The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

The electronic structures of graphene nanoflakes (GNFs) were estimated for various shapes, sizes, symmetries, and edge configurations. The Hückel molecular orbital (HMO) method was employed as a convenient way of handling the variety of possible GNF structures, since its simplicity allows the rapid solution of large system problems, such as tailoring optoelectronic characteristics of molecule containing large number of carbon atoms. The HMO method yielded the electronic structures with respect to the energy state eigenvalues, with results comparable to those obtained by other approaches, such as the tightbinding method reported elsewhere. The analyses included the consideration of various types of edge configurations of 68 GNF systems grouped by their geometric shape, reflecting symmetry. It was inferred that GNFs in the small length scale regimes, below 1 nm, which are effectively small polycyclic aromatic hydrocarbon molecules, exhibit the optoelectronic characteristic of quantum dots. This is due to the widely spaced discrete energy states, together with large energy gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). With increasing size this arrangement evolves into graphene-like ones, as revealed by the narrowing HOMO-LUMO gaps and decreasing energy differences between eigenstates. However, the changes in electronic structure are affected by the symmetries, which are related to the geometric shapes and edge configurations.

This study determined the minimum size of a representative molecular structure for use in future dynamic analyses of asphalt binders. The minimum representative size, considering factors such as aging, additive types, and temperature variations, was established using density and radial distribution functions. This approach ensures that the structure reflects temperature-dependent property changes, which are critical characteristics of asphalt binders. In this study, the structure of asphalt-binder molecules was generated using the composition proposed by Li and Greenfield (2014) for AAA1. To assess the appropriateness of the molecular structure size, we generated additional structures, X2 and X3, maintaining the same composition as X1, but with two and three times the number of molecules, respectively, as suggested by Li and Greenfield (2014). Silica and lignin were considered as additives, and the aging conditions examined included unaged, short-term aging, and long-term aging. In addition, 11 temperature conditions were investigated. The density and radial distribution functions were plotted and analyzed. The variables influencing the density and radial distribution functions were set as the aging degree of the asphalt binder (unaged, short-term aging, long-term aging), 11 temperature conditions ranging from 233 to 433 K in 20 K intervals, structure size (X1, X2, and X3), and the presence of additives (no additives, silica, and lignin). For density, clear differences were observed based on the degree of aging, temperature conditions, and presence of additives, whereas the structure size did not significantly affect the density. In terms of radial distribution functions, the X1 structure reflected differences based on the degree of aging and the presence of additives but was limited in exhibiting temperature-dependent variations. In contrast, the X3 structure effectively captured temperature-dependent trends, indicating that the size of the molecular structure is crucial when evaluating energy calculations or physical tensile strength, necessitating careful assessment.

This paper explores a convergent approach that combines advanced informatics and computational science to develop road-paving materials. It also analyzes research trends that apply artificial-intelligence technologies to propose research directions for developing new materials and optimizing them for road pavements. This paper reviews various research trends in material design and development, including studies on materials and substances, quantitative structure–activity/property relationship (QSAR/QSPR) research, molecular data, and descriptors, and their applications in the fields of biomedicine, composite materials, and road-construction materials. Data representation is crucial for applying deep learning to construction-material data. Moreover, selecting significant variables for training is important, and the importance of these variables can be evaluated using Pearson’s correlation coefficients or ensemble techniques. In selecting training data and applying appropriate prediction models, the author intends to conduct future research on property prediction and apply string-based representations and generative adversarial networks (GANs). The convergence of artificial intelligence and computational science has enabled transformative changes in the field of material development, contributing significantly to enhancing the performance of road-paving materials. The future impacts of discovering new materials and optimizing research outcomes are highly anticipated.

To improve the lithium-ion battery performance and stability, a conducting polymer, which can simultaneously serve as both a conductive additive and a binder, is introduced into the anode. Water-soluble polyaniline:polystyrene sulfonate (PANI:PSS) can be successfully prepared through chemical oxidative polymerization, and their chemical/mechanical properties are adjusted by varying the molecular weight of PSS. As a conductive additive, the PANI with a conjugated double bond structure is introduced between active materials or between the active material and the current collector to provide fast and short electrical pathways. As a binder, the PSS prevents short circuits through strong π‒π stacking interaction with active material, and it exhibits superior adhesion to the current collector, thereby ensuring the maintenance of stable mechanical properties, even under high-speed charging/discharging conditions. Based on the synergistic effect of the intrinsic properties of PANI and PSS, it is confirmed that the anode with PANI:PSS introduced as a binder has about 1.8 times higher bonding strength (0.4 kgf/20 mm) compared to conventional binders. Moreover, since active materials can be additionally added in place of the generally added conductive additives, the total cell capacity increased by about 12.0%, and improved stability is shown with a capacity retention rate of 99.3% even after 200 cycles at a current rate of 0.2 C.

그래핀 산화물(GO), 폴리에틸렌 글리콜 다이아크릴레이트(PEGDA), 폴리에틸렌 글리콜 메틸 에터 아크릴레이트 (PEGMEA)의 나노복합체를 자외선 광중합을 통해 합성하였다. GO는 가교된 폴리에틸렌 옥사이드(XPEO) 매트릭스 내에 최 대 1.0 wt% 농도까지 균일하게 분산시켰다. 더 높은 농도에서는 GO가 응집되는 경향을 보였다. 잘 분산된 GO는 친수성 PEO 사슬과 추가적인 화학적 가교 네트워크를 형성했다. XPEO-GO 나노복합체는 GO 농도에 따라 기계적 강도 및 염과 가 스에 대한 차단 특성이 향상된 것으로 나타났다. 이 연구는 다양한 GO 농도와 플레이크 크기를 가진 XPEO-GO 하이드로겔 의 제조 및 특성화를 다루고 있다. 이러한 특성은 나노복합 하이드로겔이 강화된 XPEO 기반 바이오소재 및 고급 항균성 한 외여과(UF) 친수성 코팅에서의 잠재적 응용 가능성을 시사한다.

음이온 교환막(AEM) 수전해용 AEM 소재 개발은 재생 에너지를 활용한 수소 생산 기술을 개선하는 데 중요한 역할을 한다. 이러한 소재를 설계하고 최적화하는 데 분자동역학 전산모사가 유용하게 사용되지만, 전산모사 결과의 정확도 는 사용된 force-field에 크게 의존한다. 본 연구의 목적은 AEM 소재의 구조와 이온 전도 특성을 예측할 때 force-field 선택 이 미치는 영향을 체계적으로 조사하는 것이다. 이를 위해 poly(spirobisindane-co-aryl terphenyl piperidinium) (PSTP) 구조를 모델 시스템으로 선택하고 COMPASS III, pcff, Universal, Dreiding 등 네 가지 주요 force-field를 비교 분석하였다. 각 force-field의 특성과 한계를 평가하기 위해 298~353 K의 온도 범위에서 수화 채널 형태, 물 분자와 수산화 이온의 분포, 수산 화 이온 전도성을 계산하였다. 이를 통해 AEM 소재의 분자동역학 전산모사에 가장 적합한 force-field를 제시하고, 고성능 AEM 소재 개발을 위한 계산 지침을 제공하고자 한다.