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Due to high computation complexity, traditional design methods for LLC converter usually consider limited kinds of performance, and the process for searching the optimal parameter scheme is discrete, which might cause the missing of real optimal solution. In order to solve these problems, a multi‐objectives optimization design method for the LLC converter is proposed in this paper. According to the numerical calculation method, a closed parameter space for resonant parameters is established under the constraints of operation mode, switching frequency, zero voltage switching (ZVS), and voltage stress of resonant capacitor. Besides, the root‐mean‐square (RMS) value of resonant current and secondary current, as well as the core loss of the transformer, are selected as the optimization objectives. Non‐dominated sorting genetic algorithm (NSGA‐II) is utilized to deal with these multi‐objective formulations. In order to reduce the computation complexity, a data‐driven method, called adaptive polynomial approximation (APA), is selected to obtain the explicit expressions of the parameter space and the optimization objectives. Then, by substituting this simplified surrogate model into the NSGA‐II algorithm, the optimal parameter scheme is obtained. The comprehensive comparison analysis is performed, and a 400 W/48 V experimental prototype was built to verify the theoretical analysis, which shows that the proposed method features the highest efficiency of 95%. [ABSTRACT FROM AUTHOR]

The rational design of efficient catalysts for uric acid (UA) electrooxidation, as well as the establishment of structure‐activity relationships, remains a critical bottleneck in the field of electrochemical sensing. To address these challenges, herein, a hybrid catalyst that integrates carbon‐supported Pt nanoparticles and nitrogen‐coordinated Mn single atoms (PtNPs/MnNC) is developed. The metal‐metal interaction during annealing affords the construction of metallic‐bonded Pt−Mn pairs between PtNPs and Mn single atoms, facilitating the electron transfer from PtNPs to the support and thereby optimizing the electronic structure of catalysts. More importantly, experiments and theoretical calculations provide visual proof for the 'incipient hydrous oxide adatom mediator' mechanism for UA oxidation. The Pt−Mn pairs first adsorb OH* to construct the bridged Pt−OH−Mn mediators to serve as a highly active intermediate for N−H bond dissociation and proton transfer. Benefiting from the unique electronic and geometric structure of the catalytic center and reactive intermediates, PtNPs/MnNC exhibits superior electrooxidation performance. The electrochemical sensor based on PtNPs/MnNC enables sensitive detection and discrimination of UA and dopamine in serum samples. This work offers new insights into the construction of novel electrocatalysts for sensitive sensing platforms. [ABSTRACT FROM AUTHOR]

Mask-free multi-photon lithography enables the fabrication of arbitrary nanostructures low cost and more accessible than conventional lithography. A major challenge for multi-photon lithography is to achieve ultra-high precision and desirable lateral resolution due to the inevitable optical diffraction barrier and proximity effect. Here, we show a strategy, light and matter co-confined multi-photon lithography, to overcome the issues via combining photo-inhibition and chemical quenchers. We deeply explore the quenching mechanism and photoinhibition mechanism for light and matter co-confined multiphoton lithography. Besides, mathematical modeling helps us better understand that the synergy of quencher and photo-inhibition can gain a narrowest distribution of free radicals. By using light and matter co-confined multiphoton lithography, we gain a 30 nm critical dimension and 100 nm lateral resolution, which further decrease the gap with conventional lithography. Mask-free multi-photon lithography allows the straightforward fabrication of nanostructures, but high precision and good resolution can be challenging to achieve. Here, the authors report a combination of photo-inhibition and chemical quenchers for improved lithography performance. [ABSTRACT FROM AUTHOR]

