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

Efficient three-dimensional high-resolution simulations of flow fields around cylinders

Few works use the fully three-dimensional computational fluid dynamic method to simulate the flow fields around the marine pipes with large aspect ratios due to the huge computation cost. In the present work, an operator-splitting method is used to efficiently solve the three-dimensional Reynolds Average Navier–Stokes governing equations of the fluid flow around pipes by separating the problem as a combination of a two-dimensional problem in the horizontal plane and an one-dimensional problem in the vertical direction. A second order total variation diminishing finite volume method is used to solve the model. The precision of the present model is validated by comparing the present numerical results of two typical three-dimensional cases with the available experimental and numerical results. The simulation results with a commercial software are also included in the comparison and the present model shows a higher performance in terms of computational time. Keywords: CFD, Cylinder, Cube, Operator-splitting, TVD, Three-dimensiona

ResSANet: Learning Geometric Information for Point Cloud Processing

Point clouds with rich local geometric information have potentially huge implications in several applications, especially in areas of robotic manipulation and autonomous driving. However, most point cloud processing methods cannot extract enough geometric features from a raw point cloud, which restricts the performance of their downstream tasks such as point cloud classification, shape retrieval and part segmentation. In this paper, the authors propose a new method where a convolution based on geometric primitives is adopted to accurately represent the elusive shape in the form of a point cloud to fully extract hidden geometric features. The key idea of the proposed approach is building a brand-new convolution net named ResSANet on the basis of geometric primitives to learn hierarchical geometry information. Two different modules are devised in our network, Res-SA and Res­SA­2, to achieve feature fusion at different levels in ResSANet. This work achieves classification accuracy up to 93.2% on the ModelNet40 dataset and the shape retrieval with an effect of 87.4%. The part segmentation experiment also achieves an accuracy of 83.3% (class mIoU) and 85.3% (instance mIoU) on ShapeNet dataset. It is worth mentioning that the number of parameters in this work is just 1.04 M while the network depth is minimal. Experimental results and comparisons with state-of-the-art methods demonstrate that our approach can achieve superior performance

Multiscale analysis of fine slag from pulverized coal gasification in entrained-flow bed

Abstract Fine slag (FS) is an unavoidable by-product of coal gasification. FS, which is a simple heap of solid waste left in the open air, easily causes environmental pollution and has a low resource utilization rate, thereby restricting the development of energy-saving coal gasification technologies. The multiscale analysis of FS performed in this study indicates typical grain size distribution, composition, crystalline structure, and chemical bonding characteristics. The FS primarily contained inorganic and carbon components (dry bases) and exhibited a "three-peak distribution" of the grain size and regular spheroidal as well as irregular shapes. The irregular particles were mainly adsorbed onto the structure and had a dense distribution and multiple pores and folds. The carbon constituents were primarily amorphous in structure, with a certain degree of order and active sites. C 1s XPS spectrum indicated the presence of C–C and C–H bonds and numerous aromatic structures. The inorganic components, constituting 90% of the total sample, were primarily silicon, aluminum, iron, and calcium. The inorganic components contained Si–O-Si, Si–O–Al, Si–O, SO4 2−, and Fe–O bonds. Fe 2p XPS spectrum could be deconvoluted into Fe 2p 1/2 and Fe 2p 3/2 peaks and satellite peaks, while Fe existed mainly in the form of Fe(III). The findings of this study will be beneficial in resource utilization and formation mechanism of fine slag in future

Developments in integrating nucleic acid isothermal amplification and detection systems for point-of-care diagnostics.

Early disease detection through point-of-care (POC) testing is vital for quickly treating patients and preventing the spread of harmful pathogens. Disease diagnosis is generally accomplished using quantitative polymerase chain reaction (qPCR) to amplify nucleic acids in patient samples, permitting detection even at low target concentrations. However, qPCR requires expensive equipment, trained personnel, and significant time. These resources are not available in POC settings, driving researchers to instead utilize isothermal amplification, conducted at a single temperature, as an alternative. Common isothermal amplification methods include loop-mediated isothermal amplification, recombinase polymerase amplification, rolling circle amplification, nucleic acid sequence-based amplification, and helicase-dependent amplification. There has been a growing interest in combining such amplification methods with POC detection methods to enable the development of diagnostic tests that are well suited for resource-limited settings as well as developed countries performing mass screenings. Exciting developments have been made in the integration of these two research areas due to the significant impact that such approaches can have on healthcare. This review will primarily focus on advances made by North American research groups between 2015 and June 2020, and will emphasize integrated approaches that reduce user steps, reliance on expensive equipment, and the system's time-to-result

