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• A method of promoting the axial feature size of PPI-DLW is proposed. • A 3D hollow focus was generated using one beam without light field superposition. • Two-focus parallel scanning using 3D hollow focus was realized without hardware updating based on a classic method. • The isotropic 30 nm feature size was realized. • This work will improve the ability of PPI-DLW to fabricate arbitrary 3D structures. Peripheral-photoinhibition (PPI)-based direct laser writing (DLW) inspired by simulated emission depletion (STED) microscopy has improved the fabrication resolution of three-dimensional (3D) structures on the nano-scale. However, most previous studies have focused on the lateral feature size, neglecting axial-feature-size enhancement. This article proposes a novel method to simultaneously reduce the axial and lateral feature sizes of PPI-DLW. By employing a pseudo 3D hollow focus, a suspended nanowire with an isotropic 30-nm feature size was realized. Such an isotropic 3D hollow focus for photoinhibition is generated using a single beam instead of two-beam superposition, thus it does not increase the system complexity of a PPI-DIW system. Owing to the reduced feature size in all three dimensions and the low systematic complexity, the proposed method improves the capabilities of PPI-DLW for fabricating arbitrary 3D structures. [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]

Flexible power-to-hydrogen (P2H) production enables the admittance of renewable energies on a utility scale and provides the connected electrical power system with ancillary regulatory services. To extend the flexibility and thus improve the profitability of green hydrogen production, this paper presents an optimal production scheduling approach for utility-scale P2H plants composed of multiple alkaline electrolyzers. Unlike existing works, this work discards the conservative constant steady-state constraints and first leverages the dynamic thermal and hydrogen-to-oxygen (HTO) impurity crossover processes of the electrolyzers to improve flexibility. Doing this optimizes their effects on the loading range and energy conversion efficiency. The proposed multiphysics-aware scheduling model is formulated as mixed-integer linear programming (MILP). It coordinates the electrolyzers' operation state transitions and load allocation subject to comprehensive thermodynamic and mass transfer constraints. A decomposition-based solution method, SDM-GS-ALM, is adopted to address the scalability issue for scheduling large-scale P2H plants composed of tens of electrolyzers. With an experiment-verified dynamic electrolyzer model, case studies show that the proposed method remarkably improves the hydrogen output and profit of P2H production powered by either solar or wind energy compared to the existing scheduling approach. [ABSTRACT FROM AUTHOR]

Surrogate models that predict the behaviors of solid oxide cells (SOCs) accurately at low computational cost are crucial to the control and optimization of SOC plants. Lumped physical models of SOCs, while widely used in such applications, lack accuracy because of neglected physical details. Data-driven models are the other options of surrogate models, which are proved to be more accurate because these models are identified directly from experiments or numerical simulations. However, due to the time cost of experiments and numerical simulations of SOCs, it is hoped that data-driven models can be constructed from a minimum amount of data. Also, the trained data-driven models should be robust, in other words, insensitive to the data set as well as the initial settings. These requirements are hard to be achieved by existing data-driven models of SOCs, such as lookup tables and artificial neural networks (ANNs). Aiming to preserve robustness and reduce the required amount of data, this paper introduces an adaptive polynomial approximation (APA) method, which is derived from the latest findings of numerical computation science, to the surrogate modeling of SOCs. The obtained models by the APA method are validated by both experiments and simulations. By analyzing the models, the coupling relationship among operating parameters of SOCs is revealed. The physical interpretability makes the APA method distinctive from common data-driven modeling methods. Performance comparison shows that the APA method is more accurate and robust than the existing ones with similar sampling costs. Additionally, the APA method can control the accuracy of the model by setting an error criterion in the algorithm iteration, endowing the APA method with an error control ability as per different accuracy requirements for SOC modeling. • A data-driven method to construct surrogate models of SOC is introduced. • The models built by this method are validated by both experiments and simulations. • The models built by this method are physically interpretable. • The accuracy of this method is directly controlled by an error control parameter. • With similar time cost, this method is more robust and accurate than existing ones. [ABSTRACT FROM AUTHOR]

