Korea Advanced Institute of Science and Technology

The global construction industry faces a crucial challenge reconciling economic growth with environmental sustainability, notably due to the significant environmental impact of cement production, particularly in countries like Pakistan. As the demand for cement grows, so does the carbon footprint and environmental degradation, necessitating the exploration of sustainable alternatives like sugarcane bagasse ash (SBA), a byproduct of sugarcane processing, to mitigate these issues while also addressing rising costs in concrete production. Embracing SBA offers a promising avenue to alleviate environmental concerns and enhance the sustainability of the construction sector. This study investigated the SBA properties and effectiveness as a viscosity modifying agent (VMA) in self-compacting concrete (SCC), examining varying SBA content effects on fresh and hardened SCC properties. The hydration and microstructure properties were evaluated by using X-ray diffraction (XRD), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) to investigate SBA-based SCC. The results indicate that SBA has the potential to enhance mechanical and microstructural properties by possibly increasing the formation of Calcium Silicate Hydrate (CSH) gel. Adding 5% SBA demonstrated favorable fresh properties while incorporating up to 15% SBA showed improvements in compressive strength. Overall, adding SBA to cement manufacturing during clinkerization can reduce environmental pollution and lower production costs.

Lithium–sulfur batteries (LSBs) are considered promising next‐generation batteries due to their high energy density (>500 W h kg⁻¹). However, LSBs exhibit an unsatisfactory energy density (<400 W h kg⁻¹) and cycle life (<300 cycles) because of the shuttle effect caused by soluble lithium polysulfide (LiPS) intermediates and the sluggish conversion reaction kinetics caused by insulating sulfur (S8) and lithium sulfide (Li2S). Although various types of catalysts, including metal‐based compounds to single‐atom catalysts, have been reported to address these issues, most catalysts exhibited limited catalytic activity under practical lean electrolyte conditions (<5 µL mg⁻¹). A comprehensive understanding of the synthetic strategy and catalytic mechanism of catalysts is essential for their design, but understanding the electronic effects of the catalysts and LiPS is more important. Furthermore, the electronic design of these catalysts is not well understood. In this review, we introduce the catalytic mechanisms in LSBs and discuss catalyst design strategies in terms of electronic effects on the interactions between reactants and catalysts, with a primary focus on heterogeneous catalytic systems. We additionally consider how the electronic property of homogeneous systems, particularly redox mediators, affects catalytic behavior under lean electrolyte conditions and propose future research directions for catalyst development in LSBs.

Accumulation of aquatic organisms on submerged sea vessel surfaces increases drag, resulting in higher fuel consumption and more greenhouse gas emissions. Polymer coatings hold promise to combat marine biofouling as more environmentally friendly alternatives to metal-based paints. While hydrophilic coatings are known to lose antifouling efficacy upon sediment adsorption, how to arrange hydrophobic segments and develop a sufficiently thick coating layer to suppress diatom deposition in the presence of silt and maximize the antifouling performance has been unknown. Here, we developed an effective and scalable antifouling coating by combining the polydopamine precoating method with well-defined amphiphilic copolymers synthesized using a controlled polymerization technique. Reversible addition-fragmentation chain transfer copolymerization of zwitterion-containing hydrophilic sulfobetaine methacrylate and hydrophobic trifluoroethyl methacrylate produced the target copolymers with control of composition, molecular weight, and sequence. Zr(IV)-mediated coordination bonds between polydopamine and sulfobetaine groups yielded >20 nm thick films stable for a month in seawater. The random copolymer sequence exposed both hydrophilic and hydrophobic groups on the outermost coating surface. Synergistic repelling diatom and silt led to the best antifouling performance at the optimal hydrophilic-hydrophobic balance. Superior antifouling efficacy was retained for a month and was also effective on stainless steel, suggesting the potential for practical application.

This paper presents a novel approach to posterior inference on simulation input parameters that can effectively address the challenge of multi-modality. As simulations become more complex, accounting for the possibility of a multi-modal posterior distribution is crucial. However, traditional posterior inference methods often fall short; their loss functions are structured such that once the simulation outcome approximates the real-world observation, further exploration ceases, leading to a local optimum and a mode-collapsed posterior distribution. To overcome this challenge, we propose a new approach, namely Sequential Neural Joint Estimation, which integrates two significant research directions to enforce further model exploration by maximizing the newly added mutual information loss. Experimental results demonstrate that the proposed approach performs better in multi-modal posteriors, making it a promising method for handling complex simulations that require accurate posterior inference.

