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Sensitivity analysis of volatile organic compounds to PM2.5 concentrations in a representative industrial city of Korea
No full textThis study aims to analyze the sensitivity of volatile organic compounds (VOCs) to ambient concentrations of fine particles ( PM2.5) in the representative industrial city of Ulsan, Korea. For the calculation of sensitivity coefficients between VOCs and PM2.5 (SVOCs-PM2.5), PM2.5 data were obtained from an air quality monitoring station, and their corresponding 6-h average concentrations of VOCs (alkanes, alkenes, aromatics, and total VOCs) were measured at the Yeongnam intensive air monitoring station. The air monitoring period was divided into the warm-hot season (May???October 2020) and the cold season (November 2020???January 2021). The sensitivity coefficients in the low pollution period of PM2.5 (5 < PM2.5 ??? 15 ??g/m3) were higher and much higher than those in the medium pollution period (15 < PM2.5 ??? 35 ??g/m3) and high pollution period (35 < PM2.5 ??? 50 ??g/m3), respectively. This result indicates that the change ratios of PM2.5 concentrations to the background ( PM2.5 ??? 5 ??g/m3) per unit concentration change of VOCs (particularly alkenes) in the high PM2.5 pollution period were much higher than those in the low pollution period. This also indicates that PM2.5 concentrations above 35 ??g/m3 were more easily affected by the unit concentration change of VOCs (particularly alkenes) than those below 15 ??g/m3. The average sensitivity coefficients during the cold season increased in a range of 23???125% as compared to those during the warm-hot season, except the alkenes-PM2.5 sensitivity with a decrease of 7%. It means that the impact of VOCs (except alkenes) on PM2.5 concentrations was relatively low in the cold season. However, in the cold season, the alkenes might contribute more to PM2.5 formation, particularly over the high pollution period, having PM2.5 > 35 ??g/m3, than other VOC groups. The result of this study can be a basis for establishing PM2.5 management plans in industrial cities with large VOC emissions
TS-Net: A Deep Learning Framework for Automated Assessment of Longitudinal Tumor Volume Changes in an Orthotopic Breast Cancer Model using MRI
No full textMonitoring tumor volume changes in response to therapeutic agents is a critical step in preclinical drug development. Here, an automated magnetic resonance imaging (MRI)-based approach is proposed using a deep learning framework for tracking longitudinal tumor volume changes in an orthotopic breast cancer model treated with chemotherapy. Longitudinal magnetic resonance images are employed to track changes in tumor volume over time, using an untreated group and a doxorubicin-treated group as the dataset to evaluate treatment effects. Our approach, called Tumor Segmentation-Net (TS-Net), involves replacing the encoder of U-Net with a pre-trained ResNet34 to improve performance. The model was trained using a sample size of n=19 from the untreated group and then subsequently assessed on both the untreated group (n=5) and treated group (n=6). The correlation between the tumor volume determined from the ground truth and that obtained from the trained output was strong ( R2 =0.984, slope=0.996). These results can lead to automated three-dimensional visualization of different longitudinal volume changes with and without treatment. Notably, for small tumors with volumes between 2 and 5 mm 3 , the proposed TS-Net demonstrated an average Dice similarity coefficient score of 0.85, indicating the ability to reliably detect early tumors that may often be missed. Our approach offers a promising tool for preclinical evaluation of tumor volume changes and treatment efficacy in animal models
Amine-assisted catechol-based nanocoating on ultrasmall iron oxide nanoparticles for high-resolution T1 angiography
No full textSurface engineered iron oxide nanoparticles (IONPs) with catecholic ligands have been investigated as alternative T1 contrast agents. However, complex oxidative chemistry of catechol during IONP ligand exchange causes surface etching, heterogeneous hydrodynamic size distribution, and low colloidal stability because of Fe3+ mediated ligand oxidation. Herein, we report highly stable and compact (???10 nm) Fe3+ rich ultrasmall IONPs functionalized with a multidentate catechol-based polyethylene glycol polymer ligand through amine-assisted catecholic nanocoating. The IONPs exhibit excellent stability over a broad range of pHs and low nonspecific binding in vitro. We also demonstrate that the resultant NPs have a long circulation time (???80 min), enabling high resolution T1 magnetic resonance angiography in vivo. These results suggest that the amine assisted catechol-based nanocoating opens a new potential of metal oxide NPs to take a step forward in exquisite bio-application fields
A multifunctional peroxidase-based reaction for imaging, sensing and networking of spatial biology
