Inha University

The efficiency of carbon dioxide (CO2) adsorption in carbonaceous materials is primarily influenced by their microporosity and thermodynamic affinity for CO2. However, achieving optimal heteroatom doping and precise micropore engineering through advanced activation techniques remains a significant challenge. We introduce a solvent‐free one‐pot method using polythiophene, melamine, and KOH to prepare highly microporous, heteroatom‐co‐doped carbons (NSC). This approach leverages sulfur from polythiophene, nitrogen from melamine, and the activation agent KOH to enhance CO2 capture performance. Our results demonstrate that the optimized sample, NSC‐800, achieves a CO2 adsorption capacity of 280.5 mg g⁻¹ at 273 K and 1 bar, attributed to its high nitrogen (6.5 at.%) and sulfur (3.4 at.%) contents, a specific surface area of 2888 m² g⁻¹, and a micropore volume of 1.685 cm³ g⁻¹. The moderate isosteric heat of adsorption (27.7 kJ mol⁻¹) indicates a primarily physisorption‐driven mechanism, as confirmed by close alignment with the pseudo‐first‐order polynomial model (R² > 0.99) across temperatures of 303–323 K. This study reveals that NSC‐800 also displays efficient regeneration after ten cycles of CO2 adsorption–desorption under flue gas conditions (15% CO2 and 85% N2 at 313 K), highlighting its potential as a regenerable, energy‐efficient adsorbent for practical CO2 capture applications.

In this article, we propose methods for simulating the detailed flow of dispersed fire-flake particles in response to the movement of a flame, using chaotic advection and various buoyant flow techniques. Furthermore, we utilize these techniques to gather a synthetic dataset of detailed fire-flake particles and extend the solver to represent the movement of fire-flake particles based on learning-based approaches. Fire-flake particles not only exhibit unique and complex movements on their own, but they are also significantly influenced by the movement of the flame and the surrounding airflow. Modeling the flow of fire-flake particles realistically is challenging due to their chaotic and constantly changing nature. Instead of explicitly modeling the complex fire-flake particles in the flame based on fluid mechanics, this article efficiently approximates the chaotic motion of fire-flake particles using two approaches: 1) chaotic advection to simulate the flow and 2) controlled buoyant flow, which varies based on the temperature and lifespan of the fire-flake particles. Additionally, we collect a fire-flake dataset through this simulation and extends the solver to learn the representation of fire-flake motion using neural networks. During the advection process of fire-flake particles, a new stochastic solver is used to calculate the subgrid interactions between them. In this article, not only we propose algorithms that can express these techniques through numerical simulation, but we also extend this solver using artificial intelligence techniques to enable learning representation. By using the proposed technique, it is possible to efficiently simulate fire-flake particles with various movements in chaotic regions, and it allows for more detailed representation of fire-flake particles compared to existing methods. Unlike the typical random walk approach that adds noise randomly to the movement, our method considers the size and direction of the flame. This allows us to express fire-flake particles stably in most scenes without the need for parameter adjustments.

Electroencephalogram (EEG) signal is receiving much attention from recent studies since it is highly associated with intrinsic emotion. However, EEG signals contain underlying factors of variations across different sessions of the same subject, which make it difficult to learn temporal relationships between successive time steps. To disentangle invariant features, we propose a feature re-weighting mechanism on the extracted EEG features for temporal sequence modeling. Based on this method, our proposed model, called Convolutional Channel Modulator for Transformer and LSTM networks (CCMTL), extracts emotion-related inter-channel correlations using convolution operations and emphasizes important features by generating a channel attention map. This attention map is then used to perform matrix multiplication on the extracted features, which helps the subsequent Transformer to focus on important affective features. Furthermore, the sequential temporal modeling enhances the overall model’s capability to understand temporal relationships both in global and sequential contexts. Experimental settings on public EEG emotion datasets demonstrate the superiority of the proposed CCMTL, surpassing six state-of-the-art models. Our code is publicly available at https://github.com/affctivai/CCMTL.

