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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]

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]

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]

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]

• 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]

Abstract: Background: Hyponatremia is a common but often mistreated clinical situation in the ICU. This often requires the physician to identify the underlying problem, adrenal insufficiency. However, by the textbook, the current treatment always involves sodium chloride supplementation to hyponatremic patients, either intravenous or oral intake. We hypothesize that the mechanism behind most hyponatremia is most likely to be the sodium and water redistribution from the serum to the cells or the interstitial spaces due to the insufficient cortical steroid, not the sodium deficiency. As we have no reason to believe the patients have lost that much sodium which caused hyponatremia. Therefore, giving this type of hyponatremic patients (adrenal insufficient) sodium chloride is always ineffective and sometimes catastrophic. Methods: We discuss the possible mechanism for hyponatremia in critically ill/post surgery patients who are mostly likely to be adrenal insufficient rather than absolute sodium deficiency. In combination with many other common but unexplainable symptoms such as nausea, vomiting, obstinate diarrhea, hypotension and coma in the ICU, it is highly likely that hyponatremia is a condition which reflects the patients’ adrenal function. The evidence supporting our hypothesis is that, (1) the serum sodium level does not always respond well to sodium supplementation therapy; (2) those aforementioned symptoms alleviated simultaneously with the serum sodium level returned to normal after the hydrocortisone or prednisone was administered without any oral/intravenous sodium supplementation; (3) patient with an elevated serum/urine cortisol level suffers from aforementioned unexplainable symptoms does not warrant him being adrenal sufficient. If the patient also has hyponatremia, the diagnosis can be considered as “relative adrenal insufficiency” and the patient would respond well to hydrocortisone or prednisone therapy. Conclusions: We hypothesize that hyponatremia without significant loss of sodium can be used as an indicator to monitor the patients’ adrenal function regardless of the serum/urine cortisol level. Furthermore, we propose a novel approach toward hyponatremia treatment in critically ill patients would be hydrocortisone or prednisone therapy depending on the circumstances. [Copyright &y& Elsevier]

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]

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]

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]

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]

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]

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]