To exploit the massive solar energy available in the region, photovoltaic plants have been built in the mountain areas in Southwest China, coexisting with many small cascaded run-of-the-river hydropower plants, interconnected by short-distance transmission lines. Network constraints inside these systems are often negligible. However, due to the volatile nature of solar power and the weak connection to the external power grid, without proper coordination, solar power curtailment and water spillage are inevitable. To address this issue, this paper proposes an ultra-short-term stochastic generation control method for cascaded hydropower to mitigate solar power volatility. Two technical challenges are specifically addressed: to tackle the time-varying stochastic volatility of solar power, the Itô process model is introduced; to characterize the spatial-temporal hydraulic coupling of the cascaded hydropower plants and river operation constraints, a state-space dynamic river model derived from shallow water equations is included. Followingly, a stochastic control (SC) approach for hydropower generation based on stochastic programming is proposed, where hydropower generation commands are parameterized as affine functions of measured solar power, enabling quick response to solar power volatility. Simulation of a real-life cascaded hydro-solar system and a modified system verifies the proposed control method. [ABSTRACT FROM AUTHOR]

The imbalance of generation and load caused by the increasing integration of volatile generations poses challenges on frequency regulation. AGC is required to respond to the generation fluctuations without violating operational and security constraints. Explicit model predictive control (EMPC) provides an approach to reaching such requirements, which calculates the control laws of MPC in an explicit form, allowing for offline validation of the controller and enabling fast online computation. However, the partition number of EMPC's piecewise affine control laws grows exponentially with the number of constraints and prediction/control horizons, which hinders its application in large systems. In this paper, we propose an alternative explicit control approach for AGC by approximating the control laws of EMPC using Legendre polynomial series expansions, thus entirely eliminating the partition issue. The Galerkin method is applied to the KKT conditions of EMPC's multiparametric quadratic programming (mp-QP) problem to compute the approximation. Case studies in an illustrative system and IEEE 118-Bus System verify the performance and efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Power system static voltage stability region boundary (SVSRB) is a complicated hypersurface in parameter space. Due to inequality constraints such as generator reactive power limits, different critical points on the SVSRB may be related to different groups of active constraints and can be associated with different types of loadability limit, which makes it difficult to approximate the whole SVSRB at once or assemble many local approximations of the SVSRB. To overcome the difficulty, this paper proposes an approach for globally approximating the whole SVSRB considering generator reactive power limits at one go. First, a parametric optimization model incorporating all inequality constraints is formulated to describe the SVSRB, where the optimal solutions are the critical points on the SVSRB. The optimality condition, i.e., parametric Karush–Kuhn–Tucker conditions (KKT conditions), is also derived. Then, by applying the Galerkin method to the parametric KKT conditions in an interior-point-method-like manner, the whole SVSRB is approximated and represented using a single polynomial expression. Case studies in a 10-bus system and IEEE 300-bus system verify the effectiveness and accuracy of the proposed method. [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]

A novel method for globally approximating the static voltage stability region boundaries (SVSRBs) of power systems is proposed by applying parametric polynomial approximation to the criterion equation which defines the SVSRB. Known as the Galerkin method, the implicit function portraying the SVSRB is described as a linear combination of basis polynomial functions, and the coefficients of the basis functions are obtained by projecting the SVSRB criterion onto each basis function. The approximation guarantees high precision globally in the whole domain of the parameter of interest rather than only in the neighborhood of a point, and the error can be controlled by the degree of polynomial basis functions. In the meantime, analytical expression of the left or right eigenvector of the system's Jacobian matrix corresponding to the zero eigenvalue is obtained in the form of polynomial, which provides valuable information for online voltage stability control or monitoring. Case studies in a 10-bus test system and IEEE 118-bus test system verifies the validity, accuracy, and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