Background This study aimed to determine the physical activity level and protein intake of older people with sarcopenia and investigate the adequate protein intake of older people in Korea. Methods A total of 1215 older people were recruited from the ninth Korea National Health and Nutrition Examination Survey. Participants’ physical activity, handgrip strength, appendicular skeletal muscle mass and food intake were assessed. Results A one-way ANOVA revealed that the normal group exhibited significantly higher values for moderate-to-vigorous intensity physical activity (male p=0.035 and female p=0.028), total intake kcal (p<0.001), carbohydrate (p<0.001), proteins (p<0.001) and fats (male p<0.001 and female p=0.005) compared with all other groups. Participants who met the recommended protein intake demonstrated significantly higher muscle mass (OR=2.16) and muscle strength (OR=2.31) compared with those who did not meet the recommended protein intake. A significant positive correlation between protein intake and skeletal muscle index (r=0.354, p<0.001) and handgrip strength (r=0.358, p<0.001) was observed across all participants. Conclusion Older individuals who do not meet the recommended protein intake are more likely to experience a loss of muscle mass and strength compared with those who receive the recommended protein intake.

Coordinating self-interested agents in multi-agent systems to achieve system-level objectives presents significant challenges due to the inherent misalignment between individual and collective goals. Mechanism design offers a solution by employing a bi-level optimization framework, where a designer agent intervenes in the reward structures to incentivize desired behaviors among self-interested agents. However, a major obstacle in reward optimization lies in solving multi-agent reinforcement learning problems given a reward structure. This paper addresses this challenge by introducing a novel algorithm that leverages successor features (SFs) at both levels of the optimization. Specifically, SFs help reduce the number of design iterations at the upper level by using previously learned equilibria as biased information sources and accelerate equilibrium learning at the lower level by transferring equilibria from previously solved Markov games. This innovative approach leads to significant computational savings, making the process up to ten times faster compared to traditional methods.

Prostate cancer is the second most common malignancy and the sixth leading cause of cancer-related death in men worldwide. Radical prostatectomy (RP) is the standard treatment for localized prostate cancer, but the procedure often results in postoperative erectile dysfunction (ED). The poor efficacy of phosphodiesterase 5 inhibitors after surgery highlights the need to develop new therapies to enhance cavernous nerve regeneration and improve the erectile function of these patients. In the present study, we aimed to examine the potential of heparin-binding epidermal growth factor-like growth factor (HB-EGF) in preserving erectile function in cavernous nerve injury (CNI) mice. We found that HB-EGF expression was reduced significantly on the 1 st day after CNI in penile tissue. Ex vivo and in vitro studies showed that HB-EGF promotes major pelvic ganglion neurite sprouting and neuro-2a (N2a) cell migration. In vivo studies showed that exogenous HB-EGF treatment significantly restored the erectile function of CNI mice to 86.9% of sham levels. Immunofluorescence staining showed that mural and neuronal cells were preserved by inducing cell proliferation and reducing apoptosis and reactive oxygen species production. Western blot analysis showed that HB-EGF upregulated protein kinase B and extracellular signal-regulated kinase activation and neurotrophic factor expression. Overall, HB-EGF is a major promising therapeutic agent for treating ED in postoperative RP.

This paper leverages data from February 6, 2023, Kahramanmaras (Turkiye) Earthquake (Mw 7.8) to evaluate seismic risk and assess bridge damage through a fuzzy synthetic approach (FSA). A novel hierarchical damage classification framework is introduced, integrating critical factors such as ground conditions, structural characteristics, and seismic intensity. By analyzing data from 331 bridges affected by eight major historical earthquakes, the study underscored the influence of foundation depth, construction quality, and distance to fault rupture on structural resilience. Notably, 65% of damaged bridges were within 40 km of the distance to fault rupture, with oblique span orientations (45° to 65°) showing heightened susceptibility to seismic forces. To enhance resilience against earthquakes, the findings advocated for the adoption of deep foundations, advanced materials, and optimized structural designs. Consistent with field observations, the study reinforces the utility of FSA in enabling informed decision-making for disaster risk mitigation and is also beneficial for future seismic resilience design of bridges.

