Xihua University

Graphene and graphene oxide have attracted tremendous interest over the past decade due to their unique and excellent electronic, optical, mechanical, and chemical properties. This review focuses on the functional modification of graphene and graphene oxide. First, the basic structure, preparation methods and properties of graphene and graphene oxide are briefly described. Subsequently, the methods for the reduction of graphene oxide are introduced. Next, the functionalization of graphene and graphene oxide is mainly divided into covalent binding modification, non-covalent binding modification and elemental doping. Then, the properties and application prospects of the modified products are summarized. Finally, the current challenges and future research directions are presented in terms of surface functional modification for graphene and graphene oxide.

Adhesive hydrogels have gained popularity in biomedical applications, however, traditional adhesive hydrogels often exhibit short-term adhesiveness, poor mechanical properties and lack of antibacterial ability. Here, a plant-inspired adhesive hydrogel has been developed based on Ag-Lignin nanoparticles (NPs)triggered dynamic redox catechol chemistry. Ag-Lignin NPs construct the dynamic catechol redox system, which creates long-lasting reductive-oxidative environment inner hydrogel networks. This redox system, generating catechol groups continuously, endows the hydrogel with long-term and repeatable adhesiveness. Furthermore, Ag-Lignin NPs generate free radicals and trigger self-gelation of the hydrogel under ambient environment. This hydrogel presents high toughness for the existence of covalent and non-covalent interaction in the hydrogel networks. The hydrogel also possesses good cell affinity and high antibacterial activity due to the catechol groups and bactericidal ability of Ag-Lignin NPs. This study proposes a strategy to design tough and adhesive hydrogels based on dynamic plant catechol chemistry.

We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.

The novel coronavirus pneumonia (COVID-19) epidemic has brought serious social psychological impact to the Chinese people, especially those quarantined and thus with limited access to face-to-face communication and traditional social psychological interventions. To better deal with the urgent psychological problems of people involved in the COVID-19 epidemic, we developed a new psychological crisis intervention model by utilizing internet technology. This new model, one of West China Hospital, integrates physicians, psychiatrists, psychologists and social workers into Internet platforms to carry out psychological intervention to patients, their families and medical staff. We hope this model will make a sound basis for developing a more comprehensive psychological crisis intervention response system that is applicable for urgent social and psychological problems.

Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

By adding aluminium (Al) into lead selenide (PbSe), we successfully prepared n-type PbSe thermoelectric materials with a figure-of-merit (ZT) of 1.3 at 850 K. Such a high ZT is achieved by a combination of high Seebeck coefficient caused by very possibly the resonant states in the conduction band created by Al dopant and low thermal conductivity from nanosized phonon scattering centers.

Practice of freedom is a new change of education from the traditional role to the modern role.Education,as the practice of freedom,is based on Marxist philosophy.It is concerned about the integrity of everyone's life,the development of subjectivity and the realization of self worth,reflecting the current people-oriented dimension.As the practice of freedom,education is required to respect the freedom of persons being educated,to develop their ability to achieve freedom,and to focus on the guidance of its direction,so that they will learn how to correctly practise freedom.Meanwhile,education should protect the educatees' freedom by creating all kinds of conditions to play the role of education which is to enlighten.

Significance Higher carrier mobility can contribute to a larger power factor, so it is important to identify effective means for achieving higher carrier mobility. Since carrier mobility is governed by the band structure and the carrier scattering mechanism, its possible enhancement could be obtained by manipulating either or both of these. Here, we report a substantial enhancement in carrier mobility by tuning the carrier scattering mechanism in n-type Mg 3 Sb 2 -based materials. The ionized impurity scattering in these materials has been shifted into mixed scattering by acoustic phonons and ionized impurities. Our results clearly demonstrate that the strategy of tuning the carrier scattering mechanism is quite effective for improving the mobility and should also be applicable to other material systems.

The mechanisms, figures of merit, and systems for wearable power generation are reviewed in this article. Future perspectives lie in breakthrough technologies of fiber electronics, fully printable, flexible SoC, and IoT-enabled self-awareness systems.