Ag 3 PO 4 possesses high photocatalytic activity under visible light, but its application is limited by photogenerated charges recombination, photocorrosion as well as consumption of noble Ag. It is of great interesting to develop new Ag 3 PO 4 -based photocatalysts with high charges separation efficiency, good stability and low content of Ag. In this paper, we report a novel Ag 3 PO 4 /TiO 2 /reduced graphene oxide (Ag 3 PO 4 /TiO 2 /rGO) photocatalyst. It exhibits advantages on both the microstructure and the charges separation. The microstructure shows that TiO 2 spheres of hundreds of nanometers in size are decorated with dense nano-sized Ag 3 PO 4 to form pinecone-liked particles, which are enwrapped by rGO sheets. This novel structure effectively prevents aggregation of nano-sized Ag 3 PO 4 , which not only suppresses the charges recombination in Ag 3 PO 4 but also significantly reduces the content of Ag. Ag 3 PO 4 /TiO 2 /rGO also favors separation of photogenerated charges owing to its two pathways for charges transportation, i.e., the electrons in Ag 3 PO 4 can be transferred to rGO, while the holes in Ag 3 PO 4 can be transferred to TiO 2 . The dual-pathway for charges separation as well as the pinecone-liked Ag 3 PO 4 /TiO 2 microstructure ultimately leads to enhanced photocatalytic activity and stability of Ag 3 PO 4 /TiO 2 /rGO. The photocatalytic performance varies with different contents of Ag 3 PO 4 in the composites, because low content of Ag 3 PO 4 induces weak light absorption while excess Ag 3 PO 4 results in serious charges recombination due to the aggregation of Ag 3 PO 4 nanoparticles. In this work, Ag 3 PO 4 /TiO 2 /rGO with weight ratio of Ag 3 PO 4 against TiO 2 /rGO equals to 0.6 exhibits the highest photocatalytic activity. The percentage of Ag in this composite is around 29 wt%, much lower than 77 wt% in pure Ag 3 PO 4 . [ABSTRACT FROM AUTHOR]

TiO 2 hollow spheres (THS) of 400 nm in diameter are mixed into TiO 2 nanoparticles film as light scattering centers to improve the light harvesting and photovoltaic performance of solar cells based on CdS quantum-dot sensitized TiO 2 (TiO 2 /CdS), N719 dye-sensitized TiO 2 (TiO 2 /N719) and CdS/N719 co-sensitized TiO 2 (TiO 2 /CdS/N719) photoanodes, respectively. The light harvesting and photovoltaic performance of the three types of solar cells are highly dependent on the THS content in TiO 2 films due to: (1) the significant light scattering among or within THS, (2) the extended pore size distribution, and (3) the modulated electron transport in the TiO 2 films. The optimal content of THS is around 10% by weight for TiO 2 /CdS and TiO 2 /CdS/N719 solar cells, and is around 15% for TiO 2 /N719 solar cell. It is suggested that, in TiO 2 /CdS and TiO 2 /CdS/N719 solar cells, high content THS can facilitate the aggregation of large CdS nanocrystals, which limits the injection of photo-generated electrons from CdS to TiO 2 due to serious internal recombination loss, and thus decreases the power conversion efficiency. The co-sensitized solar cell with the optimal THS content exhibits higher power conversion efficiency compared to the single-component sensitized solar cells. [ABSTRACT FROM AUTHOR]

We have established that human tenocytes can differentiate in the absence of exogenous fetal bovine serum (FBS) but in the presence of insulin-like growth factor-1 (IGF-1) and transforming growth factor-β3 (TGF-β3). The extent of tenocyte differentiation was assessed by examining cell survival, collagen synthesis, cell morphology and expression of tenocyte differentiation markers such as scleraxis (Scx), tenomodulin (Tnmd), collagen type I (Col-I) and decorin (Dcn). Our results indicate that 50 ng/ml IGF-1 and 10 ng/ml TGF-β3 (in the absence of FBS) were capable of maintaining in vitro human tenocyte survival in 14-day cultures. The extent of collagen synthesis and messenger ribonucleic acid expression of Scx, Tnmd, Col-I and Dcn were significantly upregulated in response to IGF-1 and TGF-β3. These findings have shown for the first time that human tenocytes can be maintained in long-term culture, in serum-free conditions, making this approach a suitable one for the purpose of tendon tissue engineering. Copyright © 2012 S. Karger AG, Basel [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]

• 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 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]

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]

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]

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]

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]

