Due to their advantages of self-powered capability and compact size, combined thermoelectric devices, in which a thermoelectric cooler module is driven by a thermoelectric generator module, have become promising candidates for cooling applications in extreme conditions or environments where the room is confined and the power supply is sacrificed. When the device is designed as two-stage configuration for larger temperature difference, the design degree is larger than that of a single-stage counterpart. The element number allocation to each stage in the system has a significant influence on the device performance. However, this issue has not been well-solved in previous studies. This work proposes a three-dimensional multi-physics model coupled with multi-objective genetic algorithm to optimize the optimal element number allocation with the coefficient of performance and cooling capacity simultaneously as multi-objective functions. This method increases the accuracy of performance prediction compared with the previously reported examples studied by the thermal resistance model. The results show that the performance of the optimized device is remarkably enhanced, where the cooling capacity is increased by 23.3% and the coefficient of performance increased by 122.0% compared with the 1# Initial Solution. The mechanism behind this enhanced performance is analyzed. The results in this paper should be beneficial for engineers and scientists seeking to design a combined thermoelectric device with optimal performance under the constraint of total element number.

As a direct energy converter between heat and electricity, thermoelectric generators (TEGs) have potential applications including recovery of waste heat, and solar thermoelectric power generation. Geometric parameter and material are two critical factors to improve the TEG performance. However, the strategies base on structure design and material development are always separated. There are limited studies on the effects of consolidating them simultaneously. Here, an idea of segmented material coupled with irregularly variable cross-section design was conceived to further improve the TEG output power. The performance of TEGs with rectangular leg, segmented leg, variable cross-sectional leg, and the new design are compared. The coupling effects between various mechanisms are revealed, which are responsible for the superior performance provided by the developed design. Based on this knowledge, a multiparameters optimization was performed through the genetic algorithm to reach the optimal combination of design parameters. The results show that, with a constraint of certain material volume, the optimal performance of the TEG can be further enhanced by coupling segmented material and irregularly variable cross-section design. An improvement of 51.71% was achieved when compared with the conventional counterpart. This work offers a simple route to enhance the TEG performance when the device materials are specified, without an increase in the cost of manufacturing.

Zinc–bromine redox flow battery (ZBFB) is one of the most promising candidates for large-scale energy storage due to its high energy density, low cost, and long cycle life. However, numerical simulation studies on ZBFB are limited. The effects of operational parameters on battery performance and battery design strategy remain unclear. Herein, a 2D transient model of ZBFB is developed to reveal the effects of electrolyte flow rate, electrode thickness, and electrode porosity on battery performance. The results show that higher positive electrolyte flow rates can improve battery performance; however, increasing electrode thickness or porosity causes a larger overpotential, thus deteriorating battery performance. On the basis of these findings, a genetic algorithm was performed to optimize the batter performance considering all the operational parameters. It is found that the battery energy efficiency can reach 79.42% at a current density of $20{\mathrm{mA}\mathrm{cm}}^{-2}$. This work is helpful to understand the energy storage characteristics and high-performance design of ZBFB operating at various conditions.

We study the dynamics of the quantum steering between two separated qubits trapped in a one-dimensional plasmonic waveguide. By numerical methods, we calculate the quantum steerability and other quantum correlations, i.e., entanglement, discord, and coherence, for both cases with and without laser driving fields. It is found that steerability may exhibit a sudden disappearance and sudden reappearance phenomenon. Specifically, there exist time windows with no steerability but finite entanglement. The effects of plasmon wavenumber and the distance between the two qubits on steerability are also examined. Furthermore, we show that quantum steerability is tunable by adjusting the laser driving fields.

Micro/nano-motors play an important role in energy, environment, and biomedicines. As a new type of nano-motors, nanobottles attract great attention due to their distinct advantages of a large cavity, high specific surface area, bionic streamline structure, and chemotactic motion. Here, we systematically review the development of nanobottle motors from aspects of propulsion mechanisms, fabrication methods and potential applications. Firstly, three types of propulsive modes are summarized, with focus on chemical propulsion, light driving and magnetic actuation. We then discuss the fabrication methods of nanobottles, including the soft-template-based hydrothermal method and the swelling-inducement and wet-chemistry methods. The potential applications of nanobottle motors are additionally highlighted in energy, environmental, and biomedical fields. Finally, the future challenges and outlooks of nanobottle motors are discussed for the further development of this technology.