The use of nanostructures as drug delivery vehicles for a wide range of anticancer medications to lessen their severe side effects by delivering them to the tumor cell location targeted...

Two‐dimensional (2D) MXene structure, versatile surface reactivity, flexibility, wearability, and outstanding thermal attributes make them highly suitable for numerous applications. This comprehensive review based on MXenes delves into the potential uses of fewer assessed applications, such as materials, solar thermal desalination, energy harvesting, electrochemical sensing, environmental remediation, and removal of heavy metal ions. Several industries associated with the summarized applications include hybrid photovoltaic thermal systems, energy storage, energy conversion, soft electronics, and other industries. Further, the review underscores the importance and future guidance of continued research in the MXene field to harness the potential benefits of not only summarized applications but also diverse applications.

The native defects of Bi2Se3 were investigated using scanning tunneling microscopy (STM). STM images revealed both atomic-scale and nanometer-scale features. The most frequently observed defects appeared as depressions at the atomic scale and were identified as Se vacancies in the top Se layer. Nanometer-scale defects, including triangular, cloverleaf-shaped, and rounded features, were also observed and attributed to subsurface defects due to their extended size. While the triangular and cloverleaf-shaped defects are associated with interstitial Se atoms and substitutional Bi atoms at subsurface Se sites, respectively, the exact nature of the rounded defects remains undetermined. In addition to these point defects, previously unreported linear defects were identified. These linear defects, appearing as stripe-like features across the images, are attributed to wrinkles in the top Se layer of the Bi2Se3 surface, likely formed during sample growth and subsequently exposed by cleavage.

This paper addresses the positive sampled-data observer-based output-feedback control problem for Takagi–Sugeno fuzzy models with uncertainties. Leveraging an aperiodic sampling framework driven by event-triggering mechanisms, we propose a sampled-data design technique aimed at achieving positive $\mathcal{L}_{2}$–$\mathcal{L}_{\infty}$ disturbance attenuation. A distinctive fuzzy OBOF controller is separately designed to enhance closed-loop positivity. This study investigates the Zeno-free behavior of the designed controller. A numerical example demonstrates the effectiveness of our proposed methodology.

Fast spatial contouring of the complex refractive index (n + ik) of semiconducting materials is a much sought‐after goal since the advent of semiconductor‐related industries. This study develops a novel metrology to shape the refractive index modulation of materials using hyperspectral phase microscopy by maximizing the light‐matter interaction of physical properties. The facile, non‐destructive, and wide‐field hyperspectral phase technique realizes efficient visualization of the spatially resolved refractive index nature induced by strain within and among examined MoS2 materials. Furthermore, numerical analyses based on a steady‐state transfer matrix clarify that the spectral phase difference (Δϕ) is selectively sensitive to the modulation of refractive index (n) but not of extinction coefficient (k) under certain wavelength ranges. This dependence is associated with wavelength and the thickness of the dielectric layer on the substrates. Simple linear relation between n and Δϕ for ≈100 nm of SiO2, dielectric material supporting MoS2, enables to visualize the excitonic A and B band modulation, and furthermore, refractive index with fairly high precision (coefficient of determination, R² > 0.97 in the wavelength range of 530–630 nm).

The interlayer connection of circuits within stretchable electronic devices is crucial for enhancing their performance. Conventional methods for interlayer circuit connections are fragile and prone to mechanical deformation, prompting the need for new approaches. Although various methods have been proposed for creating interconnections in stretchable circuits, a universally efficient method for junctions remains to be fully developed. In this study, we propose a method to connect the interlayers of stretchable circuits using electro-driven silver nanowires. By comparing the conventional junction method with our proposed method, we verify the efficiency of the new approach. The results reveal that applying a high electric field during the solidification of the circuit interlayer aligns the isotropic state of the silver nanowires parallel to the field, thereby increasing the conductivity of the interlayer junction. Additionally, the study demonstrates that using silver nanowires for the junction provides better mechanical stability to the junction compared to that of the bulk material, as verified through mechanical stretch experiments. An in-depth analysis with mathematical modeling is presented at the end of the study. The proposed method is a promising approach for creating junctions in stretchable devices and is expected to pave the way for increasing their efficiency.