No full textPeroxidase is a heme-containing enzyme that reduces hydrogen peroxide to water by extracting electron(s) from aromatic compounds via a sequential turnover reaction. This reaction can generate various aromatic radicals in the form of short-lived "spray" molecules. These can be either covalently attached to proximal proteins or polymerized via radical-radical coupling. Recent studies have shown that these peroxidase-generated radicals can be utilized as effective tools for spatial research in biological systems, including imaging studies aimed at the spatial localization of proteins using electron microscopy, spatial proteome mapping, and spatial sensing of metabolites (e.g., heme and hydrogen peroxide). This review may facilitate the wider utilization of these peroxidase-based methods for spatial discovery in cellular biology
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Advanced condition-based self-monitoring of composites damaged area under multiple impacts using Monte Carlo based prognostics
No full textStudies on self-sensing system under multiple impacts are limited. Furthermore, real-time prognostics research using electromechanical behavior for impact-damage growth is rare and the impact-damaged area analysis has limited in self-sensing. In this paper, the health state of the carbon-fiber-reinforced plastic samples were monitored in real time utilizing self-sensing data. Damage analysis was conducted through C-scan and crosssectional analysis, and the results were compared and correlated with those of failure analysis based on realtime electromechanical behavior during multiple impacts. Moreover, the relationship between electromechanical behavior and the impact-damaged area was investigated. The damage propagation during multiple impacts was identified in real time. Furthermore, the electromechanical behavior was predicted to prognosticate the damage propagation in the samples under multiple impacts using a particle filter. The RMSE of the impactdamaged area determined from the predicted electromechanical behavior using real-time prognostics tools was lower than 15 mm2. Moreover, the prediction accuracy according to data acquired was investigated. An advanced condition-based monitoring methodology can monitor current and future health states and damage propagation under 2 J and 3 J of multiple impacts that overcomes the previous self-sensing research. Therefore, this study showed high applicability and guidelines for future self-sensing research fields
Enhancing the Marangoni flow by inner side chain engineering in nonfullerene acceptors for reproducible blade coating-processed organic solar cell manufacturing
No full textThe industrial-scale, uniform film production of active layers is a prerequisite for high-performance, reproducible organic solar cells (OSCs), becoming a significant challenge. Blade coating, one of the most suitable protocols for industrial-scale OSC manufacturing, can be significantly affected by evaporation-driven convective flows (e.g., capillary and Marangoni flows), which directly influence film uniformity. Here, we present in-depth studies on how convective flows in blade coating-processed OSC fabrication depend on the inner side chain lengths of nonfullerene acceptors (L8-i-EB, L8-i-EH, and L8-i-BO). By analyzing the device performance in nine different regions in a blade-coated substrate, we find that the degree of variations in power conversion efficiency ranges from 15.61% to 16.85% (standard deviation (sigma) of 0.38%) for the L8-i-EB-based device, 15.31% to 17.20% (sigma of 0.57%) for the L8-i-EH-based device, and 13.92% to 16.66% (sigma of 0.97%) for the L8-i-BO-based device. This demonstrates that compared with the others, the L8-i-EB-based device with a shorter inner side chain enables higher reproductivity in blade coating-processed OSC fabrication, attributed to its superior film uniformity induced by the enhanced inward-directional Marangoni flow while counteracting the capillary flow. This study highlights the importance of the Marangoni flow effect and its contribution to realizing reproducible blade coating-processed OSCs
Anode analysis and modelling hydrodynamic behaviour of the multiphase flow field in circular PEM water electrolyzer
No full textA numerical study of the behaviour of the multiphase flow of an anode-porous transport layer of an aqueous electrolyzer with a proton-exchange membrane (PEM) of an aqueous electrolyzer is presented. A mixture model was used to study the flow behaviour in a circular-shaped anode box to determine the efficient design of a PEM water electrolyzer. As a result of the simulation, it was found that the model pressure drop profiles obtained by computational fluid dynamics (CFD) are in good agreement with the corresponding experimental data. In addition, the performance profile was predicted considering various PEM water electrolyzer cell improvement factors compared to the Bassline model. The results of the behaviour of two-phase flows with different velocity, pressure and volume fraction profiles, as well as with porous regions in the centre, are presented, which showed a key difference in the flow profile for various inlet and outlet flow configurations. In addition, the flow volume fraction behaviour was obtained at higher and lower water and oxygen rates. Three-dimensional (3D) modelling predicted flow characteristics for thre