Engineering isolated metal sites resembling the primary coordination sphere of metallocofactors enables atomically dispersed materials as promising nanozymes. However, most existing nanozymes primarily focus on replicating specific metallocofactors while neglecting other supporting cofactors within active pockets, leading to reduced electron transfer (ET) efficiency and thus inferior catalytic performances. Herein, we report a metal–organic framework UiO‐67 nanozyme with atomically dispersed iron sites, which involves multiple tailored enzyme‐like nanocofactors that synergistically drive the ET process for enhanced peroxidase‐like catalysis. Among them, the linker‐coupled atomic iron site plays a critical role in substrate activation, while bare linkers and zirconia nodes facilitate the ET efficiency of intermediates. The synergy of three nanocofactors results in a 4.29‐fold enhancement compared with the single effort of isolated metal site‐based nanocofactor, holding promise in immunoassay for sensitive detection of chlorpyrifos. This finding opens a new way for designing high‐performance nanozymes by harmonizing various nanocofactors at the atomic and molecular scale. [ABSTRACT FROM AUTHOR]

As the most fundamental unit of photonic integration, optical waveguides offer the possibility of developing efficient and practical photonic chips by facilitating the on‐chip integration of devices with different functions. Femtosecond direct laser writing (FsDLW), as a burgeoning 3D microfabrication technology, can realize the rapid formation of arbitrary optical waveguides without any mask inside versatile materials, such as crystalline dielectric crystals, glasses, polymers, and photoresists. This significant material‐independence allows FsDLW to combine different materials and manufacturing processes to implement a cross‐platform solution. This review first describes the different optical loss types and suitable measurement methods for different applicable scenes, then summarizes the two fabrication mechanisms and points out the general direction for adjusting the waveguide properties by fabrication processes. Finally, the application fields and development prospects of different materials are discussed by overviewing the characteristics of different materials and the material selection preferences of different devices. With a panoramic view of the development, it is concluded with insights and perspectives of the future development of optical waveguides and processing technology via FsDLW. [ABSTRACT FROM AUTHOR]

Constructing a renewable energy-based power system has become an important development path for the power industry's low-carbon transformation. However, as the proportion of renewable energy generation (REG) increases, the power grid gradually changes to uncertainty. Technologies to address this issue have been introduced. However, the majority of existing reviews focus on specific uncertainty modeling approaches and applications, lacking the consideration of temporal and spatial interdependence. Therefore, this paper provides a comprehensive review of the uncertainty modeling of temporal and spatial interdependence. It includes the discrete and continuous stochastic process-based methods to address temporal interdependence, the correlation coefficient and copula functions in modeling spatial interdependence, and the Itô process and random fields theory to describe temporal and spatial interdependence. Finally, their applications in power system stability, control, and economic scheduling are summarized. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is a crucial way to achieve the goal of green and sustainable development. To accomplish this goal, electric vehicles (EVs) are considered system‐schedulable energy storage devices, suppressing the negative impact of the randomness and fluctuation of renewable energy on the system's operation. In this paper, a coordination control strategy aimed at minimising the carbon emissions of a distribution network between EVs, energy storage devices, and static var compensators (SVCs) is proposed. A model‐free deep reinforcement learning (DRL)‐based approach is developed to learn the optimal control strategy with the constraint of avoiding system overload caused by random EV access. The twin‐delayed deep deterministic policy gradient (TD3) framework is applied to design the learning method. After the model learning is completed, the neural network can quickly generate a real‐time low‐carbon scheduling strategy according to the system operating situation. Finally, simulation on the IEEE 33‐bus system verifies the effectiveness and robustness of this method. On the premise of meeting the charging demand of electric vehicles, this method can optimise the system operation by controlling the charge‐discharge process of EVs, effectively absorbing the renewable energy in the system and reducing the carbon emissions of the system operation. [ABSTRACT FROM AUTHOR]

Future power systems will face more extreme operating condition scenarios, and system emergency dispatch will face more severe challenges. The use of distributed control is a well-designed way to handle this. It enables multi-energy complementation by means of autonomous communication, which greatly improves the flexibility of the grid. First, in the context of global energy conservation and emission reduction, this paper adopts the energy usage method of "renewable energy is the main source of energy, supplemented by thermal power and energy storage" to reduce the system abandoned wind (light) rate while supplementing the energy storage capacity. Second, a consensus algorithm is added to the system while considering the coordination between thermal units and energy storage. An "interface" for autonomous communication between thermal units and energy storage is created using the incremental cost of each agent. To address the recurring issue of power imbalance during emergency dispatch of the system, the consensus algorithm is enhanced so that the communication interval varies with the unit rate. This is based on the climbing characteristics of each thermal power unit. Finally, the effectiveness of the proposed method is verified in an IEEE-30 bus system. [ABSTRACT FROM AUTHOR]

