An Experimental- and Simulation-Based Evaluation on the CO_2 Utilization Efficiency in Aqueous-based Electrochemical CO_2 Reduction Reactors with Ion-Selective Membranes

The CO_2 utilization efficiency of three types of electrochemical CO2 reduction (CO_2R) reactors using different ion-selective membranes, including anion exchange membrane (AEM), cation exchange membrane (CEM), and bipolar membrane (BPM), was studied quantitively via both experimental and simulation methods. The operating current density of the CO_2R reactors was chosen to be between 10 – 50 mA cm^(-2) to be relevant for solar-fuel devices with relatively low photon flux from sunlight. In the AEM based CO_2R reactor with a 6-electron per carbon CO_2R at the cathode surface, an upper limit of 14.4% for the CO_2 utilization efficiency was revealed by modeling and validated by experimental measurements in CO_2 saturated aqueous electrolytes without any buffer electrolyte. Improvements in CO_2 utilization efficiency were observed when additional buffer electrolyte was added into the aqueous solution, especially in solutions with low bicarbonate concentrations. The effects of the feed rate of the input CO_2 stream, the Faradaic Efficiency (FE) and the participating electron numbers of the cathode reaction on the CO_2 utilization efficiency was also studied in the AEM based CO_2R reactor. The CEM based CO_2R reactor exhibited low CO_2 utilization efficiency with re-circulation between the catholyte and the anolyte, and was unsustainable due to the cation depletion from the anolyte without any re-circulation. The BPM based CO_2R reactor operated continuously without a significant increase in the cell voltage and exhibited significantly higher CO_2 utilization efficiency, up to 61.4%, as compared to the AEM based CO_2R reactors. Diffusive CO_2 loss across the BPM resulted in relatively low CO_2 utilization efficiency at low operating current densities. Modeling and simulation also provided target BPM properties for higher CO_2 utilization efficiency and efficient cell operation

Patterned nanofiber air filters with high optical transparency, robust mechanical strength, and effective PM_(2.5) capture capability

PM_(2.5), due to its small particle size, strong activity, ease of the attachment of toxic substances and long residence time in the atmosphere, has a great impact on human health and daily production. In this work, we have presented patterned nanofiber air filters with high optical transparency, robust mechanical strength and effective PM_(2.5) capture capability. Here, to fabricate a transparency air filter by a facile electrospinning method, we chose three kinds of patterned wire meshes with micro-structures as negative receiver substrates and directly electrospun polymer fibers onto the supporting meshes. Compared with randomly oriented nanofibers (named “RO NFs” in this paper) and commercially available facemasks, the patterned air filters showed great mechanical properties, and the water contact angles on their surfaces were about 122–143° (the water contact angle for RO NFs was 81°). In addition, the patterned nanofibers exhibited high porosity (>80%), and their mean pore size was about 0.5838–0.8686 μm (the mean pore size of RO NFs was 0.4374 μm). The results indicate that the transparent patterned air filters have the best PM_(2.5) filtration efficiency of 99.99% at a high transmittance of ∼69% under simulated haze pollution

Patterned nanofiber air filters with high optical transparency, robust mechanical strength, and effective PM_(2.5) capture capability

PM_(2.5), due to its small particle size, strong activity, ease of the attachment of toxic substances and long residence time in the atmosphere, has a great impact on human health and daily production. In this work, we have presented patterned nanofiber air filters with high optical transparency, robust mechanical strength and effective PM_(2.5) capture capability. Here, to fabricate a transparency air filter by a facile electrospinning method, we chose three kinds of patterned wire meshes with micro-structures as negative receiver substrates and directly electrospun polymer fibers onto the supporting meshes. Compared with randomly oriented nanofibers (named “RO NFs” in this paper) and commercially available facemasks, the patterned air filters showed great mechanical properties, and the water contact angles on their surfaces were about 122–143° (the water contact angle for RO NFs was 81°). In addition, the patterned nanofibers exhibited high porosity (>80%), and their mean pore size was about 0.5838–0.8686 μm (the mean pore size of RO NFs was 0.4374 μm). The results indicate that the transparent patterned air filters have the best PM_(2.5) filtration efficiency of 99.99% at a high transmittance of ∼69% under simulated haze pollution

Reliable Performance Characterization of Mediated Photocatalytic Water-Splitting Half Reactions

