Non-uniform line-of-sight measurements of nitric oxide using mid-infrared Faraday rotation spectroscopy.

Traditional absorption spectroscopy relies on detecting intensity variations along the line-of-sight to gauge average concentration and temperature. While methods like profile fitting and temperature binning offer insights into the non-uniformity of the path, they fall short of accurately capturing the precise spatial distribution with a single line-of-sight measurement. We propose a novel measurement scheme for non-uniformly distributed concentration of nitric oxide (NO) along the line-of-sight utilizing a single laser and path, by incorporating Faraday rotation spectroscopy with magnetic fields changing over time and space. We validate the proposed scheme by measuring a path of two regions in series with different NO concentrations, and comparing the measurement results with direct absorption spectroscopy of each respective region. In this work, the tuning range of the interband cascade laser used is from 1899.42 to 1900.97 cm-1, encompassing two sets of spectral lines corresponding to the 2Π1/2 and 2Π3/2 transitions of NO's R(6.5). The average relative uncertainty in the concentration measurement for each region is estimated to be within 1.5%, with the concentration for individual absorption cells ranging from 0.2% to 0.8%.

Amorphous Iridium Oxide-Integrated Anode Electrodes with Ultrahigh Material Utilization for Hydrogen Production at Industrial Current Densities.

Herein, ionomer-free amorphous iridium oxide (IrOx) thin electrodes are first developed as highly active anodes for proton exchange membrane electrolyzer cells (PEMECs) via low-cost, environmentally friendly, and easily scalable electrodeposition at room temperature. Combined with a Nafion 117 membrane, the IrOx-integrated electrode with an ultralow loading of 0.075 mg cm-2 delivers a high cell efficiency of about 90%, achieving more than 96% catalyst savings and 42-fold higher catalyst utilization compared to commercial catalyst-coated membrane (2 mg cm-2). Additionally, the IrOx electrode demonstrates superior performance, higher catalyst utilization and significantly simplified fabrication with easy scalability compared with the most previously reported anodes. Notably, the remarkable performance could be mainly due to the amorphous phase property, sufficient Ir3+ content, and rich surface hydroxide groups in catalysts. Overall, due to the high activity, high cell efficiency, an economical, greatly simplified and easily scalable fabrication process, and ultrahigh material utilization, the IrOx electrode shows great potential to be applied in industry and accelerates the commercialization of PEMECs and renewable energy evolution.

Facilitating COVID recognition from X-rays with computer vision models and transfer learning.

Multimedia data plays an important role in medicine and healthcare since EHR (Electronic Health Records) entail complex images and videos for analyzing patient data. In this article, we hypothesize that transfer learning with computer vision can be adequately harnessed on such data, more specifically chest X-rays, to learn from a few images for assisting accurate, efficient recognition of COVID. While researchers have analyzed medical data (including COVID data) using computer vision models, the main contributions of our study entail the following. Firstly, we conduct transfer learning using a few images from publicly available big data on chest X-rays, suitably adapting computer vision models with data augmentation. Secondly, we aim to find the best fit models to solve this problem, adjusting the number of samples for training and validation to obtain the minimum number of samples with maximum accuracy. Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the accuracy and timeliness of radiological interpretations of COVID in a cost-effective manner. Finally, we outline applications of this work during COVID and its recovery phases with future issues for research and development. This research exemplifies the use of multimedia technology and machine learning in healthcare.

Electrochemically Grown Ultrathin Platinum Nanosheet Electrodes with Ultralow Loadings for Energy-Saving and Industrial-Level Hydrogen Evolution.

Nanostructured catalyst-integrated electrodes with remarkably reduced catalyst loadings, high catalyst utilization and facile fabrication are urgently needed to enable cost-effective, green hydrogen production via proton exchange membrane electrolyzer cells (PEMECs). Herein, benefitting from a thin seeding layer, bottom-up grown ultrathin Pt nanosheets (Pt-NSs) were first deposited on thin Ti substrates for PEMECs via a fast, template- and surfactant-free electrochemical growth process at room temperature, showing highly uniform Pt surface coverage with ultralow loadings and vertically well-aligned nanosheet morphologies. Combined with an anode-only Nafion 117 catalyst-coated membrane (CCM), the Pt-NS electrode with an ultralow loading of 0.015 mgPt cm-2 demonstrates superior cell performance to the commercial CCM (3.0 mgPt cm-2), achieving 99.5% catalyst savings and more than 237-fold higher catalyst utilization. The remarkable performance with high catalyst utilization is mainly due to the vertically well-aligned ultrathin nanosheets with good surface coverage exposing abundant active sites for the electrochemical reaction. Overall, this study not only paves a new way for optimizing the catalyst uniformity and surface coverage with ultralow loadings but also provides new insights into nanostructured electrode design and facile fabrication for highly efficient and low-cost PEMECs and other energy storage/conversion devices.

