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Graphene, the thinnest, strongest, and stiffest material with exceptional thermal conductivity and electron mobility, has increasingly received world‐wide attention in the past few years. These unique properties may lead to novel or improved technologies to address the pressing global challenges in many applications including transparent conducting electrodes, field effect transistors, flexible touch screen, single‐molecule gas detection, desalination, DNA sequencing, osmotic energy production, etc. To realize these applications, it is necessary to transfer graphene films from growth substrate to target substrate with large‐area, clean, and low defect surface, which are crucial to the performances of large‐area graphene devices. This critical review assesses the recent development in transferring large‐area graphene grown on Fe, Ru, Co, Ir, Ni, Pt, Au, Cu, and some nonmetal substrates by using various synthesized methods. Among them, the transfers of the most attention kinds of graphene synthesized on Cu and SiC substrates are discussed emphatically. The advances and the main challenges of each wet and dry transfer method for obtaining the transferred graphene film with large‐area, clean, and low defect surface are also reviewed. Finally, the article concludes the most promising methods and the further prospects of graphene transfer. Graphene has increasingly received world‐wide attention in the past few years. It is necessary to transfer graphene films from growth substrate to target substrate when fabricating graphene‐based devices. This critical review assesses the recent development in transferring large‐area graphene and concludes the most promising methods and the further prospects of graphene transfer.

Graphene, an atomically thin material, has unique electrical, mechanical, and optical properties that can enhance the performance of thin film-based flexible and transparent devices, including gas sensors. Graphene synthesized on a metallic catalyst must first be transferred onto a target substrate using wet or dry transfer processes; however, the graphene surface is susceptible to chemical modification and mechanical damage during the transfer. Defects on the graphene surface deteriorate its excellent intrinsic properties, thus reducing device performance. In this study, the surface properties of transferred graphene were investigated according to the transfer method (wet vs. dry) and characterized using atomic force microscopy, Raman spectroscopy, and contact angle measurements. After the wet transfer process, the surface properties of graphene exhibited tendencies similar to the poly(methyl methacrylate) residue remaining after solvent etching. The dry-transferred graphene revealed a surface closer to that of pristine graphene, regardless of substrates. These results provide insight into the utilization of wet and dry transfer processes for various graphene applications.

In this paper, the mathematical model of an inclined longitudinal porous fin of trapezoidal, rectangular, and dovetail profiles in the presence of convective and radiative environments is considered to study the heat transfer and heat distribution within the fin. The governing equation for the energy transfer in the porous fin is derived by using the Darcy model that simulates the interaction of fluids and solids. The mathematical model has been analyzed so that a common equation can be used to study the trapezoidal, rectangular, and dovetail profiles. Furthermore, to study the temperature distribution in the fin, a supervised machine learning algorithm is developed using Cascade feedforward backpropagated (CFB) neural networks and Levenberg–Marquardt (LM) algorithm. A reference solution of 1001 points for supervised learning of the design scheme is generated by using a numerical solver (RK-4), which is further utilized by the CFB-LM algorithm with the Log-Sigmoid activation function to train, validate and test the data properly. The design algorithm’s outcomes are compared to the results of the homotopy perturbation method, shooting method, and other machine learning algorithms. Extensive graphical and statistical analyses are conducted to study the influence of variations in inclination angle, tip tapering, wet porous parameter, internal heat generation, porosity, progressive natural convective parameter, and dimensionless radiative parameter on the thermal profile and heat transfer rate of the longitudinal porous fin. The dovetail fin profile achieves the maximum heat transfer rate, followed by rectangular and trapezoidal fin profiles, provided that internal heat production is kept to a minimum.