Photocatalytic approaches using two sets of semiconductor particles and a pair of redox shuttle mediators are considered as a safe and economic solution for solar water splitting. Here, we report on accurate experimental characterization techniques for photocatalytic half reactions investigating the gas as well as the liquid products. The method is exemplified utilizing photocatalytic titania particles in an iron-based aqueous electrolyte for effective oxygen evolution and mediator reduction reactions under illumination. Several product characterization methods, including an optical oxygen sensor, pressure sensor, gas chromatography, and UV-Vis spectroscopy are used and compared for accurate, high-resolution gas-products and mediator conversion measurements. Advantages of each technique are discussed. A high Faraday efficiency of 97.5%±2% is calculated and the reaction rate limits are investigated

Protein kinase substrate identification on functional protein arrays

<p>Abstract</p> <p>Background</p> <p>Over the last decade, kinases have emerged as attractive therapeutic targets for a number of different diseases, and numerous high throughput screening efforts in the pharmaceutical community are directed towards discovery of compounds that regulate kinase function. The emerging utility of systems biology approaches has necessitated the development of multiplex tools suitable for proteomic-scale experiments to replace lower throughput technologies such as mass spectroscopy for the study of protein phosphorylation. Recently, a new approach for identifying substrates of protein kinases has applied the miniaturized format of functional protein arrays to characterize phosphorylation for thousands of candidate protein substrates in a single experiment. This method involves the addition of protein kinases in solution to arrays of immobilized proteins to identify substrates using highly sensitive radioactive detection and hit identification algorithms.</p> <p>Results</p> <p>To date, the factors required for optimal performance of protein array-based kinase substrate identification have not been described. In the current study, we have carried out a detailed characterization of the protein array-based method for kinase substrate identification, including an examination of the effects of time, buffer compositions, and protein concentration on the results. The protein array approach was compared to standard solution-based assays for assessing substrate phosphorylation, and a correlation of greater than 80% was observed. The results presented here demonstrate how novel substrates for protein kinases can be quickly identified from arrays containing thousands of human proteins to provide new clues to protein kinase function. In addition, a pooling-deconvolution strategy was developed and applied that enhances characterization of specific kinase-substrate relationships and decreases reagent consumption.</p> <p>Conclusion</p> <p>Functional protein microarrays are an important new tool that enables multiplex analysis of protein phosphorylation, and thus can be utilized to identify novel kinase substrates. Integrating this technology with a systems biology approach to cell signalling will help uncover new layers in our understanding of this essential class of enzymes.</p

A direct coupled electrochemical system for capture and conversion of CO₂ from oceanwater

Capture and conversion of CO₂ from oceanwater can lead to net-negative emissions and can provide carbon source for synthetic fuels and chemical feedstocks at the gigaton per year scale. Here, we report a direct coupled, proof-of-concept electrochemical system that uses a bipolar membrane electrodialysis (BPMED) cell and a vapor-fed CO₂ reduction (CO₂R) cell to capture and convert CO₂ from oceanwater. The BPMED cell replaces the commonly used water-splitting reaction with one-electron, reversible redox couples at the electrodes and demonstrates the ability to capture CO₂ at an electrochemical energy consumption of 155.4 kJ mol⁻¹ or 0.98 kWh kg⁻¹ of CO₂ and a CO₂ capture efficiency of 71%. The direct coupled, vapor-fed CO₂R cell yields a total Faradaic efficiency of up to 95% for electrochemical CO₂ reduction to CO. The proof-of-concept system provides a unique technological pathway for CO₂ capture and conversion from oceanwater with only electrochemical processes

A Hybrid Catalyst-Bonded Membrane Device for Electrochemical Carbon Monoxide Reduction at Different Relative Humidities