Highly Porous Iridium Thin Electrodes with Low Loading and Improved Reaction Kinetics for Hydrogen Generation in PEM Electrolyzer Cells.

Highly efficient electrodes with simplified fabrication and low cost are highly desired for the commercialization of proton exchange membrane electrolyzer cells (PEMECs). Herein, highly porous Ir-coated thin/tunable liquid/gas diffusion layers with honeycomb-structured catalyst layers were fabricated as anode electrodes for PEMECs via integrating a facile and fast electroplating process with efficient template removal. Combined with a Nafion 117 membrane, a low cell voltage of 1.842 V at 2000 mA/cm2 and a high mass activity of 4.16 A/mgIr at 1.7 V were achieved with a low Ir loading of 0.27 mg/cm2, outperforming most of the recently reported anode catalysts. Moreover, the thin electrode shows outstanding stability at a high current density of 1800 mA/cm2 in the practical PEMEC. Moreover, with in-situ high-speed visualizations in PEMECs, the catalyst layer structure's impact on real-time electrochemical reactions and mass transport phenomena was investigated for the first time. Increased active sites and improved multiphase transport properties with favorable bubble detachment and water diffusion for the honeycomb-structured electrode are revealed. Overall, the significantly simplified ionomer-free honeycomb thin electrode with low catalyst loading and remarkable performance could efficiently accelerate the industrial application of PEMECs.

Discovering Reactant Supply Pathways at Electrode/PEM Reaction Interfaces Via a Tailored Interface-Visible Characterization Cell.

In situ and micro-scale visualization of electrochemical reactions and multiphase transports on the interface of porous transport electrode (PTE) materials and solid polymer electrolyte (SPE) has been one of the greatest challenges for electrochemical energy conversion devices, such as proton exchange membrane electrolyzer cells (PEMECs), CO2 reduction electrolyzers, PEM fuel cells, etc. Here, an interface-visible characterization cell (IV-CC) is developed to in situ visualize micro-scaled and rapid electrochemical reactions and transports in PTE/SPE interfaces. Taking the PEMEC of a green hydrogen generator as a study case, the unanticipated local gas blockage, micro water droplets, and their evolution processes are successfully visualized on PTE/PEM interfaces in a practical PEMEC device, indicating the existence of unconventional reactant supply pathways in PEMs. Further comprehensive results reveal that PEM water supplies to reaction interfaces are significantly impacted with current densities. These results provide critical insights about the reaction interface optimization and mass transport enhancement in various electrochemical energy conversion devices.

Robust Copper-Based Nanosponge Architecture Decorated by Ruthenium with Enhanced Electrocatalytic Performance for Ambient Nitrogen Reduction to Ammonia.

Electrochemical conversion of nitrogen to green ammonia is an attractive alternative to the Haber-Bosch process. However, it is currently bottlenecked by the lack of highly efficient electrocatalysts to drive the sluggish nitrogen reduction reaction (N2RR). Herein, we strategically design a cost-effective bimetallic Ru-Cu mixture catalyst in a nanosponge (NS) architecture via a rapid and facile method. The porous NS mixture catalysts exhibit a large electrochemical active surface area and enhanced specific activity arising from the charge redistribution for improved activation and adsorption of the activated nitrogen species. Benefiting from the synergistic effect of the Cu constituent on morphology decoration and thermodynamic suppression of the competing hydrogen evolution reaction, the optimized Ru0.15Cu0.85 NS catalyst presents an impressive N2RR performance with an ammonia yield rate of 26.25 µg h-1 mgcat.-1 (corresponding to 10.5 µg h-1 cm-2) and Faradic efficiency of 4.39% as well as superior stability in alkaline medium, which was superior to that of monometallic Ru and Cu nanostructures. Additionally, this work develops a new bimetallic combination of Ru and Cu, which promotes the strategy to design efficient electrocatalysts for electrochemical ammonia production under ambient conditions.

Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model.

Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the collaboration efficiency, a better way is to formulate the robot to surmise what kind of assistance a human coworker needs and naturally take the right action at the right time. This paper proposes a prediction-based human-robot collaboration model for assembly scenarios. An embedded learning from demonstration technique enables the robot to understand various task descriptions and customized working preferences. A state-enhanced convolutional long short-term memory (ConvLSTM)-based framework is formulated for extracting the high-level spatiotemporal features from the shared workspace and predicting the future actions to facilitate the fluent task transition. This model allows the robot to adapt itself to predicted human actions and enables proactive assistance during collaboration. We applied our model to the seats assembly experiment for a scale model vehicle and it can obtain a human worker's intentions, predict a coworker's future actions, and provide assembly parts correspondingly. It has been verified that the proposed framework yields higher smoothness and shorter idle times, and meets more working styles, compared to the state-of-the-art methods without prediction awareness.