Utility-scale hydrogen production via alkaline electrolysis (AEL) is a promising pathway toward the decarbonization of the power, transportation, and chemical industries. The efficiency, load flexibility, and operational safety of the AEL system are subject to electrochemical, thermal, and mass transfer dynamics, and the corresponding parameters, including overvoltage coefficients, heat capacities and resistances of the stack and lye-gas separators, thickness and permeability of the diaphragm, etc. The community has developed many models to depict these dynamic behaviors. However, due to the lack of a comprehensive parameter estimation method, these models are generally tuned manually in industrial applications, which can be inaccurate and cannot fit their time-varying nature. To fill this gap, we present a fast and accurate parameter estimation method for the AEL system. Specifically, to address the difficulties of strong nonlinearity of the dynamic electrolyzer models and correlation between different parameters, a Bayesian inference-based Markov chain Monte Carlo method is proposed. To reduce the computing time for online estimation, data-driven adaptive polynomial surrogate models are established to replace repeated time-domain simulations of the electrolyzer model so that estimation can be finished within a few minutes. Experiments on a 5 Nm 3 /hr-rated AEL system validate the proposed method. Compared with the existing Kalman filter variants, the estimation error is reduced by at most 71.1% in terms of RMSE and NRMSE. In addition, the proposed method provides approaches to fault diagnosis and global sensitivity analysis for operating and designing AEL systems. • Dynamic parameters of the AEL system are estimated accurately and fast. • Strong nonlinearity and correlation of the parameters are overcome. • The proposed method is validated by experiments on a 5 Nm 3 /hr-rated electrolyzer. • Extended applications like fault diagnosis and sensitivity analysis are enabled. [ABSTRACT FROM AUTHOR]

Parametric problems in the transient analysis refer to the investigations of the relationship between parameters and dynamic behavior. To solve parametric problems, we propose a Galerkin method-based polynomial approximation. The proposed method uses a series of polynomials combined with coefficients to approximately represent state variables with parameters, where the Galerkin method is introduced to determine the coefficients. Thereby, the relationship between parameters and dynamic behavior is established through the polynomial expressions. Because models in power system transient analysis are a set of complicated differential-algebraic equations, practical issues related to the non-polynomial model terms, parameterization of initial operation point are discussed in this paper. The proposed method is tested on a three-machine system and the IEEE 145-bus test system. The case studies show that compared to trajectory sensitivities, the proposed method can maintain high accuracy in cases of large parameter variations and strong non-linearity. We also illustrate that the validity of the proposed method for parametric problems like trajectory approximation, finding the critical parameter values and uncertainty quantification. [ABSTRACT FROM AUTHOR]

Electrolysis systems use proportional–integral–derivative (PID) temperature controllers to maintain stack temperatures around set points. However, because of heat transfer delays in electrolysis systems, the manual tuning of PID temperature controllers is time-consuming, and temperature oscillations often occur. This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control. A thermal dynamic model of an electrolysis system is established in the frequency domain for controller designs. Based on this model, the temperature stability is analysed by the root distribution, and the PID parameters are optimized considering the temperature overshoot and the settling time. The performance of the optimal PID controllers is experimentally verified. Furthermore, the simulation results show that the before-stack temperature should be used as the feedback variable for small lab-scale systems to suppress stack temperature fluctuations, and the after-stack temperature should be used for larger systems to improve the economy. This study helps ensure the temperature stability and control of electrolysis systems. [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]

Three-dimensional (3D) graphene-based composites have drawn increasing attention in energy applications due to their unique structures and properties. However, the assembly of semiconductor nanoparticles (NPs) in 3D graphene structure is usually a complicated and time-consuming process. Here, we demonstrate a strategy of fast assembling Ag 3 PO 4 NPs within 3D graphene aerogels (GAs) with a facile in situ ion filtration-precipitation method, in which the sequential filtration of Ag + and PO 4 3− solutions through the porous 3D graphene structure enables the fast assembly of well-dispersed Ag 3 PO 4 NPs throughout the 3D GAs. The 3D GAs not only prevent the agglomeration of Ag 3 PO 4 NPs due to the porous and interconnected microstructure but also promote the separation of photo-generated charges in Ag 3 PO 4 due to the excellent electrical conductivity of reduced graphene oxide (rGO) that composes 3D GAs. As a result, the 3D Ag 3 PO 4 /GAs composite shows a considerable enhancement in both the activity and stability in photocatalytic oxygen evolution from water splitting when compared with pristine Ag 3 PO 4 . In addition to Ag 3 PO 4 NPs, the in situ ion filtration-precipitation method is also feasible for fast assembling other semiconductor NPs in 3D graphene structures [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]

