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Graphene has attracted much attention and holds great promise in various applications due to its extraordinary properties. To realize applications of graphene in large scale, developing a facile, green and cost-effective method for mass production of high-quality graphene is highly desired. Relative to expensive and complicated bottom-up approaches, top-down methods for graphene production are promising owing to their low cost and simplicity. Specifically, exfoliation of graphene nanosheets in liquid phase is favorable for their dispersion, functionalization and processing. Instead of highly toxic organic solvents, using water as the liquid medium makes exfoliation process eco-friendly and sustainable. In this review, recent progress on exfoliation of graphene nanosheets in water is discussed, with a particular focus on exfoliation and stabilizing mechanism in various aqueous media. Different water-based exfoliation methods, such as liquid-phase exfoliation and electrochemical exfoliation, are surveyed. [ABSTRACT FROM AUTHOR]

The test chip including transistors and Static Random Access Memory (SRAM) is proposed and fabricated by an advanced 22 nm Ultra-Thin Body and Buried Oxide Fully Depleted Silicon-on-Insulator (UTBB FD-SOI) technology. Experimental results of Co-60 irradiation and heavy ion experiments are presented. Total Ionizing Dose (TID) experiment results show that the threshold voltage shift of the transistor is about 100 mV when the dose is 500 krad(Si) and the SRAM irradiated to a dose of at least 300 krad(Si) can maintain its functionality, which means that 22 nm UTBB FD-SOI devices exhibit significantly improved total dose tolerance performance compared to the previous generation process. TID-induced threshold voltage shift in conventional-well nMOSFET can be mitigated by applying biasing to back-gate, while back-gate biasing is invalid for TID mitigation of the conventional-well pMOSFET. Heavy ion experimental results make known that 22 nm UTBB FD-SOI technology has contributions to Single Event Effect (SEE) hardness and the hardened SRAM has higher single event upset (SEU) tolerance and even SEU immunity. The research indicates that the circuits manufactured by 22 nm UTBB FD-SOI technology have great potential space applications in harsh radiation environments. • Back-gate biasing is valid for TID mitigation in RBB NMOS, which is invalid in RBB PMOS. • The 22 nm SRAM irradiated to a dose of 300 krad(Si) can maintain its functionality. • No error was recorded in DICE SRAM under all heavy ion experimental conditions. • The 22 nm UTBB FD-SOI has great potential applications in radiation environments. [ABSTRACT FROM AUTHOR]

A new ratiometric fluorescent probe ( RPM ) was developed based on through-bond energy transfer (TBET) mechanism for detecting hypochlorous acid. The probe was constructed from imidazo[1,5- a ]pyridine as the energy donor and the rhodamine as the energy acceptor. The probe could be used to determine the concentration of HOCl with high selectivity and sensitivity (the detection limit = 2.08 × 10 −6 M). In addition, RPM had a good time resolution for measuring HOCl with the response time of less than 30 s. Upon addition of HOCl, the fluorescence colors of probe RPM solution changed from green to red. Moreover, the probe has been successfully utilized for sensing HOCl in living cells. [ABSTRACT FROM AUTHOR]

Magnetron-sputtering deposition of coatings onto a flat surface is well established, but coating metal on the curved surface of nanoparticles (NPs) is still challenging. Herein, by using a magnetron sputtering system in which a vibration generator with variable frequency is applied to the sample stage to maintain NPs' dispersity and movability, we successfully deposit noble metal (Au, Ag, Pt) nanodots onto the surface of TiO 2 NPs. It is found that the metal nanodots are of a size of 1–6 nm and they are firmly bonded to the TiO 2 NPs' surface. The photocatalytic activity of Ag/TiO 2 is demonstrated to be 2.7 times that of pure TiO 2 under simulated sunlight irradiation. This study creates new opportunities for fabricating excellent NP composite catalysts by liquid-free methods. [ABSTRACT FROM AUTHOR]

