One-step Synthesis of Organic Terminal 2D Ti3C2Tx MXene Nanosheets by Etching of Ti3AlC2 in an Organic Lewis Acid Solvent.

The surface and interface chemistry are critical for controlling the properties of two-dimensional transition metal carbides and nitrides (MXenes). Numerous efforts have been devoted to the functionalization of MXenes with small inorganic ligands; however, few etching methods have been reported on the direct bonding of organic groups to MXene surfaces. In this work, we demonstrated an efficient and rapid strategy for the direct synthesis of 2D Ti3C2Tx MXene nanosheets with organic terminal groups in an organic Lewis acid (trifluoromethanesulfonic acid) solvent, without introducing additional intercalations. The dissolution of aluminum and the subsequent in situ introduction of trifluoromethanesulfonic acid resulted in the extraction of Ti3C2Tx MXene (T=CF3SO3 -) (denoted as CF3SO3H-Ti3C2Tx) flakes with sizes reaching 15 µm and high productivity (over 70 %) of monolayers or few layers. More importantly, the large CF3SO3H-Ti3C2Tx MXene nanosheets had high colloidal stability, making them promising as efficient electrocatalysts for the hydrogen evolution reaction.

A universal and scalable transformation of bulk metals into single-atom catalysts in ionic liquids.

Single-atom catalysts (SACs) with maximized metal atom utilization and intriguing properties are of utmost importance for energy conversion and catalysis science. However, the lack of a straightforward and scalable synthesis strategy of SACs on diverse support materials remains the bottleneck for their large-scale industrial applications. Herein, we report a general approach to directly transform bulk metals into single atoms through the precise control of the electrodissolution-electrodeposition kinetics in ionic liquids and demonstrate the successful applicability of up to twenty different monometallic SACs and one multimetallic SAC with five distinct elements. As a case study, the atomically dispersed Pt was electrodeposited onto Ni3N/Ni-Co-graphene oxide heterostructures in varied scales (up to 5 cm × 5 cm) as bifunctional catalysts with the electronic metal-support interaction, which exhibits low overpotentials at 10 mA cm-2 for hydrogen evolution reaction (HER, 30 mV) and oxygen evolution reaction (OER, 263 mV) with a relatively low Pt loading (0.98 wt%). This work provides a simple and practical route for large-scale synthesis of various SACs with favorable catalytic properties on diversified supports using alternative ionic liquids and inspires the methodology on precise synthesis of multimetallic single-atom materials with tunable compositions.

Electronic Modulation in Cu Doped NiCo LDH/NiCo Heterostructure for Highly Efficient Overall Water Splitting.

Layered double hydroxides (LDHs), promising bifunctional electrocatalysts for overall water splitting, are hindered by their poor conductivity and sluggish electrochemical reaction kinetics. Herein, a hierarchical Cu-doped NiCo LDH/NiCo alloy heterostructure with rich oxygen vacancies by electronic modulation is tactfully designed. It extraordinarily effectively drives both the oxygen evolution reaction (151 mV@10 mA cm-2 ) and the hydrogen evolution reaction (73 mV@10 mA cm-2 ) in an alkaline medium. As bifunctional electrodes for overall water splitting, a low cell voltage of 1.51 V at 10 mA cm-2 and remarkable long-term stability for 100 h are achieved. The experimental and theoretical results reveal that Cu doping and NiCo alloy recombination can improve the conductivity and reaction kinetics of NiCo LDH with surface charge redistribution and reduced Gibbs free energy barriers. This work provides a new inspiration for further design and construction of nonprecious metal-based bifunctional electrocatalysts based on electronic structure modulation strategies.

Recent Advances in Electrochemical-Based Silicon Production Technologies with Reduced Carbon Emission.

Sustainable and low-carbon-emission silicon production is currently one of the main focuses for the metallurgical and materials science communities. Electrochemistry, considered a promising strategy, has been explored to produce silicon due to prominent advantages: (a) high electricity utilization efficiency; (b) low-cost silica as a raw material; and (c) tunable morphologies and structures, including films, nanowires, and nanotubes. This review begins with a summary of early research on the extraction of silicon by electrochemistry. Emphasis has been placed on the electro-deoxidation and dissolution-electrodeposition of silica in chloride molten salts since the 21st century, including the basic reaction mechanisms, the fabrication of photoactive Si films for solar cells, the design and production of nano-Si and various silicon components for energy conversion, as well as storage applications. Besides, the feasibility of silicon electrodeposition in room-temperature ionic liquids and its unique opportunities are evaluated. On this basis, the challenges and future research directions for silicon electrochemical production strategies are proposed and discussed, which are essential to achieve large-scale sustainable production of silicon by electrochemistry.