A hybrid catalyst-bonded membrane device using gaseous reactants for a carbon monoxide reduction (COR) reaction in the cathode chamber, an aqueous electrolyte for an oxygen evolution reaction (OER) in the anode chamber, and an anion exchange membrane (AEM) for product separation was modeled, constructed, and tested. The Cu electrocatalyst was electrodeposited onto gas diffusion layers (GDLs) and was directly bonded to AEM by mechanical pressing in the hybrid device. The impacts of relative humidity at the cathode inlet on the selectivity and activity of COR were investigated by computational modeling and experimental methods. At a relative humidity of 30%, the Cu-based catalyst in the hybrid device exhibited a total operating current density of 87 mA cm⁻² with a −2.0 V vs Ag/AgCl reference electrode, a Faradaic efficiency (FE) for C₂H₄ generation of 32.6%, and an FE for a liquid-based carbon product of 42.6%. Significant improvements in the partial current densities for COR were observed in relation to planar electrodes or flooded gas diffusion electrodes (GDEs). In addition, a custom test bed was constructed to characterize the oxidation states of the Cu catalysts in real time along with product analysis though the backside of the GDLs via operando X-ray absorption (XAS) measurements

A direct coupled electrochemical system for capture and conversion of CO₂ from oceanwater

Capture and conversion of CO₂ from oceanwater can lead to net-negative emissions and can provide carbon source for synthetic fuels and chemical feedstocks at the gigaton per year scale. Here, we report a direct coupled, proof-of-concept electrochemical system that uses a bipolar membrane electrodialysis (BPMED) cell and a vapor-fed CO₂ reduction (CO₂R) cell to capture and convert CO₂ from oceanwater. The BPMED cell replaces the commonly used water-splitting reaction with one-electron, reversible redox couples at the electrodes and demonstrates the ability to capture CO₂ at an electrochemical energy consumption of 155.4 kJ mol⁻¹ or 0.98 kWh kg⁻¹ of CO₂ and a CO₂ capture efficiency of 71%. The direct coupled, vapor-fed CO₂R cell yields a total Faradaic efficiency of up to 95% for electrochemical CO₂ reduction to CO. The proof-of-concept system provides a unique technological pathway for CO₂ capture and conversion from oceanwater with only electrochemical processes

A Hybrid Catalyst-Bonded Membrane Device for Electrochemical Carbon Monoxide Reduction at Different Relative Humidities

A hybrid catalyst-bonded membrane device using gaseous reactants for a carbon monoxide reduction (COR) reaction in the cathode chamber, an aqueous electrolyte for an oxygen evolution reaction (OER) in the anode chamber, and an anion exchange membrane (AEM) for product separation was modeled, constructed, and tested. The Cu electrocatalyst was electrodeposited onto gas diffusion layers (GDLs) and was directly bonded to AEM by mechanical pressing in the hybrid device. The impacts of relative humidity at the cathode inlet on the selectivity and activity of COR were investigated by computational modeling and experimental methods. At a relative humidity of 30%, the Cu-based catalyst in the hybrid device exhibited a total operating current density of 87 mA cm⁻² with a −2.0 V vs Ag/AgCl reference electrode, a Faradaic efficiency (FE) for C₂H₄ generation of 32.6%, and an FE for a liquid-based carbon product of 42.6%. Significant improvements in the partial current densities for COR were observed in relation to planar electrodes or flooded gas diffusion electrodes (GDEs). In addition, a custom test bed was constructed to characterize the oxidation states of the Cu catalysts in real time along with product analysis though the backside of the GDLs via operando X-ray absorption (XAS) measurements

A deep learning–based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols

Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outcomes has also drawn much attention recently. One fundamental issue arises in how to derive model parameters reliably from image data of varying quality. This issue is even more challenging for advanced diffusion methods such as diffusion kurtosis imaging (DKI). Recently, deep learning–based methods have been demonstrated with their potential for robust and efficient computation of diffusion-derived measures. Inspired by these approaches, the current study specifically designed a framework based on a three-dimensional hierarchical convolutional neural network, to jointly reconstruct and harmonize DKI measures from multicenter acquisition to reformulate these to a state-of-the-art hardware using data from traveling subjects. The results from the harmonized data acquired with different protocols show that: 1) the inter-scanner variation of DKI measures within white matter was reduced by 51.5% in mean kurtosis, 65.9% in axial kurtosis, 53.7% in radial kurtosis, and 61.5% in kurtosis fractional anisotropy, respectively; 2) data reliability of each single scanner was enhanced and brought to the level of the reference scanner; and 3) the harmonization network was able to reconstruct reliable DKI values from high data variability. Overall the results demonstrate the feasibility of the proposed deep learning–based method for DKI harmonization and help to simplify the protocol setup procedure for multicenter scanners with different hardware and software configurations