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used in patients with injuries and inflammation of tendon and ligament, and as post-surgical analgesics. The aim of this study is to investigate the effect of indomethacin, a classic NSAID and its combinational effect with an anabolic agent of skeletal tissue, lactoferrin, on the proliferation and collagen formation of human tenocytes in vitro . A factorial experimental design was employed to study the dose-dependent effect of the combination of indomethacin and lactoferrin. The results showed that indomethacin at high concentration (100 μM) inhibited human tenocyte proliferation in culture medium with 1–10% fetal bovine serum (FBS) in vitro . Also, high dose of indomethacin inhibited the collagen formation of human tenocytes in 1% FBS culture medium. Lactoferrin at 50–100 μg/ml promoted human tenocyte survival in serum-free culture medium and enhanced proliferation and collagen synthesis of human tenocytes in 1% FBS culture medium. When 50–100 μg/ml lactoferrin was used in combination with 100–200 μM indomethacin, it partially rescued the inhibitory effects of indomethacin on human tenocyte proliferation, viability and collagen formation. To our knowledge, it is the first evidence that lactoferrin is anabolic to human tenocytes in vitro and reverses potential inhibitory effects of NSAIDs on human tenocytes. [ABSTRACT FROM AUTHOR]

Large-scale power-to‑hydrogen (P2H) stations with multi-stack configurations, are emerging as valuable flexible resources for the power grid. The energy management strategy (EMS) determines multi-stack operation statuses. Nonetheless, existing EMS focus on production without adequately addressing the implications for grid-side power factor (PF) and potential security concerns. This paper addresses this limitation by presenting a model that characterizes the PF of a multi-stack P2H system across varying operation statuses defined by current and temperature. Through this model, we highlight a clear trade-off between the PF constraint and production targets in multi-stack scheduling. Subsequently, we introduce an improved EMS framework for multi-stack P2H that seeks a balance between PF, production, and security. This EMS is organized as a two-layer execution structure to guarantee control accuracy and tractability, which includes a model-based robust multi-stack scheduling programming and a rule-based real-time increment correction algorithm in series. Case studies compare multi-stack scheduling strategies under the proposed EMS with the traditional production-oriented strategy. The effectiveness of the extended PF and security dimensions is verified to comprehensively improve the responsiveness to power instructions. Furthermore, we outline five representative cluster-level scheduling strategies aligned with different load scenarios, offering insights for practical industrial implementations. • A general framework of energy management strategy for large-scale power-to-hydrogen (P2H) stations. • The tradeoff between the power factor constraint and the production target in determining multi-stack scheduling strategies. • A two-layer EMS is executed by a model-based hour-ahead robust scheduling and a rule-based real-time increment correction. • Outlining five cluster-level scheduling strategies aligned with various load scenarios of multi-stack P2H stations. [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]

Gratings are key components in many optical systems such as spectrometers. Improving their dispersion is very important for their applications, but improving the single-order diffraction efficiency of an ultrahigh dispersion grating is still very challenging. Here, we propose an analytical approach for the design of a new type of grating named hybrid grating, which consists of a subwavelength grating and a non-subwavelength grating. The proposed analytical approach is based on a physical model in which the non-subwavelength grating acts as an ultrathin reflection grating and the subwavelength grating acts as an antireflection coating. Using the rigorous coupled-wave analysis and the finite-difference time-domain method, we numerically demonstrate that multiple hybrid gratings designed at different wavelengths using the proposed analytical approach show a diffraction efficiency of greater than 95 % in the -1st diffraction order for TE-polarized light, an angular dispersion of greater than 0.8 mrad/nm and a deflection angle of greater than 80°. Furthermore, we numerically demonstrate that by using a combined approach to optimize the parameters of a hybrid grating, the diffraction efficiency in the -1st diffraction order for TE-polarized light can be increased to more than 97 % under the condition that the angular dispersion and the deflection angle of the hybrid grating remain unchanged. Our work provides an avenue for rapid design of gratings with high angular dispersion, large deflection angle and high diffraction efficiency. • An analytical approach was proposed as a rapid design tool for high-performance hybrid gratings. • A combined approach was proposed as a time-saving tool to optimize the parameters of a hybrid grating. • A diffraction efficiency of 97.5 %, an angular dispersion of 3.91 mrad/nm and a deflection angle of 80.63° can be achieved. • The diffraction efficiency of multiple designed hybrid gratings can be greater than 80 % over a bandwidth exceeding 240 nm. [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]