A new FRET-based ratiometric fluorescent probe (DSPT) for the detection of bisulfite was developed by the dansyl-piperazine-benzothiazolium conjugate platform, in which C C conjugated with benzothiazolium was recognition unit and benzothiazolium moiety was mitochondria-targeted unit. The C C double bond group would undergo Michael addition reaction with bisulfite, triggering significant fluorescence ratio increasing which was linear to the concentrations of bisulfite. The probe can be used in real sample including dry white wine and tap water. Besides, the test strips for bisulfite were prepared. Moreover, cell imaging results indicate that this probe is cell-membrane permeable and mitochondria-targetable, and can be applied to monitor both exogenous and endogenous bisulfite in ratiometric fluorescence imaging. [ABSTRACT FROM AUTHOR]

To explore the potential of using sodium bicarbonate to take the place of sodium chloride, the changes in endogenous fluorescence, ultraviolet absorption spectrum, water holding capacity, gel properties and water distribution of pork myofibrillar protein were investigated. Because of the increase in pH and the exposure of more tryptophan and tyrosine residues with increasing sodium bicarbonate, the cooking yield and gel strength of pork myofibrillar protein significantly increased (P < 0.05), and centrifugal loss significantly decreased (P < 0.05). Meanwhile, the colour of the cooked pork myofibrillar protein was darker with increasing sodium bicarbonate, the bound water and immobile water were bound more tightly to the protein matrix, and the mobility of the water was lower, except for in the sample with 0.4 % sodium bicarbonate. Moreover, free water significantly increased (P < 0.05) when 0.4 % sodium bicarbonate was added, indicating that the mobility of the water was enhanced. Overall, the use of sodium bicarbonate could induce the protein conformation change, and improve the bond water content and texture properties of reduced-salt pork myofibrillar protein. [ABSTRACT FROM AUTHOR]

• Reduces the scanning time and data storage space of 3D topographic imaging. • Measuring height is unaffected by the sample reflectivity. • A novel alternative approach with higher lateral resolution level as conventional method. • Applied directly to existing confocal microscopy. Confocal microscopy (CM) is extensively used in many fields due to its unique axial tomographic capabilities and high-resolution three-dimensional imaging properties. However, this requires layer-by-layer scanning on the sample along the axial direction, which restricts the imaging speed. In this study, we have proposed the defocused differential correlation confocal microscopy (DDCCM), which is based on axial sensing imaging and correlation product method. This method can obtain an axial response curve independent of sample reflectivity and fast 3D sensing imaging of the sample by dividing the difference between the two defocused intensity images by their sum, improving the imaging efficiency at least five times higher than that of CM. The proposed method improves the lateral resolution using the correlation product of two defocused intensity images. DDCCM provides a new way for the fast measurement of conventional CM without changing the system conditions. [ABSTRACT FROM AUTHOR]

The Visible Infrared Imaging Radiometer Suite (VIIRS) has observed the Earth since 2011 and will continue for several decades. Unfortunately, few high-level land surface products have been produced and publicly released. The numerical inversion of radiative transfer model (RTM) has long been used for the retrieval of land surface and atmospheric variables from satellite data; however, it is computationally intensive. Some methods (e.g., using look-up tables) are efficient but are most suitable for estimating individual variable. Various machine learning (ML) models have been developed, mostly trained by either ground measurements that may not be adequate to represent different conditions, or RTM simulations that may not be realistic due to model uncertainty and unnecessary combinations of illumination-viewing geometries and variable values. In this study, we replaced the numerical inversion scheme by the ML algorithm that is trained using RTM inversion for estimating multiple variables simultaneously. Our training dataset for the ML models was generated by inverting the land and atmospheric variables from VIIRS top-of-atmosphere (TOA) reflectance data using a coupled land-surface–atmosphere RT model. Two multi-output ML algorithms were explored: backpropagation neural networks and random forest (RF) regression. The best-performing RF model was then applied to estimate seven land and atmospheric variables globally from VIIRS TOA data: the leaf area index (LAI), incident photosynthetically active radiation (PAR), fraction of absorbed photosynthetically active radiation (FAPAR), incident shortwave radiation (ISR), land surface albedo, land surface reflectance, and TOA albedo. The inversion results were validated using ground measurements at 54 sites and showed comparable accuracy to the numerical inversion scheme that is suitable for only small regions. An experiment was also conducted to test this method on a much larger region for detailed evaluation. Finally, seven global estimates in 2013 were experimentally produced. Inter-comparison with the existing satellite products showed that the VIIRS estimates had high consistency with the LAI and FAPAR products of the Global LAnd Surface Satellite (GLASS) products suite. Moreover, the VIIRS officially released the VNP43MA3 albedo and VNP09A1 surface reflectance products, the retrieved estimates also matched them well. It was also shown that simultaneous estimation of all seven variables was more accurate and efficient than estimating individual variable sequentially. • A method to replace the coupled RT model by the machine learning method for estimating multiple variables simultaneously. • The training dataset was generated by reversely simulating using the coupled RT model from TOA reflectance. • Simultaneous estimation of multiple variables is more accurate and efficient than estimating individual variable sequentially • The generated multiple products have high accuracy based on extensive validation. [ABSTRACT FROM AUTHOR]