Y-Net: a dual-branch deep learning network for nonlinear absorption tomography with wavelength modulation spectroscopy.

This paper demonstrates a new method for solving nonlinear tomographic problems, combining calibration-free wavelength modulation spectroscopy (CF-WMS) with a dual-branch deep learning network (Y-Net). The principle of CF-WMS, as well as the architecture, training and performance of Y-Net have been investigated. 20000 samples are randomly generated, with each temperature or H2O concentration phantom featuring three randomly positioned Gaussian distributions. Non-uniformity coefficient (NUC) method provides quantitative characterizations of the non-uniformity (i.e., the complexity) of the reconstructed fields. Four projections, each with 24 parallel beams are assumed. The average reconstruction errors of temperature and H2O concentration for the testing dataset with 2000 samples are 1.55% and 2.47%, with standard deviations of 0.46% and 0.75%, respectively. The reconstruction errors for both temperature and species concentration distributions increase almost linearly with increasing NUC from 0.02 to 0.20. The proposed Y-Net shows great advantages over the state-of-the-art simulated annealing algorithm, such as better noise immunity and higher computational efficiency. This is the first time, to the best of our knowledge, that a dual-branch deep learning network (Y-Net) has been applied to WMS-based nonlinear tomography and it opens up opportunities for real-time, in situ monitoring of practical combustion environments.

Exploring the Impacts of Conditioning on Proton Exchange Membrane Electrolyzers by In Situ Visualization and Electrochemistry Characterization.

For a proton exchange membrane electrolyzer cell (PEMEC), conditioning is an essential process to enhance its performance, reproducibility, and economic efficiency. To get more insights into conditioning, a PEMEC with Ir-coated gas diffusion electrode (IrGDE) was investigated by electrochemistry and in situ visualization characterization techniques. The changes of polarization curves, electrochemical impedance spectra (EIS), and bubble dynamics before and after conditioning are analyzed. The polarization curves show that the cell efficiency increased by 9.15% at 0.4 A/cm2, and the EIS and Tafel slope results indicate that both the ohmic and activation overpotential losses decrease after conditioning. The visualization of bubble formation unveils that the number of bubble sites increased greatly from 14 to 29 per pore after conditioning, at the same voltage of 1.6 V. Under the same current density of 0.2 A/cm2; the average bubble detachment size decreased obviously from 35 to 25 µm. The electrochemistry and visualization characterization results jointly unveiled the increase of reaction sites and the surface oxidation on the IrGDE during conditioning, which provides more insights into the conditioning and benefits for the future GDE design and optimization.

Tuning Catalyst Activation and Utilization Via Controlled Electrode Patterning for Low-Loading and High-Efficiency Water Electrolyzers.

An anode electrode concept of thin catalyst-coated liquid/gas diffusion layers (CCLGDLs), by integrating Ir catalysts with Ti thin tunable LGDLs with facile electroplating in proton exchange membrane electrolyzer cells (PEMECs), is proposed. The CCLGDL design with only 0.08 mgIr cm-2 can achieve comparative cell performances to the conventional commercial electrode design, saving ≈97% Ir catalyst and augmenting a catalyst utilization to ≈24 times. CCLGDLs with regulated patterns enable insight into how pattern morphology impacts reaction kinetics and catalyst utilization in PEMECs. A specially designed two-sided transparent reaction-visible cell assists the in situ visualization of the PEM/electrode reaction interface for the first time. Oxygen gas is observed accumulating at the reaction interface, limiting the active area and increasing the cell impedances. It is demonstrated that mass transport in PEMECs can be modified by tuning CCLGDL patterns, thus improving the catalyst activation and utilization. The CCLGDL concept promises a future electrode design strategy with a simplified fabrication process and enhanced catalyst utilization. Furthermore, the CCLGDL concept also shows great potential in being a powerful tool for in situ reaction interface research in PEMECs and other energy conversion devices with solid polymer electrolytes.

Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network.