Dynamic power control of wind farms (WFs) is necessary to provide automatic generation control (AGC) services for the power system. However, cooperative WF control for AGC remains a great challenge because of the nonlinear and high-dimensional nature of the wake flow dynamics. To address this challenge, this article proposes a model predictive control (MPC) framework with deep learning-based reduced-order modeling (ROM). Two novel neural network architectures are designed, which successfully formulate a WF ROM capturing the dominant wake steering dynamics in a computationally efficient manner. Compared to physical models, the data-driven ROM reduces the number of model states by orders of magnitude. Then, a novel WF AGC framework embedding the derived WF ROM is proposed. Thrust coefficient and yaw steering are both employed to optimize WF power tracking performance. Compared to prior WF AGC controllers, the dynamic yaw actuation is first optimized for AGC considering the wake steering dynamics. Case studies validate the effectiveness of the deep learning-based WF ROM at capturing the wake traveling dynamics. The WF controllers were stress-tested under time-varying inflow directions. The proposed MPC can react to different wind directions and generates higher-quality control performance than existing alternatives with extended trackable AGC range and better dynamic power tracking performance. [ABSTRACT FROM AUTHOR]

The loss of small signal stability under parameter variation (e.g., fluctuation of loads and wind powers) can be ascribed to local bifurcations, i.e., saddle-node, Hopf, singularity-induced, and limit-induced bifurcations. Classic bifurcation calculation methods like the direct method and continuation method can only provide a bifurcation point or two-parameter bifurcation curve. Based on Galerkin method and the implicit function theorem, this paper proposes accurate polynomial approximations of bifurcation hypersurfaces in the multi-dimensional parameter space of interest. The proposed method can ensure high accuracy in this parameter space, and thus is preferable to the existing Taylor expansion-based local approximation method, given the intrinsic large-variation characteristic of wind powers. Acquired bifurcation hypersurfaces are immediately used to construct the small signal stability region of the continuous system, and then compute the stability margin for the operating point subjected to uncertainty of wind generation. Besides, the validity scope of the proposed method is analyzed, and the handling of limit-triggered equation switching as well as its computational difficulty is discussed. Computational results on the two-parameter 11-bus two-area and six-parameter IEEE 145-bus test systems validate the high accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Prognostic is a potential tool for improving the durability of solid oxide fuel cells (SOFCs), which usually involves building a degradation model for prediction. However, the existing degradation models based on parallel constant operation datasets are inaccurate for integration with operation optimization and control problems of SOFCs under varying-load operation due to the nonuniform degradation behaviors. To address this issue, a link function degradation model is proposed, and its parameters are identified online with a cyclic batch identification procedure based on the maximum likelihood method, which provides results representing the degradation trend on a timescale of 103 h. The link function takes the form of an empirical function, which describes how operating parameters affect the degradation and is easy to integrate with control designs. The existence of the link function is proven on the varying-load experiment datasets of two flat-chip SOFCs because it statistically improves the prediction accuracy and stability compared with a constant degradation speed model. Furthermore, the effectiveness of the proposed identification procedure for time-varying degradation behaviors on the timescale of 104 h is also validated with 30,000-h simulation datasets. • Accurate degradation models are built, considering degradation nonuniformity. • The model form is preferred by operation control/optimization applications. • 1700-h varying-load degradation experiment datasets of two cells are obtained. • The existence of the link function is proved on the experiment datasets. • The modeling procedure adapts to time-varying degradation characteristics. [ABSTRACT FROM AUTHOR]

