Durability test with fuel starvation using a Pt/CNF catalyst in PEMFC

In this study, a catalyst was synthesized on carbon nanofibers [CNFs] with a herringbone-type morphology. The Pt/CNF catalyst exhibited low hydrophilicity, low surface area, high dispersion, and high graphitic behavior on physical analysis. Electrodes (5 cm2) were prepared by a spray method, and the durability of the Pt/CNF was evaluated by fuel starvation. The performance was compared with a commercial catalyst before and after accelerated tests. The fuel starvation caused carbon corrosion with a reverse voltage drop. The polarization curve, EIS, and cyclic voltammetry were analyzed in order to characterize the electrochemical properties of the Pt/CNF. The performance of a membrane electrode assembly fabricated from the Pt/CNF was maintained, and the electrochemical surface area and cell resistance showed the same trend. Therefore, CNFs are expected to be a good support in polymer electrolyte membrane fuel cells

Prediction of Diblock Copolymer Morphology via Machine Learning

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes.Comment: 51 page, 11 Figures and 5 figures in the S

Mesoscale Simulations of Polymer Solution Self-Assembly: Selection of Model Parameters within an Implicit Solvent Approximation

Coarse-grained modeling is an outcome of scientific endeavors to address the broad spectrum of time and length scales encountered in polymer systems. However, providing a faithful structural and dynamic characterization/description is challenging for several reasons, particularly in the selection of appropriate model parameters. By using a hybrid particle- and field-based approach with a generalized energy functional expressed in terms of density fields, we explore model parameter spaces over a broad range and map the relation between parameter values with experimentally measurable quantities, such as single-chain scaling exponent, chain density, and interfacial and surface tension. The obtained parameter map allows us to successfully reproduce experimentally observed polymer solution assembly over a wide range of concentrations and solvent qualities. The approach is further applied to simulate structure and shape evolution in emulsified block copolymer droplets where concentration and domain shape change continuously during the process

Coarse-Grained Modeling of EUV Patterning Process Reflecting Photochemical Reactions and Chain Conformations

Enabling extreme ultraviolet lithography (EUVL) as a viable and efficient sub-10 nm patterning tool requires addressing the critical issue of reducing line edge roughness (LER). Stochastic effects from random and local variability in photon distribution and photochemical reactions have been considered the primary cause of LER. However, polymer chain conformation has recently attracted attention as an additional factor influencing LER, necessitating detailed computational studies with explicit chain representation and photon distribution to overcome the existing approach based on continuum models and random variables. We developed a coarse-grained molecular simulation model for an EUV patterning process to investigate the effect of chain conformation variation and stochastic effects via photon shot noise and acid diffusion on the roughness of the pattern. Our molecular simulation demonstrated that final LER is most sensitive to the variation in photon distributions, while material distributions and acid diffusion rate also impact LER; thus, the intrinsic limit of LER is expected even at extremely suppressed stochastic effects. Furthermore, we proposed and tested a novel approach to improve the roughness by controlling the initial polymer chain orientation

Roughness at Interfaces of Photoresists from a Perspective of Molecular Modeling

Developing new materials or process conditions for photoresists, to reduce the roughness in the pattern while maintaining the high resolution and sensitivity upon light exposure, has been considered as a key for the success of high resolution (sub-10 nm) patterning technology. While inherent uncertainties due to the photon shot noise and photochemistry are considered as one of main sources of the roughness, additional factors due to the configurations of polymer chains and its distribution at the interface should be taken into account as the required roughness scales down to the molecule size. Hence beyond the former stochastic modeling, we attempted to introduce molecular simulation/modeling approach to find the inherent limits on the sharpness at the interface focusing on width and fluctuations of the interface between the exposed and unexposed area, and chain configurations during/after development. Coarse-grained polymer chain models allow us to understand the relation between the system conditions and relevant chain configurations near the interfaces.status: accepte

Facile synthesis of hollow Fe-N-C hybrid nanostructures for oxygen reduction reactions

A novel Fe-N-C composite material with a hollow graphitized nanostructure is prepared by pyrolyzing iron-chelating, nitrogen-containing precursors adsorbed on carbon black spheres for use in the oxygen reduction reaction (ORR) of polymer electrolyte membrane fuel cells (PEMFCs). The resulting composite hybrid exhibits excellent electrocatalytic activity and a four-electron dominated ORR pathway in an alkaline solution. The efficient catalytic activity of the prepared Fe-N-C is mainly attributed to the effective incorporation of nitrogen and iron atoms into the graphitized matrices and high electrical conductivity due to the interconnected structure. Furthermore, the hybrid material shows superior catalytic durability in the alkaline medium even after 3000 cyclic voltammetry cycles, making it a good candidate for a cathodic electrocatalyst in PEMFCs. (C) 2014 Elsevier B. V. All rights reserved.N

Clinical predictors of treatment response to tiotropium add-on therapy in adult asthmatic patients: From multicenter real-world cohort data in Korea

Background: Tiotropium, a long-acting muscarinic antagonist, is recommended for add-on therapy to inhaled corticosteroids (ICS)-long-acting beta 2 agonists (LABA) for severe asthma. However, real-world studies on the predictors of response to tiotropium are limited. We investigated the real-world use of tiotropium in asthmatic adult patients in Korea and we identified predictors of positive response to tiotropium add-on. Methods: We performed a multicenter, retrospective, cohort study using data from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). We enrolled asthmatic participants who took ICS-LABA with at least 2 consecutive lung function tests at 3-month intervals. We compared tiotropium users and non-users, as well as tiotropium responders and non-responders to predict positive responses to tiotropium, defined as 1) increase in forced expiratory volume in 1 s (FEV1) ≥ 10% or 100 mL; and 2) increase in asthma control test (ACT) score ≥3 after 3 months of treatment. Results: The study included 413 tiotropium users and 1756 tiotropium non-users. Tiotropium users had low baseline lung function and high exacerbation rate, suggesting more severe asthma. Clinical predictors for positive response to tiotropium add-on were 1) positive bronchodilator response (BDR) [odds ratio (OR) = 6.8, 95% confidence interval (CI): 1.6–47.4, P = 0.021] for FEV1 responders; 2) doctor-diagnosed asthma-chronic obstructive pulmonary disease overlap (ACO) [OR = 12.6, 95% CI: 1.8–161.5, P = 0.024], and 3) initial ACT score <20 [OR = 24.1, 95% CI: 5.45–158.8, P < 0.001] for ACT responders. FEV1 responders also showed a longer exacerbation-free period than those with no FEV1 increase (P = 0.014), yielding a hazard ratio for the first asthma exacerbation of 0.5 (95% CI: 0.3–0.9, P = 0.016). Conclusions: The results of this study suggest that tiotropium add-on for uncontrolled asthma with ICS-LABA would be more effective in patients with positive BDR or ACO. Additionally, an increase in FEV1 following tiotropium may predict a lower risk of asthma exacerbation