• This paper studies how firms strategically react to the 2021 U.S. capitol attack. • Corporate decision of speaking out against the capitol attack protects shareholder value and reduces firm political risks. • Corporate political activism largely affects politicians' PAC fundraising. This study explores firms' reactions to the 2021 U.S. Capitol Attack. We find that firms donating more to the 147 Republicans who voted to overturn the 2020 presidential election are more likely to speak out against the Capitol Attack to suspend political contributions. By doing so, firms mitigate negative stock market reactions and subsequently lower their political risk. Suspension of corporate political donations has profound impacts on the 147 Republicans that they almost lost all political contributions from private sectors right after the Capitol Attack. Our findings suggest that, when facing unexpected political catastrophes, firms make strategic political activist actions. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a terrestrial essential climate variable that is required in a variety of ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has been widely used, but its current version (V5) from Moderate Resolution Imaging Spectroradiometer (MODIS) data has several limitations, such as frequent temporal fluctuation, large data gaps, high dependence on the quality of surface reflectance, and low computational efficiency. To address these issues, this paper presents a deep learning model to generate a new version of the LAI product (V6) at 250-m resolution from MODIS data from 2000 onward. Unlike most existing algorithms that estimate one LAI value at one time for each pixel, this model estimates LAI for 2 years simultaneously. Three widely used LAI products (MODIS C6, GLASS V5, and PROBA-V V1) are used to generate global representative time-series LAI training samples using K-means clustering analysis and least difference criteria. We explore four machine learning models, the general regression neural network (GRNN), long short-term memory (LSTM), gated recurrent unit (GRU), and Bidirectional LSTM (Bi-LSTM), and identify Bi-LSTM as the best model for product generation. This new product is directly validated using 79 high-resolution LAI reference maps from three in situ observation networks. The results show that GLASS V6 LAI achieves higher accuracy, with a root mean square (RMSE) of 0.92 at 250 m and 0.86 at 500 m, while the RMSE is 0.98 for PROBA-V at 300 m, 1.08 for GLASS V5, and 0.95 for MODIS C6 both at 500 m. Spatial and temporal consistency analyses also demonstrate that the GLASS V6 LAI product is more spatiotemporally continuous and has higher quality in terms of presenting more realistic temporal LAI dynamics when the surface reflectance is absent for a long period owing to persistent cloud/aerosol contaminations. The results indicate that the new Bi-LSTM deep learning model runs significantly faster than the GLASS V5 algorithm, avoids the reconstruction of surface reflectance data, and is resistant to the noises (cloud and snow contamination) or missing values contained in surface reflectance than other methods, as the Bi-LSTM can effectively extract information across the entire time series of surface reflectance rather than a single time point. To our knowledge, this is the first global time-series LAI product at the 250-m spatial resolution that is freely available to the public (www.geodata.cn and www.glass.umd.edu). • Exploring a deep learning model for global LAI estimation based on temporal and spectral signatures of MODIS reflectance. • Generating the first global 21-year LAI product at 250-m spatial resolution. • The retrieved LAI maintains spatio-temporal consistency even when the high-quality reflectance is absent for a long period. • The learning model is very efficient and requires no smoothing and gap-filling of surface reflectance. [ABSTRACT FROM AUTHOR]