Reflecting the uncertainty of renewable energy generations and loads, power system AGC signals are essentially random processes. For the sake of the optimal operation and control of the AGC participant, such as energy storage systems (ESSs) in a performance/mileage-based regulation market, taking the uncertain nature, especially the temporal correlation, of the AGC signals into consideration can be beneficial; hence, random process models of the AGC signals are needed. However, a continuous random process model of the AGC signal that jointly considers the probability distribution and the temporal correlation is still lacking. To fill this gap, this paper first presents a systematic methodology for modeling the continuous random processes of AGC signals based on stochastic differential equations (SDEs). It is shown that AGC signals may have a saturated stationary probability density function and a biexponential temporal correlation, which are very different from the renewable generations. To capture these special characteristics, SDEs are then carefully constructed, which are easy to use in optimization and control. Using the PJM traditional and dynamic regulation (RegD and RegA) signals and the signal received from a battery ESS (BESS) plant in Jiangsu, China for example, simulation shows that the SDE is able to simultaneously capture the probability distribution and temporal correlation accurately. [ABSTRACT FROM AUTHOR]

• CNTs and N-doped carbon decoration can enhance electronic conductivity of Si-based anode. • CoSi 2 can improve the structural stability of Si-based anode. • The introduced Co can catalyze the graphitization of carbon shell. • The designed Si-based anode delivers enhanced electrochemical properties. [Display omitted] Silicon is evaluated as a competitive lithium-ion batteries anode material by virtue of its ultrahigh specific capacity and relatively low discharge potential. However, the large volume change during lithiation/delithiation processes, and the low intrinsic electric conductivity have seriously impeded its widespread practical application. Herein, silicon nanoparticles (SiNPs) coated with CNTs-intertwined N-doped porous carbon (NPC) are prepared via a facile solution-phase method, followed by thermal annealing treatment. In this hierarchical structure, the bimetallic ZIF-derived porous carbon can shorten the migration channels of Li+ and electrons. Moreover, the formed Co nanoparticles can not only catalyze the graphitization of carbon, but also propel in-situ growth of CNTs to constitute a 3D interconnected conductive network, thereby improving the electrochemical performance. Benefiting from the unique structure, the as-prepared Si@CoSi 2 /Co-NPC@CNTs electrode exhibits superior electrochemical performance of 1191 mAh g−1 at 0.5 A g−1 after 200 cycles. Meanwhile, the electrode also shows excellent rate capability of 930 mAh g−1 even at a high current density of 6 A g−1. This work provides a new strategy to design exceptionally high electrical conductivity and structure stability of Si-based anode materials. [ABSTRACT FROM AUTHOR]

The wake effect is one of the leading causes of energy losses in offshore wind farms (WFs). Both turbine placement and cooperative control can influence the wake interactions inside the WF and thus the overall WF power production. Traditionally, greedy control strategy is assumed in the layout design phase. To exploit the potential synergy between the WF layout and control so that a system-level optimal layout can be obtained with the greatest energy yields, the layout optimization should be performed with cooperative control considerations. For this purpose, a novel two-stage WF layout optimization model is developed in this paper. Cooperative WF control of both turbine yaw and axis-induction are considered in the WF layout design. However, the integration of WF control makes the layout optimization much more complicated and results in a large-scale nonconvex problem, hindering the application of current layout optimization methods. To increase the computational efficiency, we leverage the hierarchy and decomposability of the joint optimization problem and design a decomposition-based hybrid method (DBHM). Instead of manipulating all the variables simultaneously, the joint optimization problem is decomposed into several subproblems and their coordination. Case studies are carried out on different WFs. It is shown that WF layouts with higher energy yields can be obtained by the proposed joint optimization compared to traditional separate layout optimization. Moreover, the computational advantages of the proposed DBHM on the considered joint layout optimization problem are also demonstrated. [ABSTRACT FROM AUTHOR]

