From an algorithm design perspective, evolutionary computation-based machine learning (ECML) methods show advantages over classical integer programming, Markov decision making, and auction algorithms ...in dealing with complex optimization problems, with the most prominent advantage being that ECML has a strong search capability that can largely reduce the computational cost of cluster node analysis. ...this issue aims to provide a platform for the ECML methods and their application in multi-robot systems, demonstrating the possibilities of ECML as much as possible. ...the researchers design the cross-UTPA multi-drone collaborative path planning algorithm, which has no action space limitation for policy learning and can be multi-tasked in parallel, improving the efficiency and generalization of sample processing. ...four articles have been published on this Research Topic, including the findings of scholars and industry personnel in the relevant fields.
Redox flow batteries are promising for large-scale energy storage, but some long-standing problems such as safety issues, system cost and cycling stability must be resolved. Here we demonstrate a ...type of redox flow battery that is based on all-polymer particulate slurry electrolytes. Micro-sized and uniformly dispersed all-polymer particulate suspensions are utilized as redox-active materials in redox flow batteries, breaking through the solubility limit and facilitating the application of insoluble redox-active materials. Expensive ion-exchange membranes are replaced by commercial dialysis membranes, which can simultaneously realize the rapid shuttling of H
ions and cut off the migration of redox-active particulates across the separator via size exclusion. In result, the all-polymer particulate slurry redox flow batteries exhibit a highly reversible multi-electron redox process, rapid electrochemical kinetics and ultra-stable long-term cycling capability.
In brain storm optimization (BSO), the convergent operation utilizes a clustering strategy to group the population into multiple clusters, and the divergent operation uses this cluster information to ...generate new individuals. However, this mechanism is inefficient to regulate the exploration and exploitation search. This article first analyzes the main factors that influence the performance of BSO and then proposes an orthogonal learning framework to improve its learning mechanism. In this framework, two orthogonal design (OD) engines (i.e., exploration OD engine and exploitation OD engine) are introduced to discover and utilize useful search experiences for performance improvements. In addition, a pool of auxiliary transmission vectors with different features is maintained and their biases are also balanced by the OD decision mechanism. Finally, the proposed algorithm is verified on a set of benchmarks and is adopted to resolve the quantitative association rule mining problem considering the support, confidence, comprehensibility, and netconf. The experimental results show that the proposed approach is very powerful in optimizing complex functions. It not only outperforms previous versions of the BSO algorithm but also outperforms several famous OD-based algorithms.
•Ni–Co LDH/RGO composites were synthesized through a facile solvothermal approach.•Porous Ni–Co LDH nanosheets decorated on RGO sheets homogeneously.•The Ni–Co LDH/RGO composite exhibits excellent ...capacitive performance.•Possible mechanism for the enhanced capacitive performance was proposed.
Highly uniform composites composed of NiCo-layered double hydroxide (NiCo-LDH) and reduced graphene oxide (RGO) were successfully fabricated through a facile one-pot solvothermal method. The resulting composites (NiCo-LDH/RGO) display nanosheet architecture with tunable content of NiCo-LDH. By virtue of the greatly enhanced rate of electron and mass transfer as well as the improved specific surface area, the as-obtained NiCo-LDH/RGO composite electrodes exhibit excellent supercapacitive properties, and deliver a maximum specific capacitance of 1911.1Fg−1 at the current density of 2.0Ag−1. Moreover, at the high current density of 20Ag−1, the specific capacitance of NiCo-LDH/RGO nanocomposite still remains as high as 1469.8Fg−1, indicating an extraordinary rate capability. Good cycling stability with the capacitance retention of 74% is obtained after repeating the charge–discharge measurements for 1000cycles at the current density of 20Ag−1. The combination of the high capacity of pseudocapacitive double hydroxides and good conductivity of RGO make the NiCo-LDH/RGO composites promising candidates for electrode materials in energy storage and conversion devices.
