Coupling of two chemical reactions through an oxygen transporting perovskite membrane : thermodynamic and kinetic control

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Hydrogen Purification through a Highly Stable Dual-Phase Oxygen-Permeable Membrane

Using oxygen permeable membranes (OPMs) to upgrade low-purity hydrogen is a promising concept for high-purity H2 production. At high temperatures, water dissociates into hydrogen and oxygen. The oxygen permeates through OPM and oxidizes hydrogen in a waste stream on the other side of the membrane. Pure hydrogen can be obtained on the water-splitting side after condensation. However, the existing Co- and Fe-based OPMs are chemically instable as a result of the over-reduction of Co and Fe ions under reducing atmospheres. Herein, a dual-phase membrane Ce0.9Pr0.1O2−δ-Pr0.1Sr0.9Mg0.1Ti0.9O3−δ (CPO-PSM-Ti) with excellent chemical stability and mixed oxygen ionic-electronic conductivity under reducing atmospheres was developed for H2 purification. An acceptable H2 production rate of 0.52 mL min−1 cm−2 is achieved at 940 °C. No obvious degradation during 180 h of operation indicates the robust stability of CPO-PSM-Ti membrane. The proven mixed conductivity and excellent stability of CPO-PSM-Ti give prospective advantages over existing OPMs for upgrading low-purity hydrogen. © 2020 The Authors. Angewandte Chemie International Edition published by Wiley-VCH Gmb

2011-2012 Master Class - Elmar Oliveira (Violin)

https://spiral.lynn.edu/conservatory_masterclasses/1070/thumbnail.jp

Flexible Alignment Super-Resolution Network for Multi-Contrast MRI

Magnetic resonance images play an essential role in clinical diagnosis by acquiring the structural information of biological tissue. However, during acquiring magnetic resonance images, patients have to endure physical and psychological discomfort, including irritating noise and acute anxiety. To make the patient feel cozier, technically, it will reduce the retention time that patients stay in the strong magnetic field at the expense of image quality. Therefore, Super-Resolution plays a crucial role in preprocessing the low-resolution images for more precise medical analysis. In this paper, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast magnetic resonance images Super-Resolution. The core of multi-contrast SR is to match the patches of low-resolution and reference images. However, the inappropriate foreground scale and patch size of multi-contrast MRI sometimes lead to the mismatch of patches. To tackle this problem, the Flexible Alignment module is proposed to endow receptive fields with flexibility. Flexible Alignment module contains two parts: (1) The Single-Multi Pyramid Alignmet module serves for low-resolution and reference image with different scale. (2) The Multi-Multi Pyramid Alignment module serves for low-resolution and reference image with the same scale. Extensive experiments on the IXI and FastMRI datasets demonstrate that the FASR-Net outperforms the existing state-of-the-art approaches. In addition, by comparing the reconstructed images with the counterparts obtained by the existing algorithms, our method could retain more textural details by leveraging multi-contrast images

2011-2012 Master Class - Jon Kimura Parker (Piano)

Jon Kimura Parker Performance (January 15, 2012) - Programhttps://spiral.lynn.edu/conservatory_masterclasses/1066/thumbnail.jp

Developing a class of dual atom materials for multifunctional catalytic reactions

Dual atom catalysts, bridging single atom and metal/alloy nanoparticle catalysts, offer more opportunities to enhance the kinetics and multifunctional performance of oxygen reduction/evolution and hydrogen evolution reactions. However, the rational design of efficient multifunctional dual atom catalysts remains a blind area and is challenging. In this study, we achieved controllable regulation from Co nanoparticles to CoN4 single atoms to Co2N5 dual atoms using an atomization and sintering strategy via an N-stripping and thermal-migrating process. More importantly, this strategy could be extended to the fabrication of 22 distinct dual atom catalysts. In particular, the Co2N5 dual atom with tailored spin states could achieve ideally balanced adsorption/desorption of intermediates, thus realizing superior multifunctional activity. In addition, it endows Zn-air batteries with long-term stability for 800 h, allows water splitting to continuously operate for 1000 h, and can enable solar-powered water splitting systems with uninterrupted large-scale hydrogen production throughout day and night. This universal and scalable strategy provides opportunities for the controlled design of efficient multifunctional dual atom catalysts in energy conversion technologies

A deep learning method for foot-type classification using plantar pressure images

Background: Flat foot deformity is a prevalent and challenging condition often leading to various clinical complications. Accurate identification of abnormal foot types is essential for appropriate interventions.Method: A dataset consisting of 1573 plantar pressure images from 125 individuals was collected. The performance of the You Only Look Once v5 (YOLO-v5) model, improved YOLO-v5 model, and multi-label classification model was evaluated for foot type identification using the collected images. A new dataset was also collected to verify and compare the models.Results: The multi-label classification algorithm based on ResNet-50 outperformed other algorithms. The improved YOLO-v5 model with Squeeze-and-Excitation (SE), the improved YOLO-v5 model with Convolutional Block Attention Module (CBAM), and the multilabel classification model based on ResNet-50 achieved an accuracy of 0.652, 0.717, and 0.826, respectively, which is significantly higher than those obtained using the ordinary plantar-pressure system and the standard YOLO-v5 model.Conclusion: These results indicate that the proposed DL-based multilabel classification model based on ResNet-50 is superior in flat foot type detection and can be used to evaluate the clinical rehabilitation status of patients with abnormal foot types and various foot pathologies when more data on patients with various diseases are available for training

Roadmap for Sustainable Mixed Ionic‐Electronic Conducting Membranes

Mixed ionic‐electronic conducting (MIEC) membranes have gained growing interest recently for various promising environmental and energy applications, such as H₂ and O₂ production, CO₂ reduction, O₂ and H₂ separation, CO₂ separation, membrane reactors for production of chemicals, cathode development for solid oxide fuel cells, solar‐driven evaporation and energy‐saving regeneration as well as electrolyzer cells for power‐to‐X technologies. The purpose of this roadmap, written by international specialists in their fields, is to present a snapshot of the state‐of‐the‐art, and provide opinions on the future challenges and opportunities in this complex multidisciplinary research field. As the fundamentals of using MIEC membranes for various applications become increasingly challenging tasks, particularly in view of the growing interdisciplinary nature of this field, a better understanding of the underlying physical and chemical processes is also crucial to enable the career advancement of the next generation of researchers. As an integrated and combined article, it is hoped that this roadmap, covering all these aspects, will be informative to support further progress in academics as well as in the industry‐oriented research toward commercialization of MIEC membranes for different applications