To provide automatic generation control (AGC) service, wind farms (WFs) are required to control their operation dynamically to track the time-varying power reference. Wake effects impose significant aerodynamic interactions among turbines, which remarkably influence the WF dynamic power production. The nonlinear and high-dimensional nature of dynamic wake model, however, brings extremely high computation complexity and obscure the design of WF controllers. This paper overcomes the control difficulty brought by the dynamic wake model by proposing a novel control-oriented reduced order WF model and a deep-learning-aided model predictive control (MPC) method. Leveraging recent advances in computational fluid dynamics (CFD) to provide high-fidelity data that simulates WF dynamic wake flows, two novel deep neural network (DNN) architectures are specially designed to learn a dynamic WF reduced-order model (ROM) that can capture the dominant flow dynamics. Then, a novel MPC framework is constructed that explicitly incorporates the obtained WF ROM to coordinate different turbines while considering dynamic wake interactions. The proposed WF ROM and the control method are evaluated in a widely-accepted high-dimensional dynamic WF simulator whose accuracy has been validated by realistic measurement data. A 9-turbine WF case and a larger 25-turbine WF case are studied. By reducing WF model states by many orders of magnitude, the computational burden of the control method is reduced greatly. Besides, through the proposed method, the range of AGC signals that can be tracked by the WF in the dynamic operation is extended compared with the existing greedy controller. • A novel control-oriented dynamic wind farm model is developed based on deep learning. • The model reduction considers both the modeling accuracy and control compatibility. • The dynamic wake interactions and wake losses are alleviated by the proposed MPC. • The trackable AGC signal range in the wind farm dynamic operation is increased. • The computation burden of the optimal control is practical for online applications. [ABSTRACT FROM AUTHOR]

Alkaline electrolysis (AEL) is the most mature electrolysis technology and is widely used in large-scale power-to-gas scenarios. However, the loading range of AEL system is restricted by the minimum load, 10%–40% in general, which prevents system operation during low-load periods. This minimum load is caused by the flammable mixture formed by the impurity crossover and is related to operation conditions such as current and pressure. In this paper, we propose a novel pressure control strategy to extend the loading range of an AEL system by reducing the system pressure at low-load periods while also maximizing the system efficiency. A dynamic impurity accumulation model is established illustrating the flexibility limitation mechanism of the AEL system. Two pressure controllers are designed based on the steady-state and dynamic impurity accumulation model, including an operation curve tracking controller (OCT) and a model predictive controller (MPC). In the case study, both controllers achieve satisfying results under the peak shaving scenario, with an extension of the AEL system's minimum load from 27.5% by the traditional constant pressure controller to 10.5% by the OCT controller and 10% by the MPC controller. This pressure control strategy reduces the electricity abandonment and improves the system economy significantly. • Establish a dynamic impurity accumulation model for gas crossover. • Propose a novel pressure control strategy to extend the loading range. • Design two pressure controllers: an OCT controller and an MPC controller. • Improve the system economy by reducing the electricity abandonment. [ABSTRACT FROM AUTHOR]

Natural phosphatases featuring paired metal sites inspire various advanced nanozymes with phosphatase-like activity as alternatives in practical applications. Numerous efforts to create point defects show limited metal site pairs, further resulting in insufficient activity. However, it remains a grand challenge to accurately engineer abundant metal site pairs in nanozymes. Herein, we report a grain-boundary-rich ceria metallene nanozyme (GB-CeO 2 ) with phosphatase-like activity. Grain boundaries acting as the line or interfacial defects can effectively increase the content of Ce 4+ /Ce 3+ site pairs to 72.28%, achieving a 49.28-fold enhancement in activity. Furthermore, abundant grain boundaries optimize the band structure to assist the photoelectron transfer under irradiation, which further increases the content of metal site pairs to 88.96% and finally realizes a 114.39-fold enhanced activity over that of CeO 2 without irradiation. Given the different inhibition effects of pesticides on catalysts with and without irradiation, GB-CeO 2 was successfully applied to recognize mixed toxic pesticides.

