Integrated Catalyst-Substrate Electrodes for Electrochemical Water Splitting: A Review on Dimensional Engineering Strategy.

Water splitting (or, water electrolysis) is considered as a promising approach to produce green hydrogen and relieve the ever-increasing energy consumption as well as the accompanied environmental impact. Development of high-efficiency, low-cost practical water-splitting systems demands elegant design and fabrication of catalyst-loaded electrodes with both high activity and long-life time. To this end, dimensional engineering strategies, which effectively tune the microstructure and activity of electrodes as well as the electrochemical kinetics, play an important role and have been extensively reported over the past years. Here, a type of most investigated electrode configurations is reviewed, combining particulate catalysts with 3D porous substrates (aerogels, metal foams, hydrogels, etc.), which offer special advantages in the field of water splitting. It is analyzed the design principles, structural and interfacial characteristics, and performance of particle-3D substrate electrode systems including overpotential, cycle life, and the underlying mechanism toward improved catalytic properties. In particular, it is also categorized the catalysts as different dimensional particles, and show the importance of building hybrid composite electrodes by dimensional control and engineering. Finally, present challenges and possible research directions toward low-cost high-efficiency water splitting and hydrogen production is discussed.

Optimal IOFL-based economic model predictive control technique for boiler-turbine system.

The optimal control design of the boiler-turbine system is vital to ensure feasibility and high responsiveness over desired load variations. Using the traditional linear control techniques realization of this task is difficult, as the boiler-turbine mechanism has strong nonlinearities. Besides, environmental and economic concerns have replaced existing tracking control ones as the primary concerns of advanced power plants. Thus, this study proposes an optimal economic model predictive controller (EMPC) scheme for this unit on the basis of the input/output feedback linearization (IOFL) method. By employing the IOFL method, this unit is decoupled into a new linearized model that is utilized for developing the suggested optimal IOFL EMPC technique. The proposed control scheme is formulated in an economic quadratic programming form that considers the input-rate and input limits of the unit for optimal economic performance. In addition, an adaptive iterative algorithm is utilized for constraints mapping with guaranteeing a feasible solution in a finite number of steps without violation of original constraints over the entire predictive horizon. The outcomes of the simulation show that the suggested optimal IOFL EMPC scheme offers an improved dynamic and economic output performance over fuzzy hierarchical MPC, fuzzy EMPC, and nonlinear EMPC techniques during various load variations.

Ultra-Large Stress and Strain Polymer Nanocomposite Actuators Incorporating a Mutually-Interpenetrated, Collective-Deformation Carbon Nanotube Network.

Stimulus-responsive polymer-based actuators are extensively studied, with the challenging goal of achieving comprehensive performance metrics that include large output stress and strain, fast response, and versatile actuation modes. The design and fabrication of nanocomposites offer a promising route to integrate the advantages of both polymers and nanoscale fillers, thus ensuring superior performance. Here, it is started from a three-dimensional (3D) porous sponge to fabricate a mutually interpenetrated nanocomposite, in which the embedded carbon nanotube (CNT) network undergoes collective deformation with the shape memory polymer (SMP) matrix during large-degree stretching and releasing, increases junction density with polymer chains and enhances molecular orientation. These features result in substantial improvement of the overall mechanical properties and during thermally actuated contraction, the bulk SMP/CNT composites exhibit output stresses up to 19.5 ± 0.97 MPa and strains up to 69%, accompanied by a rapid response and high energy density, exceeding the majority of recent reports. Furthermore, electrical actuation is also demonstrated via uniform Joule heating across the self-percolated CNT network. Applications such as low-temperature thermal actuated vascular stent and wound dressing are explored. These findings lay out a universal blueprint for developing robust and highly deformable SMP/CNT nanocomposite actuators with broad potential applications.

Orthogonal-Channel, Low-Tortuosity Carbon Nanotube Platforms for High-Performance Li-O2 Batteries.