Potassium-ion batteries (PIBs) are appealing alternatives to conventional lithium-ion batteries (LIBs) because of their wide potential window, fast ionic conductivity in the electrolyte, and reduced ...cost. However, PIBs suffer from sluggish K
+
reaction kinetics in electrode materials, large volume expansion of electroactive materials, and the unstable solid electrolyte interphase. Various strategies, especially in terms of electrode design, have been proposed to address these issues. In this review, the recent progress on advanced anode materials of PIBs is systematically discussed, ranging from the design principles, and nanoscale fabrication and engineering to the structure-performance relationship. Finally, the remaining limitations, potential solutions, and possible research directions for the development of PIBs towards practical applications are presented. This review will provide new insights into the lab development and real-world applications of PIBs.
The worldwide unrestrained emission of carbon dioxide (CO2) has caused serious environmental pollution and climate change issues. For the sustainable development of human civilization, it is very ...desirable to convert CO2 to renewable fuels through clean and economical chemical processes. Recently, electrocatalytic CO2 conversion is regarded as a prospective pathway for the recycling of carbon resource and the generation of sustainable fuels. In this review, recent research advances in electrocatalytic CO2 reduction are summarized from both experimental and theoretical aspects. The referred electrocatalysts are divided into different classes, including metal–organic complexes, metals, metal alloys, inorganic metal compounds and carbon‐based metal‐free nanomaterials. Moreover, the selective formation processes of different reductive products, such as formic acid/formate (HCOOH/HCOO−), monoxide carbon (CO), formaldehyde (HCHO), methane (CH4), ethylene (C2H4), methanol (CH3OH), ethanol (CH3CH2OH), etc. are introduced in detail, respectively. Owing to the limited energy efficiency, unmanageable selectivity, low stability, and indeterminate mechanisms of electrocatalytic CO2 reduction, there are still many tough challenges need to be addressed. In view of this, the current research trends to overcome these obstacles in CO2 electroreduction field are summarized. We expect that this review will provide new insights into the further technique development and practical applications of CO2 electroreduction.
The worldwide unrestrained emission of carbon dioxide (CO2) has caused serious environmental pollution and climate change issues. In this review, recent advances in electrocatalytic CO2 reduction are summarized from both experimental and theoretical aspects. It is expected that this review will provide new insights into the further technical development and practical applications of CO2 electroreduction.
Automatic extracting of knowledge from massive data samples, i.e., big data analytics (BDA), has emerged as a vital task in almost all scientific research fields. The BDA problems are rather ...difficult to solve due to their large-scale, high-dimensional, and dynamic properties, while the problems with small data are usually hard to handle due to insufficient data samples and incomplete information. Such difficulties lead to the search-based data analytics problem, where a data analysis task is modeled as a complex, dynamic, and computationally expensive optimization problem and then solved by using an iterative algorithm. In this paper, we intend to present an extensive and in-depth discussion on the utilizing of evolutionary computation (EC) based optimization methods including evolutionary algorithms (EAs) and swarm intelligence (SI) for solving search-based data analysis problems. Then, as an example for illustration, we provide a comprehensive review of the applications of state-of-the-art EC methods for different types of data mining problems in bioinformatics. Here, the detailed analysis and discussion are conducted on three types of data samples, which include sequences data, network data, and image data. Finally, we survey the challenges faced by EC methods and the trend for future directions. Based on the applications of EC methods for search-based data analysis problems involving inexact and uncertain information, the insights of data analytics are able to understand better, and more efficient algorithms could be designed to solve real-world complex BDA problems.
Article Highlights
A green low-cost route without the acid washing step is used to prepare the N,O-codoped egg-box-like carbons.
The obtained carbons possess moderate N, O contents and tuned transfer ...channels with three-dimensional (3D) egg-box-like structures.
The fabricated electrode exhibits high areal capacitance and long-term cycle stability.