Flux towers provide essential terrestrial climate, water, and radiation budget information needed for environmental monitoring and evaluation of climate change impacts on ecosystems and society in general. They are also intended for calibration and validation of satellite-based Earth observation and monitoring efforts, such as assessment of evapotranspiration from land and vegetation surfaces using surface energy balance approaches. In this paper, 15 years of Skukuza eddy covariance data, i.e. from 2000 to 2014, were analysed for surface energy balance closure (EBC) and partitioning. The surface energy balance closure was evaluated using the ordinary least squares regression (OLS) of turbulent energy fluxes (sensible (H) and latent heat (LE)) against available energy (net radiation (Rn) less soil heat (G)), and the energy balance ratio (EBR). Partitioning of the surface energy during the wet and dry seasons was also investigated, as well as how it is affected by atmospheric vapour pressure deficit (VPD), and net radiation. After filtering years with low-quality data (2004–2008), our results show an overall mean EBR of 0.93. Seasonal variations of EBR also showed the wet season with 1.17 and spring (1.02) being closest to unity, with the dry season (0.70) having the highest imbalance. Nocturnal surface energy closure was very low at 0.26, and this was linked to low friction velocity during night-time, with results showing an increase in closure with increase in friction velocity. The energy partition analysis showed that sensible heat flux is the dominant portion of net radiation, especially between March and October, followed by latent heat flux, and lastly the soil heat flux, and during the wet season where latent heat flux dominated sensible heat flux. An increase in net radiation was characterized by an increase in both LE and H, with LE showing a higher rate of increase than H in the wet season, and the reverse happening during the dry season. An increase in VPD is correlated with a decrease in LE and increase in H during the wet season, and an increase in both fluxes during the dry season.

The primary objective of this study is to conduct a detailed investigation of the performance of a porous trapezoidal fin subjected to coupled sensible and latent heat transfer at its surface. The comprehensive literature survey reveals that, despite studies on other fin shapes, no previous research has tackled a porous trapezoidal-fin configuration. Assuming linear temperature dependence of thermal conductivity, Darcy’s law is used to describe flow within the porous fin. Employing the differential transformation method, the efficiency of a moisture-absorbing fin has been computed. To model the condensation process, the humidity ratio is approximated as a cubic polynomial function of the porous fin surface temperature, with its relationship obtained through regression-based psychrometric correlations. A comparative investigation has been conducted into the effects of parameters such as relative humidity, trapezoidal expansion ratio, and thermal conductivity coefficient on temperature variation and efficiency of porous fins with specified porosity and permeability. The numerical scheme and resulting outputs were validated through detailed comparisons with available benchmark results. A strong agreement was observed between the DTM-based solutions, the high-precision finite difference method, and existing literature results. The analysis shows that along a dry fin, the maximum temperature difference between the base and tip is approximately 1.5 °C for an expansion ratio of − 0.5 and 2.5 °C for an expansion ratio of + 0.5. When the fin surface is wet, these values increase significantly to 4.76 °C and 7.76 °C, respectively. The results indicate that variations in specific design parameters of porous trapezoidal fins, across different expansion ratios, cause notable changes in fin efficiency. The efficiency of a trapezoidal porous fin is influenced by its geometric expansion ratio, with maximum efficiency occurring at negative expansion ratios. From a comparative perspective, wet porous fins are less efficient than dry fins.

Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy metals: soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain's heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area.

In this work, we reported the simple one-step wet impregnation method of g-C 3 N 4 /MnO 2 nanocomposites aimed at improving the photocatalytic degradation efficiency of methylene blue dye. The synthesized catalysts underwent comprehensive characterization using various techniques such as X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and ultraviolet-visible diffuse reflectance spectroscopy (UV-vis DRS) to investigate their physicochemical properties. Their photocatalytic performance was evaluated by the degradation of methylene blue (MB) dye under visible light irradiation. Consequently, the MnO 2 /g-C 3 N 4 nanocomposite demonstrates superior photocatalytic degradation performance compared to both bare MnO 2 and g-C 3 N 4 . This enhancement is attributed to the improved efficiency of charge carrier separation and interfacial charge transfer within the nanocomposite structure. The degradation efficiency of MnO 2 /g-C 3 N 4 nanocomposite was found 89% of MB under visible light irradiation at 120 min. Meanwhile, the recyclability analysis demonstrated that the MnO 2 /g-C 3 N 4 nanocomposite can be recycled four times. Furthermore, the substance demonstrated positive antibacterial activity against Escherichia coli , and Staphylococcus aureus bacterial strains. These findings suggest that the MnO 2 /g-C 3 N 4 nanocomposite, with its dual roles as a photocatalyst and an antibacterial agent, has potential applications in environmental decontamination.