Abstract 2D conductive nanosheets are central to electronic applications because of their large surface areas and excellent electronic properties. However, tuning the multifunctions and hydrophilicity of conductive nanosheets are still challenging. Herein, a green strategy is developed for fabricating conductive, redox‐active, water‐soluble nanosheets via the self‐assembly of poly(3,4‐ethylenedioxythiophene) (PEDOT) on the polydopamine‐reduced and sulfonated graphene oxide (PSGO) template. The conductivity and hydrophilicity of nanosheets are highly improved by PSGO. The nanosheets are redox active due to the abundant catechol groups and can be used as versatile nanofillers in developing conductive and adhesive hydrogels. The nanosheets create a mussel‐inspired redox environment inside the hydrogel networks and endow the hydrogel with long‐term and repeatable adhesiveness. This hydrogel is biocompatible and can be implanted for biosignals detection in vivo. This mussel‐inspired strategy for assembling 2D nanosheets can be adapted for producing diverse multifunctional nanomaterials, with various potential applications in bioelectronics.

Until now, neighbor-embedding-based (NE) algorithms for super-resolution (SR) have carried out two independent processes to synthesize high-resolution (HR) image patches. In the first process, neighbor search is performed using the Euclidean distance metric, and in the second process, the optimal weights are determined by solving a constrained least squares problem. However, the separate processes are not optimal. In this paper, we propose a sparse neighbor selection scheme for SR reconstruction. We first predetermine a larger number of neighbors as potential candidates and develop an extended Robust-SL0 algorithm to simultaneously find the neighbors and to solve the reconstruction weights. Recognizing that the k-nearest neighbor (k-NN) for reconstruction should have similar local geometric structures based on clustering, we employ a local statistical feature, namely histograms of oriented gradients (HoG) of low-resolution (LR) image patches, to perform such clustering. By conveying local structural information of HoG in the synthesis stage, the k-NN of each LR input patch is adaptively chosen from their associated subset, which significantly improves the speed of synthesizing the HR image while preserving the quality of reconstruction. Experimental results suggest that the proposed method can achieve competitive SR quality compared with other state-of-the-art baselines.

This paper proposes a stochastic dynamic programming framework for the optimal energy management of a smart home with plug-in electric vehicle (PEV) energy storage. This paper is motivated by the challenges associated with intermittent renewable energy supplies and the local energy storage opportunity presented by vehicle electrification. This paper seeks to minimize electricity ratepayer cost, while satisfying home power demand and PEV charging requirements. First, various operating modes are defined, including vehicle-to-grid, vehicle-to-home, and grid-to-vehicle. Second, we use equivalent circuit PEV battery models and probabilistic models of trip time and trip length to formulate the PEV to smart home energy management stochastic optimization problem. Finally, based on time-varying electricity price and time-varying home power demand, we examine the performance of the three operating modes for typical weekdays.

This paper empirically tests the effect of FinTech development on bank risk taking using unbalanced bank-level panel data from China for the period from 2011 to 2018. We use the media's attention paid to FinTech-related information to gauge FinTech development. We find robust evidence that the development of FinTech exacerbates banks’ risk taking in general. The heterogeneity analysis further indicates that the asset quality deterioration effect brought about by prosperous FinTech is more salient in banks with larger sizes, lower efficiency, more shadow banking business and more interest-based income. Moreover, the nexus between FinTech and banks’ risk taking is a U-shaped trend, with FinTech initially intensifying and then weakening banks’ risk taking. Moreover, the banks’ responses regarding the U-shaped effect are heterogeneous among different ownership structures. The responses by state- and jointly owned banks are not notable, while those of city banks, foreign banks and rural banks are more sensitive.

Nanoemulsions have attracted significant attention in food fields and can increase the functionality of the bioactive compounds contained within them. In this paper, the preparation methods, including low-energy and high-energy methods, were first reviewed. Second, the physical and chemical destabilization mechanisms of nanoemulsions, such as gravitational separation (creaming or sedimentation), flocculation, coalescence, Ostwald ripening, lipid oxidation and so on, were reviewed. Then, the impact of different stabilizers, including emulsifiers, weighting agents, texture modifiers (thickening agents and gelling agents), ripening inhibitors, antioxidants and chelating agents, on the physicochemical stability of nanoemulsions were discussed. Finally, the applications of nanoemulsions for the delivery of functional ingredients, including bioactive lipids, essential oil, flavor compounds, vitamins, phenolic compounds and carotenoids, were summarized. This review can provide some reference for the selection of preparation methods and stabilizers that will improve performance in nanoemulsion-based products and expand their usage.