Highlights • TCLs' dynamic response for providing operating reserve is modelled analytically. • A probabilistic model of operating reserve provided by TCLs is proposed. • A multi-state reliability model of operating reserve provided by TCLs is developed. • The power system short-term reliability is evaluated based on L Z -transform approach. • TCLs accounts for 5.9% of peak demand in a typical large city can enhance power system reliability by 18%. Abstract Thermostatically-controlled-loads (TCLs) have been regarded as a good candidate for maintaining the power system reliability by providing operating reserve. The short-term reliability evaluation of power systems, which is essential for power system operators in decision making to secure the system real time balancing, calls for the accurate modelling of operating reserve provided by TCLs. However, the particular characteristics of TCLs make their dynamic response different from the traditional generating units, resulting in difficulties to accurately represent the reliability of operating reserve provided by TCLs with conventional reliability model. This paper proposes a novel multi-state reliability model of operating reserve provided by TCLs considering their dynamic response during the reserve deployment process. An analytical model for characterizing dynamics of operating reserve provided by TCLs is firstly developed based on the migration of TCLs' room temperature within the temperature hysteresis band. Then, considering the stochastic consumers' behaviour and ambient temperature, the probability distribution functions of reserve capacity provided by TCLs are obtained by cumulants. On this basis, the states of reserve capacity and the corresponding probabilities at each time instant are obtained for representing the reliability of operating reserve provided by TCLs in the L Z -transform approach. Case studies are conducted to validate the proposed technique. [ABSTRACT FROM AUTHOR]

Thermal management is vital for the efficient and safe operation of alkaline electrolysis systems. Traditional alkaline electrolysis systems use simple proportional–integral–differentiation (PID) controllers to maintain the stack temperature near the rated value. However, in renewable-to-hydrogen scenarios, the stack temperature is disturbed by load fluctuations, and the temperature overshoot phenomenon occurs which can exceed the upper limit and harm the stack. This paper focuses on the thermal modeling and controller design of an alkaline electrolysis system under dynamic operating conditions. A control-oriented thermal model is established in the form of a third-order time-delay process, which is used for simulation and controller design. Based on this model, we propose two novel controllers to reduce temperature overshoot: one is a current feed-forward PID controller (PID-I), the other is a model predictive controller (MPC). Their performances are tested on a lab-scale system and the experimental results are satisfying: the temperature overshoot is reduced by 2.2 °C with the PID-I controller, and no obvious overshoot is observed with the MPC controller. Furthermore, the thermal dynamic performance of an MW-scale alkaline electrolysis system is analyzed by simulation, which shows that the temperature overshoot phenomenon is more general in large systems. The proposed method allows for higher temperature set points which can improve system efficiency by 1%. • Establish a control-oriented thermal model with multiple temperature nodes and time-delay terms. • Propose novel temperature control strategies to reduce temperature fluctuation under dynamic operating conditions. • Achieve stable temperature control under violent load fluctuation. • Higher temperature set points are available to improve system efficiency. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) multiphysics models are powerful tools for investigating the distributions of physical quantities such as temperature inside solid oxide cell (SOC) stacks, but their high computational cost remains an obstacle to their application in simulating industrial-scale stacks with tens of cells. To accelerate the simulation for the 3D model of a novel flat-chip SOC (FCSOC) stack, this study proposes a simplification method that replaces part of the governing equations with data-driven surrogate cell submodels. The submodels, built with the adaptive polynomial approximation (APA) method, take the form of polynomials and are easy to integrate into commercial CFD software such as COMSOL. Simulation shows that the simplified stack model can predict the temperatures and voltages accurately compared with the original stack model. At the same time, the time and memory required for computation are reduced by approximately 60% for a short stack model containing seven cells, owing to the simplified fuel-side mass transfer and charge transfer processes. For a large stack model with 21 cells, the reduction in computation time can even exceed 70%. The reduced computational cost makes it possible to simulate the models of industrial-scale FCSOC stacks with up to 61 cells. • A novel 3D flat-chip SOC stack model is built and verified with experimental data. • The stack model simulation is accelerated by embedding data-driven cell submodels. • The computation time for industrial-scale stack models can be reduced by over 70%. • The proposed method can be implemented easily with commercial CFD software. [ABSTRACT FROM AUTHOR]