The Wet Flue Gas Desulfurization (WFGD) process has always been an important part in the low-carbon/green power realization process of traditional power plants. Adding a turbulator to the spray scrubber can improve the desulfurization efficiency, whereas it also increases the flow resistance. In this study, a small experiment device based on a spray scrubber with a turbulator in a power plant was built on a 1:10 scale to address the problem. The influence of the flue gas flow rate and the liquid–gas ratio on the flow resistance was investigated. A mathematical model was established for the two-phase flow and the SO2 liquid phase absorption reaction, and numerical simulations were achieved by the Fluent code. The resistance characteristics and the liquid droplet residence time were studied in detail. By fitting the experimental data, the relationship between the resistance coefficient, the Reynolds number, and the liquid–gas ratio in the tower was determined as the following: f = 0.0288 Re0.359(L/G)0.754. The desulfurization efficiency was calculated by adopting a user-defined function (UDF) code in a computational fluid dynamics (CFD) model, and the effects of the flue gas flow rate, temperature, and the liquid–gas ratio were analyzed. The results show that the effect of the rod-shaped turbulator on the flow resistance is much less than the effect of the liquid spray. The residence time of droplets around the turbulator is doubled. The pressure loss in the scrubber increases with the liquid–gas ratio (associated with the number of spray layer) and the flue gas flow rate. The turbulator can improve the uniformity of the flue gas velocity to some extent and increase the utilization rate of the spraying liquid, thereby increasing the desulfurization efficiency by 2.49%. Considering the operation cost, the reasonable value range of the liquid–gas ratio is 20~30. This work presents a good demonstration of combining the experiment and numerical simulations on a laboratory scale for large systems and associated components research, which is helpful for the engineering design and optimization of modern green power systems.

Thermal conductivity of rock is one of the important parameters to understand the heat conduction process in interior of the earth. The study of the effect on the thermal conductivity of clay-bearing sandstone subjected to drying–wetting process is of great significance to many geological and geotechnical engineering issues. In this study, drying–wetting cycle experiments on clay-bearing sandstone were carried out, including three times of drying and two times of water saturation treatment. The thermal conductivity of clay-bearing sandstone after each treatment was measured by transient hot wire method, and the thermal inhomogeneity was analyzed. The results indicate that the drying–wetting process leads to the decrease of the average thermal conductivity of clay-bearing sandstone, while the increase of thermal heterogeneity factor. Base on the results of 3D scanning and SEM, it is found that the development of pores and microcracks during the drying–wetting process is the main reason for the average thermal conductivity decreased and the thermal inhomogeneity increased. Further analysis shows that the interaction between clay minerals and water leads to the destruction of rock matrix structure, resulting in the increase of primary pores, the formation of new pores and secondary microcracks in clay-bearing sandstone. In addition, the numerical results show that the pore leads to the significant weakening of rock heat transfer effect, and the temperature field tends to be heterogeneous distribution. The research results can provide reference for the evaluation of thermal conductivity of rock mass in deep engineering.

The research and application of convolutional neural networks (CNNs) on statistical downscaling have been hampered by the fact that deep learning is highly dependent on sample size and is considered to be a black-box model. Therefore, a CNN model with transfer learning (CNN-TL) is proposed to study the pre-rainy season precipitation of South China. First, an augmented monthly dataset is created by sliding a fixed-length window over the daily circulation field and precipitation data for the entire year. Next, a base CNN network is pretrained on the augmented dataset, and then the network parameters are tuned on the actual monthly dataset from South China. Finally, guided backpropagation is conducted to obtain the distribution regions of the key features and explain the net. The coefficient of determination ( R 2 ) and root mean square error ( RMSE ) show that the CNN-TL model has higher explanatory power and better fitting performance than the feature extraction-based random forest (RF). Compared with the base CNN, the transfer learning approach can improve the explanatory power of the model by 10.29% and reduce the average RMSE by 6.82%. In addition, the interpretation results of the model show that the critical regions are primarily South China and its surrounding areas, including the Indochinese Peninsula, the Bay of Bengal, and the South China Sea. Furthermore, the ablation experiments and composite analysis illustrate that these regions are very important.