Quinoprotein glucose dehydrogenase (GDH) is the most important enzyme of inorganic phosphorus-dissolving metabolism, catalyzing the oxidation of glucose to gluconic acid. The insoluble phosphate in the sediment is converted into soluble phosphate, facilitating mass reproduction of algae. Therefore, studying the diversity of gcd genes which encode GDH is beneficial to reveal the microbial group that has a significant influence on the eutrophication of water. Taking the eutrophic Sancha Lake sediments as the research object, we acquired samples from six sites in the spring and autumn. A total of 219,778 high-quality sequences were obtained by DNA extraction of microbial groups in sediments, PCR amplification of the gcd gene, and high-throughput sequencing. Six phyla, nine classes, 15 orders, 29 families, 46 genera, and 610 operational taxonomic units (OTUs) were determined, suggesting the high genetic diversity of gcd. Gcd genes came mainly from the genera of Rhizobium (1.63–77.99%), Ensifer (0.13–56.95%), Shinella (0.32–25.49%), and Sinorhizobium (0.16–11.88%) in the phylum of Proteobacteria (25.10–98.85%). The abundance of these dominant gcd-harboring bacteria was higher in the spring than in autumn, suggesting that they have an important effect on the eutrophication of the Sancha Lake. The alpha and beta diversity of gcd genes presented spatial and temporal differences due to different sampling site types and sampling seasons. Pearson correlation analysis and canonical correlation analysis (CCA) showed that the diversity and abundance of gcd genes were significantly correlated with environmental factors such as dissolved oxygen (DO), phosphorus hydrochloride (HCl–P), and dissolved total phosphorus (DTP). OTU composition was significantly correlated with DO, total organic carbon (TOC), and DTP. GDH encoded by gcd genes transformed insoluble phosphate into dissolved phosphate, resulting in the eutrophication of Sancha Lake. The results suggest that gcd genes encoding GDH may play an important role in lake eutrophication.

The increasing demand for high food quality and safety, and concerns of environment sustainable development have been encouraging researchers in the food industry to exploit the robust and green biodegradable nanocomposites, which provide new opportunities and challenges for the development of nanomaterials in the food industry. This review paper aims at summarizing the recent three years of research findings on the new development of nanomaterials for food packaging. Two categories of nanomaterials (i.e., inorganic and organic) are included. The synthetic methods, physical and chemical properties, biological activity, and applications in food systems and safety assessments of each nanomaterial are presented. This review also highlights the possible mechanisms of antimicrobial activity against bacteria of certain active nanomaterials and their health concerns. It concludes with an outlook of the nanomaterials functionalized in food packaging.

The electrochemical coreduction of carbon dioxide (CO2) and nitrogenous species (such as NO3–, NO2–, N2, and NO) for urea synthesis under ambient conditions provides a promising solution to realize carbon/nitrogen neutrality and mitigate environmental pollution. Although an increasing number of studies have made some breakthroughs in electrochemical urea synthesis, the unsatisfactory Faradaic efficiency, low urea yield rate, and ambiguous C–N coupling reaction mechanisms remain the major obstacles to its large-scale applications. In this review, we present the recent progress on electrochemical urea synthesis based on CO2 and nitrogenous species in aqueous solutions under ambient conditions, providing useful guidance and discussion on the rational design of metal nanocatalyst, the understanding of the C–N coupling reaction mechanism, and existing challenges and prospects for electrochemical urea synthesis. We hope that this review can stimulate more insights and inspiration toward the development of electrocatalytic urea synthesis technology.

Abstract Microplastics are emerging persistent pollutants that have been extensively detected in aqueous environments. Yet, scientists have little knowledge of microplastic pollution in soils. This study reviewed over 60 articles, with the following objectives: (i) to discuss sources and the global distribution of microplastics in soils; (ii) to evaluate current extraction techniques and analytical methods for microplastics in soils; and (iii) to comprehensively assess their adverse impacts on soils and soil organisms. Moreover, this review highlights the lack of research into microplastic contamination in soils as a significant knowledge gap. Research into the fate, sources and analytical techniques of soil microplastics and the interactions between soil organisms, soils and microplastics is essential in order to underpin management decisions aimed at safeguarding the ecological integrity of our soils. © 2020 Society of Chemical Industry

This review summarizes promising strategies including the design of catalysts and the construction of coupled electrocatalytic reaction systems, aimed at achieving the selective production of various products from CO 2 electroreduction.

This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.