BACKGROUND: Fluorescence image analysis in biochemical science often involves the complex tasks of identifying samples for analysis and calculating the desired information from the intensity traces. Analyzing giant unilamellar vesicles (GUVs) is one of these tasks. Researchers need to identify many vesicles to statistically analyze the degree of molecular interaction or state of molecular organization on the membranes. This analysis is complicated, requiring a careful manual examination by researchers, so automating the analysis can significantly aid in improving its efficiency and reliability. RESULTS: We developed a convolutional neural network (CNN) assisted intelligent analysis routine based on the whole 3D z-stack images. The programs identify the vesicles with desired morphology and analyzes the data automatically. The programs can perform protein binding analysis on the membranes or state decision analysis of domain phase separation. We also show that the method can easily be applied to similar problems, such as intensity analysis of phase-separated protein droplets. CNN-based classification approach enables the identification of vesicles even from relatively complex samples. We demonstrate that the proposed artificial intelligence-assisted classification can further enhance the accuracy of the analysis close to the performance of manual examination in vesicle selection and vesicle state determination analysis. CONCLUSIONS: We developed a MATLAB based software capable of efficiently analyzing confocal fluorescence image data of giant unilamellar vesicles. The program can automatically identify GUVs with desired morphology and perform intensity-based calculation and state decision for each vesicle. We expect our method of CNN implementation can be expanded and applied to many similar problems in image data analysis.

Engineered Thin Diffusion Layers for Anion-Exchange Membrane Electrolyzer Cells with Outstanding Performance.

Anion-exchange membrane electrolyzer cells (AEMECs) are one of the most promising technologies for carbon-neutral hydrogen production. Over the past few years, the performance and durability of AEMECs have substantially improved. Herein, we report an engineered liquid/gas diffusion layer (LGDL) with tunable pore morphologies that enables the high performance of AEMECs. The comparison with a commercial titanium foam in the electrolyzer indicated that the engineered LGDL with thin-flat and straight-pore structures significantly improved the interfacial contacts, mass transport, and activation of more reaction sites, leading to outstanding performance. We obtained a current density of 2.0 A/cm2 at 1.80 V with an efficiency of up to 81.9% at 60 °C under 0.1 M NaOH-fed conditions. The as-achieved high performance in this study provides insight to design advanced LGDLs for the production of low-cost and high-efficiency AEMECs.

Optical properties of anatase and rutile TiO2 films deposited by using a pulsed laser.

Transparent titanium dioxide (TiO2) films were deposited on quartz substrates by using a pulsed-laser deposition technique. The effects of deposition temperatures on the crystalline phases and optical properties of the films were investigated. Phase-pure anatase and rutile TiO2 films were obtained at temperatures of 600°C and 800°C, respectively. The deposited TiO2 films were transparent in the visible spectral region, and the linear refractive indices of the films were determined by using the optical transmission spectra. The optical bandgaps were obtained as 3.25 and 3.02 eV for the anatase and rutile TiO2 films, respectively. The third-order nonlinear optical properties of the films were investigated by using a single-beam z-scan method at a wavelength of 532 nm. The values of the nonlinear absorption coefficient and the nonlinear refractive index of the rutile TiO2 film were determined to be -7.44×10-10m/W and -1.36×10-16m2/W, respectively, which are greater than those of the anatase TiO2 film. The metal-oxygen bond lengths based on the bond-orbital theory could be used to explain the results.

Gaussian process regression for direct laser absorption spectroscopy in complex combustion environments.

Tunable diode laser absorption spectroscopy (TDLAS) has been proved to be a powerful diagnostic tool in combustion research. However, current methods for post-processing a large number of blended spectral lines are often inadequate both in terms of processing speed and accuracy. The present study verifies the application of Gaussian process regression (GPR) on processing direct absorption spectroscopy data in combustion environments to infer gas properties directly from the absorbance spectra. Parallelly-composed generic single-output GPR models and multi-output GPR models based on linear model of coregionalization (LMC) are trained using simulated spectral data at set test matrix to determine multiple unknown thermodynamic properties simultaneously from the absorbance spectra. The results indicate that compared to typical data processing methods by line profile fitting, the GPR models are proved to be feasible for accurate inference of multiple gas properties over a wide spectral range with a manifold of blended lines. While further validation and optimization work can be done, parallelly composed single-output GPR model demonstrates sufficient accuracy and efficiency for the demand of temperature and concentration inference.

Constructing Ultrathin W-Doped NiFe Nanosheets via Facile Electrosynthesis as Bifunctional Electrocatalysts for Efficient Water Splitting.