Aerogels and foams are promising electrode materials owing to their lightweight, high porosity, and large surface area for creating abundant active/catalytic sites. Tailoring their porous structure is essential toward maximum electrode performance yet remains challenging in the field. Here, by modifying a pristine carbon nanotube (CNT) sponge with random internal distribution, we present a CNT platform consisting of regular, orthogonally intercrossed through-channels centered at a suitable lateral size (around 5 µm), with low tortuosity and enhanced electrochemical kinetics under predefined compression. Our CNT platforms, grafted by bifunctional transitional metal hydroxide catalyst, overcome considerable challenges of both long cycle life and high rates simultaneously, serving as Li-O2 cathodes and achieving lifetime of 500 cycles at 0.5 mA cm-2 (275 cycles even at 1 mA cm-2) and also displaying high areal capacity (27 mA h cm-2), which are superior to most of the recently reported porous electrodes based on various materials. The mechanism involving fast triple-phase transport and reversible discharge product deposition, enabled by catalyst-loaded orthogonal channels, has been disclosed. Such structure-tailored robust CNT platforms could find many applications in electrochemical catalysis and energy storage systems.

Recent Advances in MXene-Based Fibers: Fabrication, Performance, and Application.

Two-dimensional transition metal carbide/nitrides (MXenes) have recently received extensive attention due to their diverse material types and versatile structures, large-scale production, and excellent properties. MXene sheets possess abundant hydrophilic functional groups on their surface, which enable them to be assembled into macroscopic fibers or compounded with other functional materials to produce composite fibers. This review aims to provide a comprehensive analysis of MXene fibers in terms of their fabrication, structure, properties, and recent applications as flexible and wearable electronics. The review will discuss the principles of different methods used to synthesize MXene fibers and analyze the characteristics of the as-synthesized fibers, with a particular focus on the wet spinning method. The fundamental relationships between the microstructure of MXene fibers and their resulting mechanical and electrical properties will be explored. Furthermore, the review will elaborate on the progress made in MXene-based fibers in the rapidly growing field of wearable electronics applications, provide insights into future development of MXene fiber materials and propose solutions to the challenges facing practical applications.

An Efficient Iterative Learning Predictive Functional Control for Nonlinear Batch Processes.

Iterative learning model-predictive control (ILMPC) is very popular in controlling the batch process since it possesses not only the learning ability along batches but also the strong time-domain tracking properties. However, for a fast batch process with strong nonlinear dynamics, the application of the ILMPC is challenging due to the difficulty in balancing the computational efficiency and tracking accuracy. In this article, an efficient iterative learning predictive functional control (ILPFC) is proposed. The original nonlinear system is linearized along the reference trajectory to derive a 2-D tracking-error predictive model. The linearization error is compensated by utilizing the Lipschitz condition so that the objective function can be formulated with the upper bound of the actual tracking error. For enhancing control efficiency, predictive functional control (PFC) is applied in the time domain, which reduces the dimension of the decision variable in order to effectively cut down the computational burden. The stability and convergence of this ILPFC with terminal constraint are analyzed theoretically. Simulations on an unmanned ground vehicle and a typical fast batch reactor verify the effectiveness of the proposed control algorithm.

Iterative Learning Model Predictive Control Based on Iterative Data-Driven Modeling.

Iterative learning model predictive control (ILMPC) has been recognized as an effective approach to realize high-precision tracking for batch processes with repetitive nature because of its excellent learning ability and closed-loop stability property. However, as a model-based strategy, ILMPC suffers from the unavailability of accurate first principal model in many complex nonlinear batch systems. On account of the abundant process data, nonlinear dynamics of batch systems can be identified precisely along the trials by neural network (NN), making it enforceable to design a data-driven ILMPC. In this article, by using a control-affine feedforward neural network (CAFNN), the features in the process data of the former batch are extracted to form a nonlinear affine model for the controller design in the current batch. Based on the CAFNN model, the ILMPC is formulated in a tube framework to attenuate the influence of modeling errors and track the reference trajectory with sustained accuracy. Due to the control-affine structure, the gradients of the objective function can be analytically computed offline, so as to improve the online computational efficiency and optimization feasibility of the tube ILMPC. The robust stability and the convergence of the data-driven ILMPC system are analyzed theoretically. The simulation on a typical batch reactor verifies the effectiveness of the proposed control method.