Growth from Below: Bilayer Graphene on Copper by Chemical Vapor Deposition

We evaluate how a second graphene layer forms and grows on Cu foils during chemical vapor deposition (CVD). Low-energy electron diffraction and microscopy is used to reveal that the second layer nucleates and grows next to the substrate, i.e., under a graphene layer. This underlayer mechanism can facilitate the synthesis of uniform single-layer films but presents challenges for growing uniform bilayer films by CVD. We also show that the buried and overlying layers have the same edge termination.Comment: Revised after review. Accepted for publication in New Journal of Physic

Spatiotemporal Dynamics of Land Cover and Their Driving Forces in the Yellow River Basin since 1990

The national strategy for ecological protection and high-quality development is raising the ecological security protection to an unprecedented level in the Yellow River Basin (YRB) of China. Due to the explicitly analyzed land cover changes under climate change and rapid urbanization in the YRB area since 1990, land cover dynamic degree index, transfer matrix, and geo-detector method were used to explicate land cover changes and their key driving factors, based on the spatial data of land cover from 1990 to 2020. The results show that grasslands, croplands, and forests are the main land cover types, accounting for 48.37%, 25.05%, and 13.50%, respectively, of the total area in the YRB area. Grassland, cropland, and cropland are the major land cover type, accounting for 61.49%, 37.13%, and 66.33%, respectively, in the upstream, midstream, and downstream of the YRB area. Built-up land has showed a continual increasing trend, and its dynamic degree was up to 3.38% between 2010 and 2020. Population density was a key factor for land cover change, with an average contribution rate of 0.264; then, elevation and temperature also expressed an important role to drive the land cover change in the YRB area during the period from 1990 to 2020

Historical Changes of Black Carbon in Snow and Its Radiative Forcing in CMIP6 Models

Black carbon in snow (BCS) has a significant impact on global climate and is an important component of Earth system modeling. Here, we provide a comprehensive evaluation of BCS simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and its radiative forcing on a global scale. Overall, the multi-model mean generally captures the characteristics of BCS spatial patterns, with maximum concentrations in East Asia and the Tibetan Plateau (~120 ng·g−1), and the lowest in Antarctica (~0.05 ng·g−1). The BCS concentrations in all CMIP6 multi-model mean and individual models generally exhibit a temporally increasing trend globally, with particularly large increases after the 1940s. In terms of seasonal cycles, individual models are generally consistent in most regions. Globally, BCS concentrations are highest around January and lowest in September. The albedo reduction in the Tibetan Plateau and East Asia simulated by the CMIP6 multi-model mean reached ~0.06 in 2014 and may influence climate more than expected

Divergent features of the upper-tropospheric carbonaceous aerosol layer: effects of atmospheric dynamics and pollution emissions in Asia, South America, and Africa

The upper-tropospheric carbonaceous aerosol layer (TCAL) represents the increase of aerosols in the upper-troposphere. It was first discovered over Asia but was found in this study to also occur over South America and Africa. The TCALs over three regions typically exist during the strong deep convection season, with the Asian, South American, and African TCALs showing peak intensity during July–August, October–December, and November–December, respectively. Over Asia, the TCAL has the highest altitude and widest spread due to strongest deep convection and upper-troposphere anticyclonic system. TCAL intensity is highest in South America maybe due to heaviest pollutant emissions. Anthropogenic pollution from India and western China produces two Asian TCAL centers, whereas widespread wildfires result in single centers over South America and Africa. TCAL radiative effect at the top of the atmosphere has warming effects over Asia (+0.23 W m ^−2 ), whereas cooling effects perform over South America (−0.54 W m ^−2 ) and Africa (−0.20 W m ^−2 ) owing to its altitude and the divergent strengths of black-carbon absorption and organic-carbon scattering

Historical Changes of Black Carbon in Snow and Its Radiative Forcing in CMIP6 Models

Black carbon in snow (BCS) has a significant impact on global climate and is an important component of Earth system modeling. Here, we provide a comprehensive evaluation of BCS simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and its radiative forcing on a global scale. Overall, the multi-model mean generally captures the characteristics of BCS spatial patterns, with maximum concentrations in East Asia and the Tibetan Plateau (~120 ng·g−1), and the lowest in Antarctica (~0.05 ng·g−1). The BCS concentrations in all CMIP6 multi-model mean and individual models generally exhibit a temporally increasing trend globally, with particularly large increases after the 1940s. In terms of seasonal cycles, individual models are generally consistent in most regions. Globally, BCS concentrations are highest around January and lowest in September. The albedo reduction in the Tibetan Plateau and East Asia simulated by the CMIP6 multi-model mean reached ~0.06 in 2014 and may influence climate more than expected