A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem.

As a nondeterministic polynomial (NP) problem, the flexible job shop scheduling problem (FJSP) is a difficult problem to be solved in terms of finding an acceptable solution. In last decades, genetic algorithm (GA) displays very promising performance in the field. In this article, a hybrid algorithm combining global and local search with reinitialization (GLRe)-based GA is proposed to minimize makespan for FJSP. The solution of FJSP is conveniently represented by a double-layer chromosome representation method, which is convenient for subsequent genetic operations, that is, sorting of operations and selection of machines. Two strategies of choosing the job with the most remaining operations (CRO) and 6-dimensional variable determined search position (6D-VSP) are proposed as two components for GA, which are applied to generate a population with superior quality and reduce the global search space during the initialization stage. At the same time, in order to prevent the loss of diversity during evolution, a reinitialization strategy is introduced in the later stage of evolution to adaptively adjust the search domain of the problem. Finally, two sets of benchmark data are tested. The experimental results demonstrate the accuracy and effectiveness of the GLRe proposed in this article for solving FJSP.

Collaborative influence of morphology tuning and RE (La, Y, and Sm) doping on photocatalytic performance of nanoceria.

Tuning morphology and doping additional rare earth (RE) cations are potential techniques to promote the photocatalytic performance of ceria (CeO2), evaluating the collaborative effects of morphology and RE dopants is significant for producing high active ceria-based catalysts. So in this work, cubic, polyhedral and rod-like nanoceria doped with 10 mol % La (lanthanum), Y (yttrium), or Sm (samarium) were synthesized by a facile template-free hydrothermal method. Phases, morphologies, oxygen vacancies (OVs) concentration, energy band structure, photo-carriers separation/recombination, and photodegradation ratio toward methylene blue (MB) dye of as prepared ceria were studied. Results show that doped CeO2 maintains a similar morphology structure with un-doped sample and the band gap narrows slightly. Y-doped nanoceria, with an improved separation and a reduced recombination of photo-excited electrons (e-) and holes (h+), owns a higher MB photodegradation ratio than that of samples doping with La or Sm, which is measured as 79.04, 84.43, and 85.59% for Y-doped cubic, polyhedral, and rod-like CeO2. The collaborative influence of morphology tuning and RE (La, Y, and Sm) doping on photocatalytic performance of nanoceria includes the effects of doped elements and the formation of OVs. The elevation of OVs concentration as well as the separation efficiency of photo-generated e-/h+ are suggested to further enhance the photocatalytic performance of ceria.

Improved Interactive Genetic Algorithm for Three-Dimensional Vase Modeling Design.

Interactive genetic algorithm (IGA) is an effective way to help users with product design optimization. However, in this process, users need to evaluate the fitness of all individuals in each generation. It will cause users' fatigue when users cannot find satisfactory products after multi-generation evaluations. To solve this problem, an improved interactive genetic algorithm (IGA-KDTGIM) is proposed, which combines K-dimensional tree surrogate model and a graphic interaction mechanism. In this algorithm, the K-dimensional tree surrogate model is built on the basis of users' historical evaluation information to assist the user's evaluation, so as to reduce the times of users' evaluation. At the same time, users are allowed to interact with the graphic interface to adjust the shape of the individual, so as to increase users' creation fun and to make the evolution direction of the population conform to users' expectations. The IGA-KDTGIM is applied to the 3D vase design system and independently experimented with IGA, IGA-KDT, and IGA-GIM, respectively. The average fitness, maximum average fitness, and evaluation times of statistical data were compared and analyzed. Compared with traditional IGA, the number of evaluations required by users decreased by 60.0%, and the average fitness of the population increased by 15.0%. The results show that this method can reduce the users' operation fatigue and improve the ability of finding satisfactory solutions to a certain extent.

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET.

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.

Insight into the Contributions of Surface Oxygen Vacancies on the Promoted Photocatalytic Property of Nanoceria.

