Search

This investigation delves into the development of cupric oxide functionalized activated carbon fabrics (ACF@CuO) as a filter material for self-decontaminating protective clothing designed to counter chemical warfare agents (CWAs). Three variants of samples were developed by controlling functionalization levels (7.5%, 13.0%, 16.0% w/w) through optimized precursor concentrations, integrating CuO particles onto ACF surfaces. Comprehensive analysis using techniques like FTIR, BET surface area, SEM, EDX, STEM, XRD, TGA, and XPS explored the material's properties. The studies concentrated on evaluating the performance through kinetic studies of self-detoxification of CWA Sarin (GB) using developed ACF@CuO materials analyzed by Gas Chromatography-Mass Spectrometry (GC-MS). ACF@CuO with 16% w/w functionalization exhibited superior self-decontamination against GB, achieving 92.87% efficiency within 18 hours, in contrast to 26.55% for ACF alone. Additionally, materials were tested for tensile strength, air-permeability, and Sulfur Mustard breakthrough time (HDBTT) as per IS 17377:2020 standard. The material with 13% w/w functionalization emerged as the recommended filter layer for CWA protective clothing designed for defense and civilian safety applications. The significant improvements in the self-decontamination efficiency of the material were attributed to the synergistic effects of ACF's adsorption capabilities combined with the decontamination properties of CuO rod-shaped crystals embedded in the surface of ACF. [ABSTRACT FROM AUTHOR]

Chemical warfare agents (CWAs) have become a pivotal concern for the global community and spurred a wide spectrum of research for the development of new generation protective materials. Herein, a highly effective self-detoxifying filter consisting of in-situ immobilized Zirconium hydroxide [Zr(OH)4] over woven activated carbon fabric [Zr(OH)4@W-ACF] is presented for the removal of CWAs. It was prepared to harness the synergistic effect of high surface area of W-ACF, leads to high dispersion of CWAs and high phosphilicity and reactivity of [Zr(OH)4]. The synthesized materials were characterized by ATR-FTIR, EDX, SEM, TEM, XPS, TGA, and BET surface area analyzer. The kinetics of in-situ degradation of CWAs over Zr(OH)4@W-ACF were studied and found to be following the first-order reaction kinetics. The rate constant was found to be 0.244 min−1 and 2.31 × 10−2 min−1 for sarin and soman, respectively over Zr(OH)4@W-ACF. The potential practical applicability of this work was established by fabricating Zr(OH)4@W-ACF as reactive adsorbent layer for protective suit, and found to be meeting the specified criteria in terms of air permeability, tearing strength and nerve agent permeation as per TOP-08-2-501A:2013 and IS-17380:2020. The degradation products of CWAs were analyzed with NMR and GC–MS. The combined properties of dual functional textile with reactive material are expected to open up new exciting avenues in the field of CWAs protective clothing and thus find diverse application in defence and environmental sector. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by electrical neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-Blinks (EB) and Cardiac Activity (CA) are two of the most common types of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG) and chest electrodes, as in electrocardiography (EKG), however this complicates imaging setup, decreases patient comfort, and often induces further artifacts from facial twitching and postural muscle movement. We propose an EOG- and EKG-free approach to identify eye-blink, cardiac, or neuronal signals for automated artifact suppression. Our contributions are two-fold. First, we combine a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA) and a highly accurate classifier constructed as a deep 1-D Convolutional Neural Network. Second, we visualize the features learned to reveal what features the model uses and to bolster user confidence in our model's training and potential for generalization. We train and test three variants of our method on resting state MEG data from 49 subjects. Our cardiac model achieves a 96% sensitivity and 99% specificity on the set-aside test-set. Our eye-blink model achieves a sensitivity of 85% and specificity of 97%. This work facilitates automated MEG processing for both, clinical and research use, and can obviate the need for EOG or EKG electrodes.