Directing in-situ self-optimization of single-atom catalysts for improved oxygen evolution

The demand for clean and sustainable energy has encouraged the production of hydrogen from water electrolyzers.To overcome the obstacle to improving the efficiency of water electrolyzers,it is highly desired to fabricate active electrocatalysts for the sluggish oxygen evolution process.However,there is generally an intrinsic gap between the as-prepared and real electrocatalysts due to structure evolution under the oxidative reaction conditions.Here,we combine in-situ anionic leaching and atomic deposition to realize single-atom catalysts with self-optimized structures.The introduced F ions facilitate structural transformation from Co(OH)xF into CoOOH(F),which generates an amorphous edge surface to provide more anchoring sites for Ir single atoms.Meanwhile,the in-situ anionic leaching of F ions elevates the Co valence state of Ir_(1)/CoOOH(F)more significantly than the counterpart without F ions(Ir_(1)/CoOOH),leading to stronger adsorption of oxygenated intermediates.As revealed by electrochemical measurements,the increased Ir loading together with the favored adsorption of*OH intermediates improve the catalytic activity of Ir_(1)/CoOOH(F).Specifically,Ir_(1)/CoOOH(F)delivered a current density of 10 mA cm-2at an overpotential of 238 mV,being lower than 314 mV for Ir_(1)/CoOOH.The results demonstrated the facility of the in-situ optimization process to optimize catalyst structure for improved performance.

Study on Cognitive Optical Network Structure and Self-optimization with the Application of Artificial Intelligence Technology

Cognitive optical network is the intermediate to combine artificial intelligence technology with network,and also the important network technology to promote network intelligence level constantly.In the paper,it analyzes the cognitive optical network structure with the application of artificial intelligence technology by starting from the basic conditions of cognitive network and cognitive optional network on the basis of fully understanding the connotation of cognitive network and cognitive optical network,and explores its self-governance functions,so as to better realize the self-optimization and self-configuration of network.

Process analytical technologies and self-optimization algorithms in automated pharmaceutical continuous manufacturing

The pharmaceutical industry is now paying increased attention to continuous manufacturing.While the revolution to continuous and automated manufacturing is deepening in most of the top pharma companies in the world,the advancement of automated pharmaceutical continuous manufacturing in China is relatively slow due to some key challenges including the lack of knowledge on the related technologies and shortage of qualified personnels.In this review,emphasis is given to two of the crucial technologies in automated pharmaceutical continuous manufacturing,i.e.,process analytical technology(PAT)and self-optimizing algorithm.Research work published in recent 5 years employing advanced PAT tools and self-optimization algorithms is introduced,which represents the great progress that has been made in automated pharmaceutical continuous manufacturing.

Q-learning based heterogenous network self-optimization for reconfigurable network with CPC assistance

With the irreversible trend of the convergence and cooperation among heterogeneous networks, there emerge some important issues for network evolution. One of them is to reconfigure network elements such as cellular base stations （BSs） or access points （APs） of wireless local area networks （WLANs） according to the real-time network environment, in order to maximize the cooperation gain of different networks. In this paper, we consider cognitive pilot channel （CPC） as an assistant to enable cooperation among heterogeneous networks. Based on the widely used reinforcement learning algorithm, this paper has proposed the heterogeneous network self-optimization algorithm （HNSA） to solve the adaptation problem in reconfigurable systems. In the algorithm, distributed agents perform reinforcement learning, and make decisions cooperatively with the help of CPC in order to reduce the system blocking rate and improve network revenue. Finally our simulation proves the anticipated goal is achieved.

Probing self-optimization of carbon support in oxygen evolution reaction

Despite acknowledgment of structural reconstruction of materials following oxygen evolution reaction (OER) reaction, the role of support during the reconstruction process has been ignored. Given this, we directly in situ transform the residual iron present in raw single-walled carbon nanotubes (SWCNT) into Fe_(2)O_(3) and thus build Fe_(2)O_(3)-CNT as the model system. Intriguingly, an anomalous self-optimization occurred on SWCNT and the derived components show satisfactory electrochemical performance. Soft X-ray absorption spectroscopy (sXAS) analysis and theory calculation correspondingly indicate that self-optimization yields stronger interaction between SWCNT and Fe_(2)O_(3) nanoparticles, where the electrons migrate from Fe_(2)O_(3) to optimized SWCNT. Such polarization will generate a positive charge center and thus boost the OER activity. This finding directly observes the self-optimization of support effect, providing a new perspective for OER and related electrochemical reactions.

A Dual Closed-Loop Digital Twin Construction Method for Optimizing the Copper Disc Casting Process

The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.

Morphology-controlled Tantalum Diselenide Structures as Self-optimizing Hydrogen Evolution Catalysts

Metallic layered transition metal dichalcogenides(TMDs)nanomaterials based on Group 5 transition metals are attracting substantial interests as alternative catalysts for hydrogen evolution reaction(HER).However,controllable preparation of tantalum diselenide(TaSe2)remains challenging,which has hindered the exploration on its application in HER.Herein,we develop a facile method named surface-assisted chemical vapor transport(SACVT)for controllable synthesis of TaSe2 plates and nanobelts,by regulating the molar ratio of selenium to tantalum and reaction temperature.Unique quasi-arrays and self-supported structure help TaSe2 nanobelt own more active sites and higher ability of charge transfer,so it is superior to TaSe2 plate in electrocatalytic HER.Interestingly,they both exhibit the ability to optimize their morphologies upon cycling for dramatically improved and robust electrocatalytic performance.The selfoptimized structures can increase the effective active surface by exposing more active sites on the basal-planes and edges,shorten the interlayer electron-transfer pathways at a thinned domain,and accelerate the charge transfer,which mainly derive from high basal-plane activity and weak interaction between layers of metallic TaSe2.This work provides a reliable way for controllable synthesis of different TaSe2 structures,motivating further efforts to explore new high-efficiency catalysts in the large family of metallic TMDs for electrochemical energy conversion.