Oxygen vacancies (OVs) have critical effects on the photoelectric characterizations and photocatalytic activity of nanoceria, but the contributions of surface OVs on the promoted photocatalytic properties are not clear yet. In this work, we synthesized ceria nanopolyhedron (P-CeO2), ceria nanocube (C-CeO2) and ceria nanorod (R-CeO2), respectively, and annealed them at 600 °C in air, 30%, 60% or pure H2. After annealing, the surface OVs concentration of ceria elevates with the rising of H2 concentration. Photocatalytic activity of annealed ceria is promoted with the increasing of surface OVs, the methylene blue photodegradation ratio with pure hydrogen annealed of P-CeO2, C-CeO2 or R-CeO2 is 93.82%, 85.15% and 90.09%, respectively. Band gap of annealed ceria expands first and then tends to narrow slightly with the rising of surface OVs, while the valence band (VB) and conductive band (CB) of annealed ceria changed slightly. Both of photoluminescence spectra and photocurrent results indicate that the separation efficiency of photoinduced electron-hole pairs is significantly enhanced with the increasing of the surface OVs concentration. The notable weakened recombination of photogenerated carrier is suggested to attribute a momentous contribution on the enhanced photocatalytic activity of ceria which contains surface OVs.

Optimal rare-earth (La, Y and Sm) doping conditions and enhanced mechanism for photocatalytic application of ceria nanorods.

Morphological tuning or additional cation doping is one of the potential and simple methods to enhance the photocatalytic properties of ceria, in which rare-earth element doped ceria nanorods (CeO2-RE NRs) are expected to be a promising photocatalyst with high activity. But the optimal doping conditions, including the variety and concentration of RE elements are ambiguous, and the contribution of doped RE ions to the enhancement of photocatalytic activity needs to be further studied. In this work, we doped La, Y and Sm with a wide range of 0%-30% into CeO2 NRs, and investigated the phase, morphology, band gap, oxygen vacancy concentration, PL spectra and photocatalytic activity variation under different doping conditions. All synthesized CeO2-RE NRs possessed a good nanorod morphology except the 15 and 30% Y-doped samples. The energy band gaps of the synthesized samples changed slightly; the 10% CeO2-RE NRs with the narrowest band gaps possessed the higher photocatalytic performance. The most outstanding photocatalyst was found to be the 10% Y-doped CeO2 NRs with a methylene blue photodegradation ratio of 85.59% and rate constant of 0.0134 min-1, which is particularly associated with a significant higher oxygen vacancy concentration and obviously lower recombination rate of photogenerated e-/h+ pairs. The doped RE ions and the promotion of oxygen vacancy generation impede the recombination of photogenerated carriers, which is proposed as the main reason to enhance the photocatalytic property of CeO2.

Triple Archives Particle Swarm Optimization.

There are two common challenges in particle swarm optimization (PSO) research, that is, selecting proper exemplars and designing an efficient learning model for a particle. In this article, we propose a triple archives PSO (TAPSO), in which particles in three archives are used to deal with the above two challenges. First, particles who have better fitness (i.e., elites) are recorded in one archive while other particles who offer faster progress, called profiteers in this article, are saved in another archive. Second, when breeding each dimension of a potential exemplar for a particle, we choose a pair of elite and profiteer from corresponding archives as two parents to generate the dimension value by ordinary genetic operators. Third, each particle carries out a specific learning model according to the fitness of its potential exemplars. Furthermore, there is no acceleration coefficient in TAPSO aiming to simplify the learning models. Finally, if an exemplar has excellent performance, it will be regarded as an outstanding exemplar and saved in the third archive, which can be reused by inferior particles aiming to enhance the exploitation and to save computing resources. The experimental results and comparisons between TAPSO and other eight PSOs on 30 benchmark functions and four real applications suggest that TAPSO attains very promising performance in different types of functions, contributing to both higher solution accuracy and faster convergence speed. Furthermore, the effectiveness and efficiency of these new proposed strategies are discussed based on extensive experiments.

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems.

The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.

Modified differential evolution algorithm with onlooker bee operator for mixed discrete-continuous optimization.

For solving non-linear programming problems containing discrete and continuous variables, this article suggests two modified algorithms based on differential evolution (DE). The two proposed algorithms incorporate a novel random search strategy into DE/best/1 and DE/cur-to-best/1 respectively. Inspired by the artificial bee colony algorithm, the random search strategy overcomes the searching unbalance of DE/best/1 and DE/cur-to-best/1 by enhancing the global exploration capability of promising individuals. Two numerical experiments are given to test the two modified algorithms. Experiment 1 is conducted on the benchmark function set of CEC2005 in order to verify the effectiveness of the improved strategy. Experiment 2 is designed to optimize two mixed discrete-continuous problems to illustrate the competitiveness and the practicality of the proposed algorithms. In particular, the modified DE/cur-to-best/1 finds the new optima of two engineering optimization problems.

Multiobjective Image Color Quantization Algorithm Based on Self-Adaptive Hybrid Differential Evolution.

In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm.