유리상 고분자 멤브레인은 높은 투과도와 선택도를 동시에 달성하면서도 에너지 소비가 낮아, 고성능 기체 분리 용 멤브레인 후보로 주목받아 왔다. 그러나 기존 고분자 멤브레인은 Robeson 상한선으로 표현되는 투과도-선택도 간의 고유 한 상충관계에 의해 성능이 제한되는 한계를 지닌다. 최근 수년간, 고유 자유부피가 큰 유리상 고분자, 특히 고유 미세다공성 고분자(PIMs) 및 6FDA 기반 폴리이미드와 같은 고성능 재료의 개발이 활발히 이루어지며 이러한 병목 현상을 극복하고 있 다. 고분자 주 사슬 구조 설계, 후 합성 기능화, 고분자 블렌딩, 다공성 필러를 포함한 혼합 매질 멤브레인(mixed-matrix membrane, MMM) 제조, 열재배열 공정 등 다양한 전략을 통해 기체 분리 성능이 크게 향상되었다. 본 총설에서는 유리상 고 분자 기반 기체 분리 멤브레인의 최신 연구 동향을 다룬다. 특히, PIM-1 및 유도체, 6FDA 기반 폴리이미드, MMM을 중심으 로 어떻게 투과도-선택도 상충관계, 물리적 노화, 가소화 저항성과 같은 핵심 기술적 과제를 해결하는지를 다룬다. 최신 문헌 분석을 통해, 유리상 고분자 멤브레인이 기체 분리 성능의 새로운 기준을 제시하고 있으며, 탄소 포집부터 천연가스 처리에 이르기까지 상업적 적용 가능성이 높아지고 있음을 논의한다. 마지막으로, 이러한 멤브레인 기술이 산업적 응용으로 이어지 기 위한 주요 과제와 향후 연구 방향에 대해 고찰한다.

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 %.

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.