Background and Aims Heart failure (HF) remains a significant clinical challenge due to its diverse aetiologies and complex pathophysiology. The molecular alterations specific to distinct cell types and histological patterns during HF progression are still poorly characterized. This study aimed to explore cell-type- and histology-specific gene expression profiles in cardiomyopathies. Methods Ninety tissue cores from 44 participants, encompassing various forms of cardiomyopathy and control samples with diverse histological features, were analysed using the GeoMx Whole Human Transcriptome Atlas. Data on cell types, clinical information, and histological features were integrated to examine gene expression profiles in cardiomyopathy. Results The study characterized the cellular composition of ventricular myocardium and validated the GeoMx platform’s efficiency in compartmentalizing specific cell types, demonstrating high accuracy for cardiomyocytes but limitations for endothelial cells and fibroblasts. Differentially expressed genes, including UCHL1 from cardiomyocytes, were associated with degeneration, while CCL14, ACKR1, and PLVAP from endothelial cells were linked to fibrosis. Multiplex immunohistochemistry and integrative analysis of prior sc/snRNA-seq data identified a PLVAP, ACKR1, and CCL14-positive pro-inflammatory endothelial cell subtype linked to fibrosis in HF. Downregulation of ribosomal proteins in cardiomyocytes was associated with myocyte disarray in hypertrophic cardiomyopathy. Additionally, pronounced inflammatory responses were observed in end-stage HF. Combined histological and clinical analysis identified CRIP3, PFKFB2, and TAX1BP3 as novel contributors to HF pathogenesis. Conclusions These findings highlight the critical role of cell-enriched and histology-specific transcriptome mapping in understanding the complex pathophysiological landscape of failing hearts, offering molecular insights and potential therapeutic targets for future interventions.

Scalar dissipation rate (SDR) evolution in a stopping turbulent jet was analysed using direct numerical simulations and a theoretical approach. After the jet is stopped, a deceleration wave for the SDR propagates downstream with a speed similar to that for axial velocity. Upstream of the deceleration wave, mean centreline SDR becomes proportional to axial distance, and inversely proportional to the square of time. After passing of the deceleration wave, normalised radial profiles of SDR and its axial, radial and azimuthal components reach self-similar states, denoted decelerating self-similar profiles, which are different from their steady-state counterparts. Production and destruction terms in the mean SDR transport equation remain dominant in the decelerating self-similar state. The theoretical approach provides an explicit prediction for the radial profile of a turbulent fluctuation term of the mean SDR transport equation. Three turbulent SDR models are validated, and modifications suitable for the decelerating jet are proposed, based on a self-similarity analysis.

Amyloid‐β (Aβ) plays a crucial role in Alzheimer's disease pathogenesis. Understanding how Aβ overexpression alters the proteome of individual brain cell types is essential but challenging due to the nature of brain tissue, which contains intermingled various cell types. The current methods for cell‐type‐specific proteomics either require genetic modifications or complex cell isolation, limiting their use. This study introduces a novel method, in situ cell‐type‐specific proteome analysis using antibody‐mediated biotinylation (iCAB), which applies immunohistochemistry with biotin‐tyramide to target cell‐specific proteins directly in tissue. Applied to 5xFAD mice, iCAB enables us to identify ≈8000 cell‐type‐specific proteomes with significantly more differentially expressed proteins than traditional bulk proteome methods, pinpointing unique pathways such as mRNA processing, calcium regulation, and phagocytosis for neurons, astrocytes, and microglia, respectively. This study reports in‐depth the cell‐type‐specific brain proteome landscape of the human Aβ overexpression mouse model for the first time using an innovative tool that is powerful, straightforward, and applicable to both animal models and human tissues, without the need for prior genetic alterations.

Polyolefins are versatile materials for various purposes, but their functionality should be fine‐tuned for target applications including the mitigation of adverse environmental impacts. Producing such polymers with desired properties requires catalysts that can control polymerization at an atomistic level. However, complex reaction mechanisms and very limited experimental data make it difficult to design new efficient catalysts using conventional computational and data‐driven approaches. Here, we present a pragmatic strategy based on data‐efficient predictive models combined with a genetic algorithm to design new catalysts for controlled ethylene/hexene copolymerization. By deriving the chemically intuitive descriptors from the mechanistic analysis of the polymerization, we achieved the promising predictive models with small data applicable to various core structures and different experimental conditions, respectively. We screened catalysts through a virtual screening scheme combining a genetic algorithm and predictive models using chemically intuitive descriptors and considered their synthesizability through the manual inspections of experts. As a result, we successfully designed nine catalysts with desired comonomer ratios and diverse core structures.