Functional carbonaceous materials for supercapacitors (SCs) without using acid for post-treatment remain a substantial challenge. In this paper, we present a less harmful strategy for preparing three-dimensional (3D) N,O-codoped egg-box-like carbons (EBCs). The as-prepared EBCs with opened pores provide plentiful channels for ion fast transport, ensure the effective contact of EBCs electrodes and electrolytes, and enhance the electron conduction. The nitrogen and oxygen atoms doped in EBCs improve the surface wettability of EBC electrodes and provide the pseudocapacitance. Consequently, the EBCs display a prominent areal capacitance of 39.8 μF cm
−2
(340 F g
−1
) at 0.106 mA cm
−2
in 6 M KOH electrolyte. The EBC-based symmetric SC manifests a high areal capacitance to 27.6 μF cm
−2
(236 F g
−1
) at 0.1075 mA cm
−2
, a good rate capability of 18.8 μF cm
−2
(160 F g
−1
) at 215 mA cm
−2
and a long-term cycle stability with only 1.9% decay after 50,000 cycles in aqueous electrolyte. Impressively, even in all-solid-state SC, EBC electrode shows a high areal capacitance of 25.0 μF cm
−2
(214 F g
−1
) and energy density of 0.0233 mWh cm
−2
. This work provides an acid-free process to prepare electrode materials from industrial by-products for advanced energy storage devices.
•NiCo2O4/RGO composites were synthesized through a facile approach.•Porous NiCo2O4 hexagonal nanoplates were well combined with RGO sheets.•The capacitive performance of NiCo2O4/RGO composites was ...significantly improved.•Possible mechanism for the enhanced capacitive performance was proposed.
A novel composite with porous NiCo2O4 hexagonal nanoplates deposited on reduced graphene oxide (RGO) sheets is synthesized through a simple hydrothermal method followed by a thermal annealing process. The average side length and thickness of the NiCo2O4 nanoplates are ca. 61 and 9.5nm, respectively. The capacitive performances of the as-prepared composites as electrode materials are investigated. It is found that the NiCo2O4/RGO (NCG) composites exhibit an enhanced capacitive performance as compared with pure NiCo2O4 hexagonal nanoplates and RGO. The NCG composites can achieve a maximum average specific capacitance of 947.4Fg−1 at the current density of 0.5Ag−1, and great rate capability, remaining 725.4Fg−1 at the high current density of 10.0Ag−1. The specific capacitance of the composites decays by only 2.1% after 3000 cycles at the current density of 10.0Ag−1, indicating an excellent cycling stability. The enhanced capacitive performance for NCG composites can be attributed to the structural advantages of high specific surface area, superior electrical properties and well-connected network of the RGO support. The superior capacitive performance demonstrates the promising application of the NCG composites in electrode materials for supercapacitor.
Despite high theoretical energy density, the practical deployment of lithium–sulfur (Li–S) batteries is still not implemented because of the severe capacity decay caused by polysulfide shuttling and ...the poor rate capability induced by low electrical conductivity of sulfur. Herein, we report a novel sulfur host material based on “sea urchin”-like cobalt nanoparticle embedded and nitrogen-doped carbon nanotube/nanopolyhedra (Co-NCNT/NP) superstructures for Li–S batteries. The hierarchical micromesopores in Co-NCNT/NP can allow efficient impregnation of sulfur and block diffusion of soluble polysulfides by physical confinement, and the incorporation of embedded Co nanoparticles and nitrogen doping (∼4.6 at. %) can synergistically improve the adsorption of polysulfides, as evidenced by beaker cell tests. Moreover, the conductive networks of Co-NCNT/NP interconnected by nitrogen-doped carbon nanotubes (NCNTs) can facilitate electron transport and electrolyte infiltration. Therefore, the specific capacity, rate capability, and cycle stability of Li–S batteries are significantly enhanced. As a result, the Co-NCNT/NP based cathode (loaded with 80 wt % sulfur) delivers a high discharge capacity of 1240 mAh g–1 after 100 cycles at 0.1 C (based on the weight of sulfur), high rate capacity (755 mAh g–1 at 2.0 C), and ultralong cycling life (a very low capacity decay of 0.026% per cycle over 1500 cycles at 1.0 C). Remarkably, the composite cathode with high areal sulfur loading of 3.2 mg cm–2 shows high rate capacities and stable cycling performance over 200 cycles.
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