Search

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]

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]

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]

To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run‐of‐the‐river hydropower and volatile energies such as photovoltaic (pv) and wind are built around the world. In islanded operation, the primary and secondary frequency controls, that are hydro governors and automatic generation control (AGC), are responsible for the frequency stability. However, due to limited water storage capacity of run‐of‐the‐river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC cannot fully exploit the adjustability of hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. To unify the multi‐timescale dynamics of the power system and river dynamics to establish a controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge is approximated by a polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding‐horizon manner. Simulation of a real‐life system shows the proposed method significantly reduces load loss under pv volatility. [ABSTRACT FROM AUTHOR]

Wake effects impose significant aerodynamic interactions among wind turbines. To improve the wind farm operating performance, practical wind farm online control considering wake effects becomes very important. To achieve online optimal wind farm control while responding to grid demands, this paper proposes a novel optimal wind farm supervisory control (SC) model and its explicit solutions. From the controller modelling perspective, the two major wind farm operating modes, the maximum power point tracking mode and the set‐point tracking mode, are first analysed and unified in one optimisation model while considering wake effects. In this way, wind farm power production and rotor kinetic energy reserve can be simultaneously considered to conveniently modify the operation mode in response to different grid demands. Aside from controller modelling, the collocation method is first introduced to address the online application problem of such wake‐effect aware optimal WF control. Although a few optimisation algorithms have been proposed to find the optimum offline, online optimal control is still challenging because of the computational complexity brought by wake model non‐linearity and non‐convexity. The proposed collocation method explicitly approximates the optimal solutions to the proposed supervisory control model, through which only a direct algebraic operation is required for online optimal control instead of repeated optimisations. Case studies are carried out on different wind farms under various wind conditions, showing that the wind farm power production potential and releasable power reserve are improved compared to traditional greedy control in both modes. The accuracy of the collocation method is verified. A detailed analysis of the wind farm production capacity under different wind speeds and directions is also provided. [ABSTRACT FROM AUTHOR]

Transition metal oxides (TMOs) are promising materials for next‐generation lithium‐ion batteries while the lithium storage and cycling stability of TMOs are still limited. Herein, a novel Co3O4/MnO2/C composite using metal‐organic frameworks (MOFs) as sacrificial templates is reported. MnO2 and C shells are layer by layer coated on the MOF‐derived Co3O4/C samples via hydrothermal technique and heat treatment. When employed as anode materials for LIBs, the hierarchically heterostructured Co3O4/MnO2/C composites incorporate the merits of Co3O4, MnO2, and C, showing high Li storage performances at a current density of 0.2 A g−1 with an initial capacity of ∼1245.7 mAh g−1 and a reversible capacity of ∼854.9 mAh g−1 after 100 cycles, remarkable cycling stability, and excellent rate capability (almost 98 % recovery as the current density returns to the initial 0.2 A g−1, and especially, the specific capacity remains ∼587.6 mAh g−1 at a high current density of 2.0 A g−1). [ABSTRACT FROM AUTHOR]

To solve the problem associated with the huge volume change and low conductivity of Mn3O4, a novel C/Mn3O4 microsphere with Mn3O4 layer and void space sandwiched by nitrogen‐doped carbon (NC) is designed and fabricated. In this composite, the NC is obtained from the metal–organic frameworks (MOFs) and polydopamine (PDA). The preparation process is facile and effective without additional templates or redox solvents introduced. When used as anodes for lithium‐ion batteries (LIBs), the C/Mn3O4 composite incorporates the metrics of N‐doping, yolk‐shell structure, double carbon layer, mesoporous Mn3O4, showing an outstanding lithium storage of ≈1015.9 mAh g−1 at 0.2 A g−1 in the 250th cycle, high cycling stability (without clear capacity decay after 400 cycles), and excellent rate capability (capacity can fully recover as the current density returns back to the initial value of 0.2 A g−1). The yolk‐shell structure derived from MOFs can be expanded to fabricate other materials with improved performance. [ABSTRACT FROM AUTHOR]

Platelet rich plasma (PRP) is the autologous plasma fraction with a platelet-rich cellular component which is enriched with a number of growth factors. Due to its availability and low cost, PRP has become an increasingly popular clinical tool as an alternative source of growth factors for various applications, for example, tendon regeneration but with limited success in clinical trials. The main objective of the current study was to determine whether activated PRP [i.e., platelet rich plasma-clot release (PRCR)] could be used to induce the proliferation and collagen synthesis in human tenocyte in vitro. The advantage of using PRCR is that the platelet-derived bioactive factors are more concentrated and could initiate a more rapid and accelerated healing response than PRP. Our results demonstrated that 10% PRCR treatment accelerated the extent of cell proliferation and collagen production by human tenocytes in vitro. The expression of specific tenocyte markers were similar to conventional fetal bovine serum (FBS)-treated tenocytes implanted in mice within 14 days of implantation in diffusion chambers. Moreover, relatively more collagen fibrils were evident in PRCR-treated tenocytes in vivo as compared to 10% FBS-treated cells. Overall, our feasibility study has indicated that PRCR can induce human tenocyte proliferation and collagen synthesis which could be implemented for future tendon regeneration in reconstructive surgeries. © 2011 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 30:982-990, 2012 [ABSTRACT FROM AUTHOR]