Super-gain nanostructure with self-assembled well-wire complex energy-band engineering for high performance of tunable laser diodes

Although traditional quantum-confined nanostructures e.g. regular quantum wells or quantum dots have achieved huge success in the field of semiconductor lasers for past decades, these traditional nanostructures are encountering the difficulty of enhancing device performance to a higher level due to their inherent gain bottleneck. In this paper, we are proposing a new super-gain nanostructure based on self-assembled well-wire complex energy-band engineering with InGaAs-based materials to break through the existing bottleneck. The nanostructure is constructed by utilizing the special strain-driven indium (In)-segregation and the growth orientation-dependent on-GaAs multi-atomic step effects to achieve the distinguished ultra-wide and uniform super-gain spectra. The structural details and its luminescence mechanism are investigated by multiple measurement means and theoretical modeling. The polarized gain spectra with the max fluctuation of <3 cm−1 in 904 nm–998 nm for transverse electric (TE) mode and 904 nm–977 nm for transverse magnetic (TM) mode are simultaneously obtained with this nanostructure. It enables an ultra-low output power fluctuation of <0.7 dB and a nearly-constant threshold power throughout an ultra-wide wavelength range under a fixed injection level. It was difficult to realize these in the past. Therefore, the described super-gain nanostructure brings a brand-new chance of developing high performance of tunable laser diodes

The Combined Effects of Hourly Multi-Pollutant on the Risk of Ambulance Emergency Calls: A Seven-Year Time Series Study

Background: Ambulance emergency calls (AECs) are seen as a more suitable metric for syndromic surveillance due to their heightened sensitivity in reflecting the health impacts of air pollutants. Limited evidence has emphasized the combined effect of hourly air pollutants on AECs. This study aims to investigate the combined effects of multipollutants (i.e., PM2.5, PM10, Ozone, NO2, and SO2) on all-cause and cause-specific AECs by using the quantile g-computation method. Methods: We used ambulance emergency dispatch data, air pollutant data, and meteorological data from between 1 January 2013 and 31 December 2019 in Shenzhen, China, to estimate the associations of hourly multipollutants with AECs. We followed a two-stage analytic protocol, including the distributed lag nonlinear model, to examine the predominant lag for each air pollutant, as well as the quantile g-computation model to determine the associations of air pollutant mixtures with all-cause and cause-specific AECs. Results: A total of 3,022,164 patients were identified during the study period in Shenzhen. We found that each interquartile range increment in the concentrations of PM2.5, PM10, Ozone, NO2, and SO2 in 0–8 h, 0–8 h, 0–48 h, 0–28 h, and 0–24 h was associated with the highest risk of AECs. Each interquartile range increase in the mixture of air pollutants was significantly associated with a 1.67% (95% CI, 0.12–3.12%) increase in the risk of all-cause AECs, a 1.81% (95% CI, 0.25–3.39%) increase in the risk of vascular AECs, a 1.77% (95% CI, 0.44–3.11%) increase in reproductive AECs, and a 2.12% (95% CI, 0.56–3.71%) increase in AECs due to injuries. Conclusions: We found combined effects of pollutant mixtures associated with an increased risk of AECs across various causes. These findings highlight the importance of targeted policies and interventions to reduce air pollution, particularly for PM, Ozone, and NO2 emissions

The Association of Formula Protein Content and Growth in Early Infancy: A Systematic Review and Meta-Analysis

This systematic review aimed to examine differences in growth outcomes between breastfed infants and infants fed with formula with different protein/energy ratios during the first six months of life. We conducted a systematic review in the PubMed, Web of Science, and Springer databases. Twenty clinical trials qualified for inclusion. We extracted data about the growth outcomes of infants who were exclusive breastfed or exclusively infant formula fed in the first six months and used a meta-analysis to pool the finding data. We categorized study formulas into four groups according to their protein content: 2.2 g/100 kcal. In the first month of life, growth was not different between formula- and breastfed infants. During 2–3 months of life, growth was faster in infants who consumed formulas with protein contents higher than 2.0 g/100 kcal. After 3 months, formula-fed infants grew faster than breastfed infants. Our meta-analysis indicated that the growth outcomes of infants fed with infant formula with a relatively low protein/energy ratios, compared with that a relatively high protein/energy ratio, were close to those of breastfed infants