Anhui University

Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.

(NCs). NCs have shown high catalytic activity and unique selectivity in many catalytic reactions, which are related to their ultrasmall size, abundant unsaturated active sites, and unique electronic structure different from that of traditional nanoparticles (NPs). More importantly, because of their definite structure and monodispersity, they are used as model catalysts to reveal the correlation between catalyst performance and structure at the atomic scale. Therefore, this review aims to summarize the recent progress on NCs in catalysis and provide potential theoretical guidance for the rational design of high-performance catalysts. First a brief summary of the synthetic strategies and characterization methods of NCs is provided. Then the primary focus of this review-the model catalyst role of NCs in catalysis-is illustrated from theoretical and experimental perspectives, particularly in electrocatalysis, photocatalysis, photoelectric conversion, and catalysis of organic reactions. Finally, the main challenges and opportunities are examined for a deep understanding of the key catalytic steps with the goal of expanding the catalytic application range of NCs.

Silver nanoparticles (NPs) were rapidly synthesized by treating silver ions with a Capsicum annuum L. extract. The reaction process was simple and convenient to handle, and was monitored using ultraviolet-visible spectroscopy (UV-vis). The effect of Capsicum annuum L. proteins on the formation of silver NPs was investigated using X-ray photoemission spectroscopy (XPS), electrochemical measurements, Fourier-transform infrared spectroscopy (FTIR) and differential spectrum techniques. The morphology and crystalline phase of the NPs were determined from transmission electron microscopy (TEM), selected area electron diffraction (SAED) and X-ray diffraction (XRD) spectra. The results indicated that the proteins, which have amine groups, played a reducing and controlling role during the formation of silver NPs in the solutions, and that the secondary structure of the proteins changed after reaction with silver ions. The crystalline phase of the NPs changed from polycrystalline to single crystalline and increased in size with increasing reaction time. A recognition–reduction–limited nucleation and growth model was suggested to explain the possible formation mechanism of silver NPs in Capsicum annuum L. extract.

Deep supervised learning has achieved great success in the last decade. However, its defects of heavy dependence on manual labels and vulnerability to attacks have driven people to find other paradigms. As an alternative, self-supervised learning (SSL) attracts many researchers for its soaring performance on representation learning in the last several years. Self-supervised representation learning leverages input data itself as supervision and benefits almost all types of downstream tasks. In this survey, we take a look into new self-supervised learning methods for representation in computer vision, natural language processing, and graph learning. We comprehensively review the existing empirical methods and summarize them into three main categories according to their objectives: generative, contrastive, and generative-contrastive (adversarial). We further collect related theoretical analyses on self-supervised learning to provide deeper thoughts on why self-supervised learning works. Finally, we briefly discuss open problems and future directions for self-supervised learning. An outline slide for the survey is provided.

Due to their atomically precise structures and intriguing chemical/physical properties, metal nanoclusters are an emerging class of modular nanomaterials. Photo-luminescence (PL) is one of their most fascinating properties, due to the plethora of promising PL-based applications, such as chemical sensing, bio-imaging, cell labeling, phototherapy, drug delivery, and so on. However, the PL of most current nanoclusters is still unsatisfactory-the PL quantum yield (QY) is relatively low (generally lower than 20%), the emission lifetimes are generally in the nanosecond range, and the emitted color is always red (emission wavelengths of above 630 nm). To address these shortcomings, several strategies have been adopted, and are reviewed herein: capped-ligand engineering, metallic kernel alloying, aggregation-induced emission, self-assembly of nanocluster building blocks into cluster-based networks, and adjustments on external environment factors. We further review promising applications of these fluorescent nanoclusters, with particular focus on their potential to impact the fields of chemical sensing, bio-imaging, and bio-labeling. Finally, scope for improvements and future perspectives of these novel nanomaterials are highlighted as well. Our intended audience is the broader scientific community interested in the fluorescence of metal nanoclusters, and our review hopefully opens up new horizons for these scientists to manipulate PL properties of nanoclusters. This review is based on publications available up to December 2018.

Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.