Low-carbon steel plates play a crucial role in various industries such as automotive, energy, or structures. The hot rolling process to produce the steel plate may lead to the occurrence of edge cracks which requires to remove the affected edges, reducing the productivity in the manufacturing process. This study uses a stress-based fracture model considering the strain rate and temperature to predict the edge crack initiation of Si-added low-carbon steel during hot rolling. A new hardening model considering the strain rate and the temperature is proposed to model the target material including the softening behavior. Verification has been conducted by comparing finite element analysis with experiments from the hot rolling simulator under the same conditions. Using the strain-based fracture model led to an incorrect prediction of the edge crack initiation.

Importance Fluorescence lifetime imaging (FLIm) is a molecular imaging technique used to visualize the biochemical composition of atherosclerosis. Novel dual-modal imaging using optical coherence tomography (OCT)-FLIm has the potential to provide both microstructural and biocompositional information on coronary plaques; however, it needs validation for clinical application. Objective To investigate the clinical feasibility and safety of OCT-FLIm for characterizing plaque compositions in patients with coronary artery disease (CAD) undergoing revascularization therapy. Design, Setting, and Participants A prospective, open-label, single-center diagnostic feasibility study involving 40 patients with significant CAD requiring coronary revascularization. This first-in-human clinical study of the novel intracoronary OCT-FLIm imaging was conducted between February and August 2022. The analyses were performed from August 2022 to July 2023. Interventions An OCT-FLIm system with 2.6-F catheters was constructed. All patients underwent OCT-FLIm for target/culprit and nontarget/nonculprit lesions during coronary revascularization. Intravascular ultrasound imaging was performed for comparison. Main Outcomes and Measures The primary outcome was to assess the FLIm-derived molecular readouts of prespecified plaque compositions. The secondary outcome was the feasibility of OCT-FLIm in determining target/culprit plaque compositions across different subsets of atherosclerotic disease activity: (1) acute coronary syndrome (ACS) vs chronic stable angina (CSA) and (2) angiographic rapid disease progression vs nonprogressive controls. Results We prospectively enrolled 40 patients (mean [SD] age, 63.1 [8.1] years; 32 men [80.0%]), of whom 20 presented with ACS and 20 with CSA. OCT provided the structural features of plaques, and FLIm characterized the molecular signatures of atheroma compositions, including macrophages, healed plaques, superficial calcification, and fibrosis, in a reproducible manner. Fluorescence lifetime (FL) values of the plaque compositions correlated with findings from prior autopsy studies. Plaque inflammation was significantly greater in patients with ACS than those with CSA. The mean (SD) of inflammation-FL was 7.59 (0.96) nanoseconds for patients with ACS vs 6.46 (0.87) nanoseconds for patients with CSA ( P < .001). The healed plaque phenotype was more prominently distributed in the segments of rapid disease progression than in nonprogressive controls. The mean (SD) healed plaque-FL was 5.31 (0.20) nanoseconds for the rapidly progressive lesions vs 4.81 (0.30) nanoseconds for the rapidly nonprogressive lesions ( P < .001). All patients underwent OCT-FLIm safely without adverse clinical events. Conclusions and Relevance This diagnostic feasibility study found that an OCT-FLIm structural-molecular intracoronary imaging is clinically feasible and safe for the comprehensive characterization of human atheromas, supporting its potential role in the diagnosis and biological understanding of high-risk plaques.

Functionalization of carboranes, icosahedral boron−carbon molecular clusters, is of great interest as they have wide applications in medicinal and materials chemistry. Thus, site- and enantioselective synthesis of carboranes requires complete control of the reaction. Herein, we describe the asymmetric Rh(II)-catalyzed insertion reactions of carbenes into cage B–H bond of carboranes. This reaction thereby generates carboranes possessing a carbon-stereocenter adjacent to cage boron of the carborane, in excellent site- and enantioselectivity under mild reaction conditions. The fully computed transition structures of Rh(II)-catalyzed carbene insertion process through density functional theory are reported. These B–H insertion transition structures, in conjunction with topographical proximity surfaces analyses, visually reveal the region between the carborane and the phthalimide ligands responsible for the selectivities of this reaction.