Bioinspired artificial visual system based on 2D WSe₂ synapse array

Machine vision systems that capture images for visual inspection and recognition tasks must be able to perceive, memorize, and compute any color scene. To achieve this, most of the current visual systems use circuits and algorithms which may reduce efficiency and increase complexity. Herein, a 2D semiconductor tungsten diselenide (WSe2)-based phototransistor that successfully demonstrates an artificial vision system integrating the processing capability of visual information sensing memory, is reported. Furthermore, based on a 6 × 6 fabricated retinal perception array, artificial visual information sensing memory and processing system are proposed to perform image recognition tasks, which can avoid the time delay and energy consumption caused by data conversion and movement. On the other hand, highly linear symmetric synaptic plasticity can be achieved based on the modulation of carrier types in WSe2 transistors with different thicknesses, facilitating the high level of training and inference accuracy for artificial neural networks. Last, through training and inference simulations, the feasibility of the hybrid synapses for optical neural networks (ONN) is demonstrated.This research was supported by the NSFC Program (grant nos. 62122055, 62074104, 62104154, 61974093, and 62001307), the Guangdong Basic and Applied Basic Research Foundation (Grant Nos. 2023A1515012479 and 2021A1515012569), the Science and Technology Innovation Commission of Shenzhen (Grant Nos. RCYX20200714114524157, JCYJ20220818100206013, and 20210324095207020), and the NTUT-SZU Joint Research Program

The Impact of Alternative Fuels on Ship Engine Emissions and Aftertreatment Systems: A Review

Marine engines often use diesel as an alternative fuel to improve the economy. In recent years, waste oil, biodiesel and alcohol fuel are the most famous research directions among the alternative fuels for diesel. With the rapid development of the shipping industry, the air of coastal areas is becoming increasingly polluted. It is now necessary to reduce the emission of marine engines to meet the strict emission regulations. There are many types of alternative fuels for diesel oil and the difference of the fuel may interfere with the engine emissions; however, PM, HC, CO and other emissions will have a negative impact on SCR catalyst. This paper reviews the alternative fuels such as alcohols, waste oils, biodiesel made from vegetable oil and animal oil, and then summarizes and analyzes the influence of different alternative fuels on engine emissions and pollutant formation mechanism. In addition, this paper also summarizes the methods that can effectively reduce the emissions of marine engines; it can provide a reference for the study of diesel alternative fuel and the reduction of marine engine emissions

Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?

<jats:p>Abstract. The extensive snow cover across the Tibetan Plateau (TP) has a major influence on the climate and water supply for over 1 billion downstream inhabitants. However, an adequate evaluation of variability in the snow cover fraction (SCF) over the TP simulated by multiple reanalysis datasets has yet to be undertaken. In this study, we used the Snow Property Inversion from Remote Sensing (SPIReS) SCF dataset for the water years (WYs) 2001–2017 to evaluate the capabilities of eight reanalysis datasets (HMASR, MERRA2, ERA5, ERA5L, JRA55, CFSR, CRAL, and GLDAS) in simulating the spatial and temporal variability in SCF in the TP. CFSR, GLDAS, CRAL, and HMASR are good in simulating the spatial pattern of climatological SCF, with lower bias and higher correlation and Taylor skill score (SS). By contrast, ERA5L, JRA55, and ERA5 have a relatively good performance in terms of SCF annual trends among eight reanalysis datasets. The biases in SCF simulations across reanalysis datasets are influenced by a combination of meteorological forcings, including snowfall and temperature, as well as by the SCF parameterization methods. However, the primary influencing factors vary among the reanalysis datasets. Additionally, averaging multiple reanalysis datasets can enhance the spatiotemporal accuracy of SCF simulations, but this enhancement effect does not consistently increase with the number of reanalysis datasets used. </jats:p&gt

The Spatio-Temporal Variability in the Radiative Forcing of Light-Absorbing Particles in Snow of 2003–2018 over the Northern Hemisphere from MODIS

Light-absorbing particles (LAPs) deposited on snow can significantly reduce surface albedo and contribute to positive radiative forcing. This study firstly estimated and attributed the spatio-temporal variability in the radiative forcing (RF) of LAPs in snow over the northern hemisphere during the snow-covered period 2003–2018 by employing Moderate Resolution Imaging Spectroradiometer (MODIS) data, coupled with snow and atmospheric radiative transfer modelling. In general, the RF for the northern hemisphere shows a large spatial variability over the whole snow-covered areas and periods, with the highest value (12.7 W m−2) in northeastern China (NEC) and the lowest (1.9 W m−2) in Greenland (GRL). The concentration of LAPs in snow is the dominant contributor to spatial variability in RF in spring (~73%) while the joint spatial contributions of snow water equivalent (SWE) and solar irradiance (SI) are the most important (>50%) in winter. The average northern hemisphere RF gradually increases from 2.1 W m−2 in December to 4.1 W m−2 in May and the high-value area shifts gradually northwards from mid-altitude to high-latitude over the same period, which is primarily due to the seasonal variability of SI (~58%). More interestingly, our data reveal a significant decrease in RF over high-latitude Eurasia (HEUA) of −0.04 W m−2 a−1 and northeastern China (NEC) of −0.14 W m−2 a−1 from 2003 to 2018. By employing a sensitivity test, we find the concurrent decline in the concentration of LAPs in snow accounted for the primary responsibility for the decrease in RF over these two areas, which is further confirmed by in situ observations