A novel triple-component TiO 2 /Ag 3 PO 4 /graphene oxide (TiO 2 /Ag 3 PO 4 /GO) photocatalyst with dual channels for photo-generated charges separation has been synthesized to improve the photocatalytic activity and stability of Ag 3 PO 4 under visible light. The synthesis involved in-situ growth of Ag 3 PO 4 nanoparticles on GO sheets to form Ag 3 PO 4 /GO, and then deposited TiO 2 nanocrystals on the surface of Ag 3 PO 4 by hydrolysis of Ti(SO 4 ) 2 at low-temperature hydrothermal condition. The TiO 2 /Ag 3 PO 4 /GO exhibited superior photocatalytic activity and stability to bare Ag 3 PO 4 , TiO 2 /Ag 3 PO 4 and Ag 3 PO 4 /GO in degradation of Rhodamine B and phenol solutions under visible light. It is suggested that the photo-generated electrons in the conduction band of Ag 3 PO 4 can be quickly transferred to GO, while the holes in the valence band of Ag 3 PO 4 can be transferred to the valence band of TiO 2 . The dual transfer channels at the interfaces of TiO 2 /Ag 3 PO 4 /GO result in effective charges separation, leading to enhanced photocatalytic activity and stability. Furthermore, the content of noble metal Ag significantly reduces from 77 wt% in bare Ag 3 PO 4 to 55 wt% in the nanocomposite. The concept of establishing dual channels for charges separation in a triple-component heterostructure provides a promising way to develop photocatalysts with high efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three novel 1,3,5-triphenyl-2-pyrazoline-based sulfonium salts were designed and synthesized as novel photoinitiators (PIs). • The newly synthesized PIs were highly sensitive to UV–Vis LEDs (365 nm–425 nm) • The quantum yields of photoacid generation of PIs are high and wavelength-dependent. • The pyrazoline-based PIs can initiate cationic polymerization as well as free radical polymerization. • The PIs can be applied in two-photon induced lithography. Three 1,3,5-triphenyl-2-pyrazoline-based sulfonium salt photoinitiators (PIs) were synthesized, and the relationship between their molecular structures and photophysical and photochemical properties under one/two-photon irradiation were presented. The sulfonium salts had absorption peaks from 343 nm to 372 nm that overlapped the emission of 365–425 nm light-emitting diode (LED) light sources. The energy levels of molecular orbitals were closely related to the quantum yield of photoacid generation (Φ H +) as determined by the electrochemical and absorption properties of the PIs, and the introduction of an electron-pushing group could increase Φ H + due to the enlarged energy gap between the singlet excited states S 1 and S 2. Interestingly, the wavelength dependence of Φ H + was due to the nature of the transition. The π–π* transition showed high Φ H + values (0.56–0.66) under excitation by UV LEDs, while the π–σ* transition showed low efficiency under visible LED irradiation. Intramolecular charge transfer from the pyrazoline ring to sulfonium salts induced efficient photolysis and high Φ H +. The sulfonium salts could efficiently initiate cationic polymerization and free radical polymerization. In addition, their high two-photon absorption cross-sections (δ 780 nm = 75–105 GM) reduced the threshold of two-photon microfabrication. These PIs should have potential in LED photocuring and two-photon 3D microfabrication. [ABSTRACT FROM AUTHOR]

Si has been regarded as one of the most promising anodes for next-generation lithium ion batteries for its high theoretical capacity (3579 mA h g−1) and low operating potential of 0.4 V vs Li/Li+. However, the huge volume change during (de)lithiation and low electronic conductivity impede its further commercial applications. In this work, Zn/Si porous multi-layer films with controllable layer number (from 1 to 6 layers) are constructed firstly, and then a ZnO thin layer is sputtered on the top surface of the Zn/Si films to fabricate the Zn/Si/ZnO porous multi-layer films. When used as binder-free anodes, the designed 4 layers Zn/Si/ZnO thin film anode delivers the highest specific capacity, best cycling and rate performance than other multi-layer film anodes and the bulk Zn/Si/ZnO film anode with the same overall thickness. The excellent electrochemical properties of the prepared 4 layers Zn/Si/ZnO porous composite film anode can be attributed to its porous and multilayer structure, which can moderate the volume change, decrease the internal stress and benefit to the penetration of electrolyte. Moreover, the 3D electron and Li+ ion conductive channels resulted from the formed Li–Zn alloy during cycling can help to increase the electrochemical reaction kinetics. • Porous multilayer structure helps moderate the volume change of Si film anode. • Ion and electron conductive alloy framework increase the reaction kinetics. • The introduced Li 2 O layer helps to stabilize the electrode interface. [ABSTRACT FROM AUTHOR]