮게 나타났다. 이는 현행 RC 이론 기반의 변위 산정 방식이 CFRP 보강재의 높은 탄성계수 효과를 충분히 반영하지 못하여 최대강도 도달 시의 변위를 과대평가하기 때문으로 판단된다.

This study proposes a methodology for predicting the physical properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, features are constructed using molecular dynamics (MD) simulation results and descriptor calculation tools. This study investigates the changes in the calculated density depending on the characteristics of the training dataset and analyzes the feature characteristics across datasets to identify key features. In this study, 2,415 hydrocarbon and binder-derived polymer molecules were analyzed using MD simulations and 2,790 chemical descriptors generated using alvaDesc. The features were pre-processed using correlation filtering, PCA, and recursive feature elimination. The XGBoost models were trained using k-fold cross-validation and Optuna optimization. SHAP analysis was used to interpret feature contributions. The variables influencing the density prediction differed between the hydrocarbon and binder groups. However, the hydrogen atom count (H), van der Waals energy, and descriptors such as SpMAD_EA_LboR consistently had a strong impact. The trained models achieved high accuracy (R² > 0.99) across different datasets, and the SHAP results revealed that the edge adjacency, topological, and 3D geometrical descriptors were critical. In terms of predictive accuracy and interpretability, the integrated MDQSPR framework demonstrated high reliability for estimating the properties of individual binder polymers. This approach contributed to a molecular-level understanding and facilitated the development of ecofriendly and efficient modifiers for asphalt binders.

This study proposes a methodology for predicting properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, this study investigates the variations in molecular dynamics (MD) results with molecular structural complexity and assesses the independence and correlation of variables that influence density. In this study, MD simulations were performed on hydrocarbon-based and individual asphalt binder molecules. The effects of various temperatures, molecular conditions, and structural features on the density were analyzed. MD-related variables influencing the calculated density were evaluated and compared with experimentally measured densities. The MD-calculated densities were used as target variables in a subsequent study, in which a machine learning model was applied to perform quantitative structure–property relationship analysis.The MD-calculated densities showed a strong correlation with experimental measurements, achieving a coefficient of determination of R2 > 0.95. Potential energy exhibited a tendency to cluster into 4–6 groups depending on the molecular structure. In addition, increasing molecular weight and decreasing temperature led to higher density and viscosity. Torsional energy and other individual energy components were identified as significant factors influencing both potential energy and density. This study provided foundational data for the property prediction of asphalt binders by quantitatively analyzing the relationship between the molecular structure and properties using MD simulations. Key features that could be used in the construction of polymer structure databases and AI-based material design were also proposed. In particular, the integration of MD-based simulation and machine learning was confirmed to be a practical alternative for predicting the properties of complex polymer composite systems.

A high-pressure in-situ permeation measuring system was developed to evaluate the hydrogen permeation properties of polymer sealing materials in hydrogen environments up to 100 MPa. This system employs the manometric method, utilizing a compact and portable manometer to measure the permeated hydrogen over time, following high-pressure hydrogen injection. By utilizing a self-developed permeation-diffusion analysis program, this system enables precise evaluation of permeation properties, including permeability, diffusivity and solubility. To apply the developed system to high-pressure hydrogen permeation tests, the hydrogen permeation properties of ethylene propylene diene monomer (EPDM) materials containing silica fillers, specifically designed for gas seal in high-pressure hydrogen environments, were evaluated. The permeation measurements were conducted under pressure conditions ranging from 5 MPa to 90 MPa. The results showed that as pressure increased, hydrogen permeability and diffusivity decreased, while solubility remained constant regardless of pressure. Finally, the reliability of this system was confirmed through uncertainty analysis of the permeation measurements, with all results falling within an uncertainty of 11.2 %.

Many recent research efforts have focused on developing high-performance wearable health monitoring systems. This work presents a mechanically stretchable and skin-mountable sensor system based on a conductive polymer composite-based elastic printed circuit board (EPCB) in which a resistive-type composite strain sensor is monolithically integrated. The composite-based EPCB is simply prepared by patterning a silver nanowire (AgNW)/dragon skin (AgNW/DS) composite film in a programmable manner using a direct cut patterning technique. The proposed sensor system was successfully fabricated by directly mounting various components (e.g., microcontroller, circuit elements, light emitting device chips, temperature sensor, Bluetooth module) on the prepared AgNW/DS-based EPCB. The fabricated sensor system was found to be highly stretchable and rollable enough to maintain tight adhesion to the wrist region without significant physical deterioration, even when the wrist was in motion. The wireless sensor system attached to the wrist part enabled us to monitor the wrist motion and surrounding temperature in real time, opening the possible application as a wearable health monitoring platform.

티오에테르를 기반으로 한 고분자 막은 이온 교환 및 나노 여과에서 중요한 분리 과정의 한 종류를 나타낸다. 막 을 통한 이온의 선택적 투과는 연료 전지, 전기투석, 역전기투석 등 다양한 응용 분야에서 활용되고 있습니다. 티오에테르 변 형은 막의 안정성, 기능 및 상호 작용에 미치는 영향으로 주목받고 있다. 나피온과 같은 양이온 교환 막은 인기 있는 상업적 옵션이지만 비용은 여전히 상당한 제약으로 남아 있다. 반면, 설폰화 폴리(아릴렌 티오에테르 설폰)(SPTES)와 같은 공중합체 는 경제적으로 실용적이며 연료 전지의 핵심 요구 사항인 설폰화 정도를 쉽게 제어할 수 있다. 탈염은 염분은 거부되고 압력 은 구동력이기 때문에 막 분리 공정이 활용되는 또 다른 분야이다. 이 리뷰에서는 위에서 언급한 발전 사항에 대해 논의한다.