The rational design of efficient catalysts for uric acid (UA) electrooxidation, as well as the establishment of structure-activity relationships, remains a critical bottleneck in the field of electrochemical sensing. To address these challenges, herein, a hybrid catalyst that integrates carbon-supported Pt nanoparticles and nitrogen-coordinated Mn single atoms (Pt NPs /MnNC) is developed. The metal-metal interaction during annealing affords the construction of metallic-bonded Pt-Mn pairs between Pt NPs and Mn single atoms, facilitating the electron transfer from Pt NPs to the support and thereby optimizing the electronic structure of catalysts. More importantly, experiments and theoretical calculations provide visual proof for the 'incipient hydrous oxide adatom mediator' mechanism for UA oxidation. The Pt-Mn pairs first adsorb OH* to construct the bridged Pt-OH-Mn mediators to serve as a highly active intermediate for N-H bond dissociation and proton transfer. Benefiting from the unique electronic and geometric structure of the catalytic center and reactive intermediates, Pt NPs /MnNC exhibits superior electrooxidation performance. The electrochemical sensor based on Pt NPs /MnNC enables sensitive detection and discrimination of UA and dopamine in serum samples. This work offers new insights into the construction of novel electrocatalysts for sensitive sensing platforms., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

FeNC catalysts are considered one of the most promising alternatives to platinum group metals for the oxygen reduction reaction (ORR). Despite the extensive research on improving ORR activity, the undesirable durability of FeNC is still a critical issue for its practical application. Herein, inspired by the antioxidant mechanism of natural enzymes, CeO 2 nanozymes featuring catalase-like and superoxide dismutase-like activities were coupled with FeNC to mitigate the attack of reactive oxygen species (ROS) for improving durability. Benefiting from the multienzyme-like activities of CeO 2 , ROS generated from FeNC is instantaneously eliminated to alleviate the corrosion of carbon and demetallization of metal sites. Consequently, FeNC/CeO 2 exhibits better ORR durability with a decay of only 5 mV compared to FeNC (18 mV) in neutral electrolyte after 10k cycles. The FeNC/CeO 2 -based zinc-air battery also shows minimal voltage decay over 140 h in galvanostatic discharge-charge cycling tests, outperforming FeNC and commercial Pt/C.

Engineering isolated metal sites resembling the primary coordination sphere of metallocofactors enables atomically dispersed materials as promising nanozymes. However, most existing nanozymes primarily focus on replicating specific metallocofactors while neglecting other supporting cofactors within active pockets, leading to reduced electron transfer (ET) efficiency and thus inferior catalytic performances. Herein, we report a metal-organic framework UiO-67 nanozyme with atomically dispersed iron sites, which involves multiple tailored enzyme-like nanocofactors that synergistically drive the ET process for enhanced peroxidase-like catalysis. Among them, the linker-coupled atomic iron site plays a critical role in substrate activation, while bare linkers and zirconia nodes facilitate the ET efficiency of intermediates. The synergy of three nanocofactors results in a 4.29-fold enhancement compared with the single effort of isolated metal site-based nanocofactor, holding promise in immunoassay for sensitive detection of chlorpyrifos. This finding opens a new way for designing high-performance nanozymes by harmonizing various nanocofactors at the atomic and molecular scale., (© 2024 Wiley-VCH GmbH.)

Photoinhibition (PI) mechanisms have been introduced in nanofabrication which allows breaking the diffraction limit by large factors. Donut-shaped laser is usually selected as a depletion beam to reduce linewidth, but the parasitic process has made the results of the experiment less than expected. As a result, the linewidth is difficult to achieve below 50 nm with 780 nm femtosecond and 532 nm continuous-wave lasers. Here, we propose a new, to the best of our knowledge, method based on a center-non-zero (CNZ) depletion laser to further reduce linewidth. By constructing a smaller zone of action under the condition of keeping the maximum depletion intensity constant, a minimum linewidth of 30 nm (λ / 26) was achieved. Two ways to construct CNZ spots were discussed and experimented, and the results show the advantages of our method to reduce the parasitic process to further improve the writing resolution.