Li- and Mn-rich (LMR) cathodes have emerged as promising candidates for next-generation lithium-ion batteries (LIBs) due to their high energy density and reliance on earth-abundant elements. Unlike conventional layered transition metal (TM) oxides, LMRs utilize both TM and anion (oxygen) redox reactions to achieve superior capacity. However, their widespread commercialization is hindered by voltage fade, a persistent issue characterized by a gradual decline in the operating voltage upon cycling, which leads to significant energy density loss. This review provides a comprehensive understanding of the fundamental mechanisms contributing to voltage fade, including irreversible phase transitions, transition metal migration, oxygen loss, and microstructural degradation. Furthermore, we discuss state-of-the-art strategies for mitigating voltage fade, including elemental doping, surface coatings, composition modulation, and concentration gradient engineering. Each approach is critically evaluated in terms of its effectiveness in stabilizing the cathode structure and improving long-term electrochemical performance. By integrating recent advancements in material design, this review outlines a strategic roadmap for developing structurally robust and electrochemically stable LMR cathodes, paving the way for their practical implementation in high-energy density LIBs.

Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) methods, specifically convolutional neural networks (CNNs), are extremely accurate at identifying malignant lesions. Deep models that have been pre-trained and tailored through transfer learning and fine-tuning become faster and more effective, even when data is scarce. This paper implements a state-of-the-art Hybrid Learning Network that combines the Progressive Resizing approach and Principal Component Analysis (PCA) for enhanced cervical cancer diagnostics of whole slide images (WSI) slides. ResNet-152 and VGG-16, two fine-tuned DL models, are employed together with transfer learning to train on augmented and progressively resized training data with dimensions of 224 × 224, 512 × 512, and 1024 × 1024 pixels for enhanced feature extraction. Principal component analysis (PCA) is subsequently employed to process the combined features extracted from two DL models and reduce the dimensional space of the feature set. Furthermore, two ML methods, Support Vector Machine (SVM) and Random Forest (RF) models, are trained on this reduced feature set, and their predictions are integrated using a majority voting approach for evaluating the final classification results, thereby enhancing overall accuracy and reliability. The accuracy of the suggested framework on SIPaKMeD data is 99.29% for two-class classification and 98.47% for five-class classification. Furthermore, it achieves 100% accuracy for four-class categorization on the LBC dataset.

Solid-state batteries (SSBs) have emerged as a promising alternative technology for advancing global electrification efforts. The SSBs offer significant advantages over conventional electrolyte-based batteries, including enhanced safety, increased energy density, and improved performance. Their non-flammability, enhanced thermal and mechanical stability, and lower self-discharge rates make them particularly promising for future energy solutions. However, their prevalent implementation in large-scale industries is inhibited by inadequate ionic conductivity and the interfacial challenges associated with solid-state electrolytes (SSEs). These challenges include suboptimal solid–solid contact, grain boundary limitations, poor wettability, and unfavorable phenomena such as dendrite growth, interface voids, interdiffusion layer formation, and lattice mismatch. This comprehensive review meticulously examines recent developments and prospects in SSEs, categorizing them into halide, sulfide, oxide, hydride, and polymer types. It then analyzes the challenges and interfacial limitations of SSBs, including dendrite growth, voids, cracks, contact issues, lattice mismatch, and interdiffusion. In addition, potential solutions for enhancing interfacial adherence between electrodes and SSEs are outlined. Furthermore, recent trends in the SSB industry, including successfully commercialized products, are highlighted. Finally, this review explores the future potential of SSEs in advanced SSBs, projecting their significant industrial impact.

The synthesis of tricyclic fused pyrazolines from N-silyl enamines and nitrile imines is presented. N-silyl enamines are an emerging class of enamines that can be used as free enamine surrogates. The N-silyl enamines from readily available isoquinolines underwent [3 + 2] cycloaddition with nitrile imines to form pyrazolines. This cascade protocol entailed the in-situ utilization of both N-silyl enamine and nitrile imine intermediates.