Rational design of catalytic nanomaterials is essential for developing high‐performance fuel cell catalysts. However, structural degradation and elemental dissolution during operation pose significant challenges to achieving long‐term stability. Herein, the development of multi‐grained NiPt nanocatalysts featuring an atomically ordered Ni3Pt5 phase within intragrain is reported. Ultrasound‐assisted synthesis facilitates atomic transposition by supplying sufficient diffusion energy along grain boundaries, enabling unprecedented phase formation. The Ni3Pt5 embedded nanocatalysts exhibit outstanding proton exchange membrane fuel cell performance under both light‐duty and heavy‐duty vehicle conditions, with significantly reduced Ni dissolution. Under light‐duty vehicle conditions, the catalyst achieves a mass activity of 0.94 A mgPt⁻¹ and a 421 mA cm⁻² current density (@ 0.8 V in air), retaining 78% of its initial mass activity after long‐term operation. Under heavy‐duty vehicle conditions, the multi‐grained nanocrystal demonstrates only an 8% decrease in Pt utilization, a 5% power loss, and a 13 mV voltage drop, surpassing U.S. Department of Energy (DOE) durability targets. This study underscores the critical role of the atomically ordered Ni3Pt5 phase in stabilizing multi‐grained NiPt nanocrystals, enhancing both durability and catalytic activity. These findings establish Ni3Pt5 embedded nanocatalysts as promising candidate for next‐generation PEMFC applications, addressing key challenges in long‐term operation.

Towards the real-world deployment of autonomous vehicles, it is crucial that autonomous driving systems plan safe, collision-free trajectories. However, generating a collision-free trajectory around potential obstacles requires solving a non-convex problem for conventional optimization-based planners. This is characterized by multiple local minima reflecting possible maneuvers in different homotopies that the ego vehicle can execute to avoid collision. Finding a solution among these maneuvers involves combinatorial decision-making, which incurs increasing computational costs as more obstacles are considered. In this paper, we propose a hybrid approach using learning-based decision-making for optimization planning. Our learning-based decision-maker predicts homotopic boundary constraints for the optimization planner, effectively determining a maneuver for the ego vehicle without any combinatorial decision process. In addition, the homotopic bounds enable us to reformulate the non-convex optimization problem into a more tractable quadratic programming (QP) problem. We evaluate our approach in unsignalized intersection scenarios using a simulator, demonstrating that it achieves better driving performance than existing decision-making and planning methods in non-convex driving situations.

Aging is associated with the accumulation of senescent cells, which are triggered by tissue injury response and often escape clearance by the immune system. The specific traits and diversity of these cells in aged tissues, along with their effects on the tissue microenvironment, remain largely unexplored. Despite the advances in single-cell and spatial omics technologies to understand complex tissue architecture, senescent cell populations are often neglected in general analysis pipelines due to their scarcity and the technical bias in current omics toolkits. Here we used the physical properties of tissue to enrich the age-associated fibrotic niche and subjected them to single-cell RNA sequencing and single-nuclei ATAC sequencing (ATAC-seq) analysis and named this method fibrotic niche enrichment sequencing (FiNi-seq). Fibrotic niche of the tissue was selectively enriched based on its resistance to enzymatic digestion, enabling quasi-spatial analysis. We profiled young and old livers of male mice using FiNi-seq, discovered Wif1- and Smoc1-producing mesenchymal cell populations showing senescent phenotypes, and investigated the early immune responses within this fibrotic niche. Finally, FiNi–ATAC-seq revealed age-associated epigenetic changes enriched in fibrotic niche cells. Thus, our quasi-spatial, single-cell profiling method allows the detailed analysis of initial aging microenvironments, providing potential therapeutic targets for aging prevention.

We present the development of a machine-learning (ML) model for predicting the congruency of compound melts by utilizing a combination of density functional theory-calculated formation energies and a database of experimental melting reactions. Among the various ML models tested, the XGBoost model is found to be the most suitable. Therefore, the XGBoost model is trained with a labeled compound database to determine whether a compound melted congruently. Feature importance from the trained model is extracted and compared to identify descriptors crucial for predicting melting behavior. Notably, the change in slope of the DFT convex hull at the composition of the compound (i.e., the “convex hull sharpness”) exhibited significantly higher importance than the other features. The methodology employed in this study has the potential to enhance the design of alloy processing and can be applied to more complex higher-order alloys.