Exploring cost-effective and efficient bifunctional electrocatalysts via simple fabrication strategies is strongly desired for practical water splitting. Herein, an easy and fast one-step electrodeposition process is developed to fabricate W-doped NiFe (NiFeW)-layered double hydroxides with ultrathin nanosheet features at room temperature and ambient pressure as bifunctional catalysts for water splitting. Notably, the NiFeW nanosheets require overpotentials of only 239 and 115 mV for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively, to reach a current density of 10 mA/cm2 in alkaline media. Their exceptional performance is further demonstrated in a full electrolyzer configuration with the NiFeW as both anode and cathode catalysts, which achieves a low cell voltage of 1.59 V at 10 mA/cm2, 110 mV lower than that of the commercial IrO2 (anode) and Pt (cathode) catalysts. Moreover, the NiFeW nanosheets are superior to various recently reported bifunctional electrocatalysts. Such remarkable performances mainly ascribe to W doping, which not only effectively modulates the electrocatalyst morphology but also engineers the electronic structure of NiFe hydroxides to boost charge-transfer kinetics for both the OER and HER. Hence, the ultrathin NiFeW nanosheets with an efficient fabrication strategy are promising as bifunctional electrodes for alkaline water electrolyzers.

Functional Modular Network Identifies the Key Genes of Preoperative Inhalation Anesthesia and Intravenous Anesthesia in Off-Pump Coronary Artery Bypass Grafting.

Off-pump coronary artery bypass grafting (OPCABG) is an effective strategy for revascularization. Preoperative anesthesia appears critical due to surgical instability and the risk of organ damage. This study, based on a functional module network, analysed the effects of preoperative inhalation anesthesia and intravenous anesthesia on OPCABG and performed a pivot analysis of its potential drug regulators. We obtained microarray data of sevoflurane anesthesia and propofol anesthesia from the GEO database and analysed the difference between the two groups of data, resulting in 5701 and 3210 differential genes to construct the expression matrix. WGCNA analysis showed that sevoflurane anesthesia clustered into 7 functional disorder modules, including PDCD6IP, WDR3, and other core genes; propofol anesthesia clustered to form two functional disorder modules, including KCNB2 and LHX2, two core genes Enrichment analysis of the functions and pathways of interest suggests that both anesthesia-related module genes tend to function as pathways associated with ion and transmembrane transport. The underlying mechanism may be that targeted regulation of transmembrane-associated biological processes and ion pathways in the core genes of each module affect the surgical process. Pivot analysis of potential drug regulators revealed 229 potential drugs for sevoflurane anesthesia surgery, among which zinc regulates three functional disorder modules via AHSG, F12, etc., and 67 potential drugs for propofol anesthesia surgery, among which are propofol, methadone, and buprenorphine, regulate two functional disorder modules through four genes, CYP2C8, OPRM1, CYP2C18, and CYP2C19. This study provides guidance on clinical use or treatment by comparing the effects of two anesthesias on surgery and its potential drugs.

Picosecond nonlinear optical properties of SrTiO3 composite films doped with gold and nickel nanoparticles.

Nanoparticles composite thin films formed by nanometer-sized gold and nickel particles embedded in SrTiO3 matrices were fabricated on MgO single-crystal substrates by co-depositing the metal and ceramic targets using the pulsed laser deposition technique. The linear optical absorption properties were measured from 350 to 800 nm, and the absorption peak due to the surface plasmon resonance of Au metal particles was observed around 557 nm. The ultra-fast third-order nonlinear optical properties of the films were determined by a single-beam z-scan method at a wavelength of 532 nm with laser duration of 55 ps. The nonlinear refractive index n2 and the nonlinear absorption coefficient ß were determined, respectively, and the figure of merit χ(3)/α (with χ(3) being the third-order nonlinear susceptibility and α the linear optical absorption coefficient) was discussed. Whether gold or nickel, metal particles have little effect on the figure of merit, but significantly affect the ratio of the real part to the imaginary part of χ(3) [Reχ(3)/Imχ(3)]. The obtained Reχ(3)/Imχ(3) of Au/SrTiO3 is 1.43, which is more than 3 times as large as that of Ni/SrTiO3.

Nonlinear refraction and saturable absorption in Au:BaTiO3 composite films.

Composite thin films Au:BaTiO3, comprising nanometer-sized gold particles embedded in barium titanate matrices, were synthesized on MgO (100) substrates with the pulsed laser deposition technique. The nanostructure of the films and the size distributions of the Au particles were analyzed by high-resolution transmission electron microscopy. Crystal lattice fringes from the Au nanocrystals and the BaTiO3 matrices were observed. The nonlinear optical properties of the Au:BaTiO3 films were measured with the z-scan method at a wavelength of 532 nm, which was closed to the surface plasmon resonance of nanoscale Au particles. The features of the closed-aperture z-scan transmittance curves were affected by the ratio, which increased greatly at a high metal concentration, of the imaginary part to the real part of the third-order nonlinear susceptibility chi(3).