The heat shock proteins (HSPs) are ubiquitous and conserved protein families in both prokaryotic and eukaryotic organisms, and they maintain cellular proteostasis and protect cells from stresses. HSP protein families are classified based on their molecular weights, mainly including large HSPs, HSP90, HSP70, HSP60, HSP40, and small HSPs. They function as molecular chaperons in cells and work as an integrated network, participating in the folding of newly synthesized polypeptides, refolding metastable proteins, protein complex assembly, dissociating protein aggregate dissociation, and the degradation of misfolded proteins. In addition to their chaperone functions, they also play important roles in cell signaling transduction, cell cycle, and apoptosis regulation. Therefore, malfunction of HSPs is related with many diseases, including cancers, neurodegeneration, and other diseases. In this review, we describe the current understandings about the molecular mechanisms of the major HSP families including HSP90/HSP70/HSP60/HSP110 and small HSPs, how the HSPs keep the protein proteostasis and response to stresses, and we also discuss their roles in diseases and the recent exploration of HSP related therapy and diagnosis to modulate diseases. These research advances offer new prospects of HSPs as potential targets for therapeutic intervention.

A principal goal of cancer nanomedicine is to deliver therapeutics effectively to cancer cells within solid tumors. However, there are a series of biological barriers that impede nanomedicine from reaching target cells. Here, we report a stimuli-responsive clustered nanoparticle to systematically overcome these multiple barriers by sequentially responding to the endogenous attributes of the tumor microenvironment. The smart polymeric clustered nanoparticle (iCluster) has an initial size of ∼100 nm, which is favorable for long blood circulation and high propensity of extravasation through tumor vascular fenestrations. Once iCluster accumulates at tumor sites, the intrinsic tumor extracellular acidity would trigger the discharge of platinum prodrug-conjugated poly(amidoamine) dendrimers (diameter ∼5 nm). Such a structural alteration greatly facilitates tumor penetration and cell internalization of the therapeutics. The internalized dendrimer prodrugs are further reduced intracellularly to release cisplatin to kill cancer cells. The superior in vivo antitumor activities of iCluster are validated in varying intractable tumor models including poorly permeable pancreatic cancer, drug-resistant cancer, and metastatic cancer, demonstrating its versatility and broad applicability.

Metal nanoclusters fill the gap between discrete atoms and plasmonic nanoparticles, providing unique opportunities for investigating the quantum effects and precise structure-property correlations at the atomic level. As a versatile strategy, alloying can largely improve the physicochemical performances compared to the corresponding homo-metal nanoclusters, and thus benefit the applications of such nanomaterials. In this review, we highlight the achievements of atomically precise alloy nanoclusters, and summarize the alloying principles and fundamentals, including the synthetic methods, site-preferences for different heteroatoms in the templates, and alloying-induced structure and property changes. First, based on various Au or Ag nanocluster templates, heteroatom doping modes are presented. The templates with electronic shell-closing configurations tend to maintain their structures during doping, while the others may undergo transformation and give rise to alloy nanoclusters with new structures. Second, alloy nanoclusters of specific magic sizes are reviewed. The arrangement of different atoms is related to the symmetry of the structures; that is, different atoms are symmetrically located in the nanoclusters of smaller sizes, and evolve into shell-by-shell structures at larger sizes. Then, we elaborate on the alloying effects in terms of optical, electrochemical, electroluminescent, magnetic and chiral properties, as well as the stability and reactivity via comparisons between the doped nanoclusters and their homo-metal counterparts. For example, central heteroatom-induced photoluminescence enhancement is emphasized. The applications of alloy nanoclusters in catalysis, chemical sensing, bio-labeling, and other fields are further discussed. Finally, we provide perspectives on existing issues and future efforts. Overall, this review provides a comprehensive synthetic toolbox and controllable doping modes so as to achieve more alloy nanoclusters with customized compositions, structures, and properties for applications. This review is based on publications available up to February 2020.

During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.

The efficient use of solar energy has received wide interest due to increasing energy and environmental concerns. A potential means in chemistry is sunlight-driven catalytic reactions. We report here on the direct harvesting of visible-to-near-infrared light for chemical reactions by use of plasmonic Au-Pd nanostructures. The intimate integration of plasmonic Au nanorods with catalytic Pd nanoparticles through seeded growth enabled efficient light harvesting for catalytic reactions on the nanostructures. Upon plasmon excitation, catalytic reactions were induced and accelerated through both plasmonic photocatalysis and photothermal conversion. Under the illumination of an 809 nm laser at 1.68 W, the yield of the Suzuki coupling reaction was ~2 times that obtained when the reaction was thermally heated to the same temperature. Moreover, the yield was also ~2 times that obtained from Au-TiOx-Pd nanostructures under the same laser illumination, where a 25-nm-thick TiOx shell was introduced to prevent the photocatalysis process. This is a more direct comparison between the effect of joint plasmonic photocatalysis and photothermal conversion with that of sole photothermal conversion. The contribution of plasmonic photocatalysis became larger when the laser illumination was at the plasmon resonance wavelength. It increased when the power of the incident laser at the plasmon resonance was raised. Differently sized Au-Pd nanostructures were further designed and mixed together to make the mixture light-responsive over the visible to near-infrared region. In the presence of the mixture, the reactions were completed within 2 h under sunlight, while almost no reactions occurred in the dark.

Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions. To remedy this issue, this article proposes a coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem. The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one. While the two populations are evolved by the same optimizer separately, the assistance in solving the original CMOP is achieved by sharing useful information between the two populations. In the experiments, the proposed framework is compared to several state-of-the-art algorithms tailored for CMOPs. High competitiveness of the proposed framework is demonstrated by applying it to 47 benchmark CMOPs and the vehicle routing problem with time windows.

Abstract Demonstrated here is the correlation between atomic configuration induced electronic density of single‐atom Co active sites and oxygen reduction reaction (ORR) performance by combining density‐functional theory (DFT) calculations and electrochemical analysis. Guided by DFT calculations, a MOF‐derived Co single‐atom catalyst with the optimal Co 1 ‐N 3 PS active moiety incorporated in a hollow carbon polyhedron (Co 1 ‐N 3 PS/HC) was designed and synthesized. Co 1 ‐N 3 PS/HC exhibits outstanding alkaline ORR activity with a half‐wave potential of 0.920 V and superior ORR kinetics with record‐level kinetic current density and an ultralow Tafel slope of 31 mV dec −1 , exceeding that of Pt/C and almost all non‐precious ORR electrocatalysts. In acidic media the ORR kinetics of Co 1 ‐N 3 PS/HC still surpasses that of Pt/C. This work offers atomic‐level insight into the relationship between electronic density of the active site and catalytic properties, promoting rational design of efficient catalysts.

The current literature of evolutionary many-objective optimization is merely focused on the scalability to the number of objectives, while little work has considered the scalability to the number of decision variables. Nevertheless, many real-world problems can involve both many objectives and large-scale decision variables. To tackle such large-scale many-objective optimization problems (MaOPs), this paper proposes a specially tailored evolutionary algorithm based on a decision variable clustering method. To begin with, the decision variable clustering method divides the decision variables into two types: 1) convergence-related variables and 2) diversity-related variables. Afterward, to optimize the two types of decision variables, a convergence optimization strategy and a diversity optimization strategy are adopted. In addition, a fast nondominated sorting approach is developed to further improve the computational efficiency of the proposed algorithm. To assess the performance of the proposed algorithm, empirical experiments have been conducted on a variety of large-scale MaOPs with up to ten objectives and 5000 decision variables. Our experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art evolutionary algorithms in terms of the scalability to decision variables on MaOPs.

Excitonic effects caused by Coulomb interactions between electrons and holes play subtle and significant roles on photocatalysis, yet have been long ignored. Herein, porphyrinic covalent organic frameworks (COFs, specifically DhaTph-M), in the absence or presence of different metals in porphyrin centers, have been shown as ideal models to regulate excitonic effects. Remarkably, the incorporation of Zn2+ in the COF facilitates the conversion of singlet to triplet excitons, whereas the Ni2+ introduction promotes the dissociation of excitons to hot carriers under photoexcitation. Accordingly, the discriminative excitonic behavior of DhaTph-Zn and DhaTph-Ni enables the activation of O2 to 1O2 and O2•–, respectively, under visible light irradiation, resulting in distinctly different activity and selectivity in photocatalytic terpinene oxidation. Benefiting from these results, DhaTph-Ni exhibits excellent photocatalytic activity in O2•–-engaged hydroxylation of boronic acid, while DhaTph-Zn possesses superior performance in 1O2-mediated selective oxidation of organic sulfides. This work provides in-depth insights into molecular oxygen activation and opens an avenue to the regulation of excitonic effects based on COFs.