A Novel Exact Fixed-node Quantum Monte Carlo Algorithm

In this paper we proposed a novel exact fixed-node quantum Monte Carlo (EFNQMC) algorithm, which is a self-optimizing and self-improving procedure. In contrast to the previous EFNQMC method, the trial function is optimized synchronistically in the diffusion procedure, but not before the beginning of EFNQMC computation. In order to optimize the trial function, the improved steepest descent technique is used, in which the step size is automatically adjustable. The procedure is quasi-Newton and converges super linearly. We also use a novel trial function, which has correct electron-electron and electron-nucleus cusp conditions. The novel EFNQMC algorithm and the novel trial function are employed to calculate the energies of 11 A1 state of CH2, 1Ag state of C8 and the ground-states of H2, LiH, Li2, H2O, respectively. The test results show that both the novel algorithm and the trial function proposed in the present paper are very excellent.

Constructing oxygen deficiency-rich V_(2)O_(3)@PEDOT cathode for high-performance aqueous zinc-ion batteries

Aqueous zinc-ion batteries(AZIBs)have attracted widespread attention due to the advantages of high safety and environmental friendliness.Although V_(2)O_(3) is a promising cathode,the strong electrostatic interaction between Zn^(2+) and V_(2)O_(3) crystal,and the sluggish reaction kinetics still limit their application in AZIBs.Herein,the oxygen defects rich V_(2)O_(3) with conducive poly(3,4-ethylenedioxythiophene)(PEDOT)shell(V_(2)O_(3)-Od@PEDOT)was fabricated for AZIBs by combining the sulfur-assisted thermal reduction and in-situ polymerization method.The introduced oxygen vacancies of V_(2)O_(3)–Od@PEDOT weaken the electrostatic interaction between Zn^(2+) and the host material,improving the interfacial electron transport,while the PEDOT coating enhances the structural stability and conductivity of V_(2)O_(3),thus accelerating the reaction kinetics.Based on the advantages,V_(2)O_(3)–Od@PEDOT electrode delivers a reversible capacity of 495 mAh·g^(−1) at 0.1 A·g^(−1),good rate capability(189 mAh·g^(−1)at 8.0 A·g^(−1)),and an impressive cycling stability with 90.1%capacity retention over 1000 cycles at 8.0 A·g^(−1).The strategy may provide a path for exploiting the other materials for high performance AZIBs.

A self-optimizing QoS-aware service composition approach in a context sensitive environment

QoS-aware service composition is aimed to maximize the global QoS of a composite service when selecting candidate services.In a context sensitive service execution environment in pervasive computing,the context information for service composition is not static:device,policy,and user constraints,and QoS requirements may change,new services may be deployed,old ones withdrawn,or existing ones change their QoS parameters.This results in the current service composition plan failing or its QoS degrading from the optimum.In this paper,a runtime self-optimizing service composition framework is proposed.An implementation of a prototype for this framework is presented,addressing the issues of reducing extra delay while increasing global QoS in service composition in a dynamic context environment.Three service re-plan algorithms are compared that can be used in dynamic context environment,i.e.,minimal-conflict hill-climbing repair genetic algorithm(MCHC-repair GA),an improved penalty-based GA,and our multi-population conflicts sorted repair genetic algorithm(MP-CS-repair GA),as well as three kinds of service composition mechanisms-with backup,without backup,and our context-aware service re-selection mechanisms.The results show that our MP-CS-repair GA and context-aware service re-selection method can reduce more extra delay while acquiring a higher global QoS for the composite service in a context sensitive environment.This context-aware service re-selection mechanism also shows some adaptability to different context change frequencies and user requirements for reducing computation cost in the self-optimizing process.

Exact Fixed-node Quantum Monte Carlo: Self-optimizing Procedure

In this paper, a novel exact fixed-node quantum Monte Carlo (EFNQMC) algorithm was proposed, which is a self-optimizing and self-improving procedure. In contrast to the previous EFNQMC method, the importance function of this method is optimized synchronistically in the diffusion procedure, but not before beginning the EFNQMC computation. In order to optimize the importance function, the improved steepest descent technique is used, in which the step size is automatically adjustable. The procedure is quasi-Newton type and converges super linearly. The present method also uses a novel trial function, which has correct electron-electron and electron-nucleus cusp conditions. The novel EFNQMC algorithm and the novel trial function are employed to calculate the energies of 1 1A 1 state of CH 2, 1A g state of C 8 and the ground-states of H 2, LiH, Li 2 and H 2O.

Coordinated Damping Optimization Control of Sub-synchronous Oscillation for DFIG and SVG

Currently, the power electronics-based devices, includinglarge-scale non-synchronized generators and reactivepower compensators, are widely used in power grids. This helpsintroduce the coupling interactions between the devices andthe power grid, resulting in a new sub-synchronous oscillationphenomenon. It is a critical element for the stability operation ofthe power grid and its devices. In this paper, the sub-synchronousoscillation phenomenon of the power grid connected with largescalewind power generation is analyzed in detail. Then, inorder to damp the sub-synchronous oscillation, a coordinateddamping optimization control strategy for wind power generatorsand their reactive power compensators is proposed. The proposedcoordinated control strategy tracks the sub-synchronousoscillation current signal to correct the corresponding controlsignal, which increases the damping of power electronics. Theresponse characteristics of the proposed control strategy areanalyzed, and a self-optimization parameter tuning method basedon sensitivity analysis is proposed. The simulation results validatethe effectiveness and the availability of the proposed controlstrategy.