The g-C(3)N(4)-ZnO composite photocatalysts with various weight percents of ZnO were synthsized by a simple calcination process. The photocatalysts were characterized by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), UV-vis diffuse reflection spectroscopy (UV-vis), X-ray photoelectron spectroscopy (XPS) and thermogravimetric analysis (TGA). The PXRD and HR-TEM results show that the composite materials consist of hexagonal wurzite phase ZnO and g-C(3)N(4). The solid-state UV-vis diffuse reflection spectra show that the absorption edge of the composite materials shifts toward the lower energy region and to longer wavelengths in comparison with pure ZnO and g-C(3)N(4). Remarkably, the photocatalytic activity of g-C(3)N(4)-ZnO composites has been demonstrated, via photodegradation of Methyl Orange (MO) and p-nitrophenol experiments. The photocatalytic activity of g-C(3)N(4)-ZnO for photodegradation of Methyl Orange and p-nitrophenol under visible light irradiation was increased by over 3 and 6 times, respectively, to be much higher than that of single-phase g-C(3)N(4), clearly demonstrating a synergistic effect between ZnO and g-C(3)N(4). The concentrations of Zn(2+) in g-C(3)N(4)-ZnO system after a photocatalytic reaction at various reaction times were found to be much lower than those for a ZnO system under the same reaction conditions, indicating that the g-C(3)N(4)-ZnO composite possesses excellent long-term stability for a photocatalytic reaction in aqueous solutions. Furthermore, a synergistic photocatalysis mechanism between ZnO and g-C(3)N(4) was proposed based on the photodegradation results. Such obviously improved performance of g-C(3)N(4)-ZnO can be ascribed mainly to the enhancement of electron-hole separations at the interface of ZnO and g-C(3)N(4).

The rod-shaped Au25 nanocluster possesses a low photoluminescence quantum yield (QY=0.1%) and hence is not of practical use in bioimaging and related applications. Herein, we show that substituting silver atoms for gold in the 25-atom matrix can drastically enhance the photoluminescence. The obtained Ag(x)Au(25-x) (x=1-13) nanoclusters exhibit high quantum yield (QY=40.1%), which is in striking contrast with the normally weakly luminescent Ag(x)Au(25-x) species (x=1-12, QY=0.21%). X-ray crystallography further determines the substitution sites of Ag atoms in the Ag(x)Au(25-x) cluster through partial occupancy analysis, which provides further insight into the mechanism of photoluminescence enhancement.

A new kind of nitrogen-doped graphene/carbon nanotube nanocomposite can be synthesized by a facile hydrothermal process under mild conditions, which exhibits synergistically enhanced electrochemical activity for the oxygen reduction reaction. This research provides a new route to access a metal-free electrocatalyst with high activity under mild conditions. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Abstract Direct conversion of solar light into chemical energy by means of photocatalysis or photoelectrocatalysis is currently a point of focus for sustainable energy development and environmental remediation. However, its current efficiency is still far from satisfying, suffering especially from severe charge recombination. To solve this problem, the piezo‐phototronic effect has emerged as one of the most effective strategies for photo(electro)catalysis. Through the integration of piezoelectricity, photoexcitation, and semiconductor properties, the built‐in electric field by mechanical stimulation induced polarization can serve as a flexible autovalve to modulate the charge‐transfer pathway and facilitate carrier separation both in the bulk phase and at the surfaces of semiconductors. This review focuses on illustrating the trends and impacts of research based on piezo‐enhanced photocatalytic reactions. The fundamental mechanisms of piezo‐phototronics modulated band bending and charge migration are highlighted. Through comparing and classifying different categories of piezo‐photocatalysts (like the typical ZnO, MoS 2 , and BaTiO 3 ), the recent advances in polarization‐promoted photo(electro)catalytic processes involving water splitting and pollutant degradation are overviewed. Meanwhile the optimization methods to promote their catalytic activities are described. Finally, the outlook for future development of polarization‐enhanced strategies is presented.

Abstract Growth in intermittent renewable sources including solar and wind has sparked increasing interest in electrical energy storage. Grid‐scale energy storage integrated with renewable sources has significant advantages in energy regulation and grid security. Aqueous zinc‐ion batteries (AZIBs) have emerged as a practically attractive option for electrical storage because of environmentally benign aqueous‐based electrolytes, high theoretical capacity of Zn anode, and significant global reserves of Zn. However, application of AZIBs at the grid‐scale is restricted by drawbacks in cathode material(s). Herein, a comprehensive summary of the features and storage mechanisms of the latest cathode materials is provided. The fundamental problems and corresponding in‐depth causes for cathode materials is critically reviewed. It is also assess practical challenges, appraise their translation to commerce and industry, and systematically summarize and discuss the potential solutions reported in recent works. It is established necessary design strategies for Zn anodes and electrolytes that are matched with cathode materials for commercializing AZIBs. Finally, it is concluded with a perspective on the practical prospects for advancing the development of future AZIBs. Findings will be of interest and benefit to a range of researchers and manufacturers in the design and application of AZIBs for grid‐scale energy storage.