Glass exhibits high transmittance in the solar radiation band but high absorbance in the mid-far infrared (MIR) band, which causes a poor energy-saving effect. Thermal insulation coatings offer the most effective solution to address this. Among these, nano-cesium tungsten bronze (CsxWO3) had a strong blocking effect in the solar radiation band due to its intrinsic absorption, local surface plasmon resonance, and small polaron absorption, but its reflectivity in the MIR band was very low. Conversely, silver nanowires (AgNWs) formed a dense network structure with low transmittance and high reflectance in the MIR band; however, when mixed into Cs0.32WO3 slurries, the solar radiation-blocking ability was weakened. In order to assess the impact of AgNWs on the properties of Cs0.32WO3 films, this study collected experimental data from different Cs0.32WO3 films doped with AgNWs for multilayer perceptron (MLP) neural network machine learning. The trained models exhibited efficient and accurate prediction abilities. A large number of extrapolated independent variables were input to the trained MLP models using the grid search method, and then the predicted results of dependent variables were displayed in three-dimensional (3D) models to more intuitively show the influence of doping AgNWs on the optical performance of different Cs0.32WO3 films. Through an optimization analysis of two 3D models of T-550 nm (transmittance at 550 nm) and SC (shading coefficient), two transition values of T-550 nm were found: 69.5 and 64.2%. When T-550 nm surpassed 69.5%, the SC value of nondoped Cs0.32WO3 films was the lowest. Conversely, when T-550 (nm) was below 69.5%, doping with AgNWs decreased the SC value of Cs0.32WO3 films. Using the optimal mixture of Cs0.32WO3 slurries and AgNW slurry at a ratio of 1:3, the SC value of nondoped Cs0.32WO3 films was lower when T-550 (nm) exceeded 64.2%. Conversely, when T(550 nm)was below 64.2%, the same mixture resulted in a lower SC value. These predicted results and their accuracy were verified by experiments and provided important technical guidance and method support for future research and the application of high-performance Cs0.32WO3 films.

Thermoelectric materials can convert heat directly into electricity, and vice versa. As one of the excellent candidates for recovering the waste heat from industry and transportation, they have received extensive attention. The waste heat is widely dispersed and can be found in the temperature region from 323 K to higher than 1200 K. The composite materials have attracted much attention as popular materials in the field of thermoelectricity, and integrated high-temperature and low-temperature thermoelectric materials together to form a composite system with much higher thermoelectric superiority than single materials. Therefore, investigating thermoelectric materials suitable for high temperature is beneficial for their wider application. We have investigated the electric and thermoelectric properties of the novel high-temperature thermoelectric material La2Te3. A heavy-mass band occurs in the band structure and the figure of merit reaches 0.97 at 840 K. The optimal design variables were investigated to provide a reference for thermoelectric device design and applications. Comparing different sizes of the thermoelectric unit, the optimal segmented thermoelectric unit was obtained and the mechanism of performance improvement was explained. It was found that for a segmented thermoelectric device, there is an optimal length ratio for the n- and p-type thermoelectric materials corresponding to the maximum output power or highest conversion efficiency, but the optimal length ratios for the maximum output power and efficiency are different. Through simulation analysis and calculation, we found that the structure optimization can improve the output power and efficiency of the thermoelectric unit by 35.67

Applying solar energy over a wider spectral range can lead to more efficient energy conversion.The combination of a photovoltaic (PV) cell and a thermoelectric generator (TEG) is a widely studied technology for effectively broadening the use of the solar spectrum.In this paper, we select two kinds of photovoltaic cells and combine them with a TEG to form different systems, and analyze the overall performance of each system to provide a certain reference for optimal use of photovoltaic cells and a TEG in a hybrid system.Furthermore, we use machine learning to optimize the structural parameters of the hybrid system, and predict the optimal output power of the system when the area ratio of the TEG and PV module is 4.41.This work provides an important reference for further research on the PV-TEG hybrid system and its applications.

Due to the flexibility and lightness, flexible thermoelectric (TE) technology shows great potential in the field of renewable energy and low-temperature waste heat collection. In recent years, a lot of efforts have been made to improve the efficiency of flexible TE technology, such as synthesizing high-performance flexible TE materials, improving the structure of flexible thermoelectric generators (TEGs), and optimizing the system integration design. This review comprehensively summarizes the flexible TE materials, device types, substrate selection, and fabrication techniques, aiming to reveal the latest research trend of flexible TE technology. The methods used to improve the physical properties of flexible TE materials and device design are discussed, including theoretical analysis, experimental verification, numerical simulation, and especially the potential and challenges of machine learning in flexible TE materials and devices. Besides, we summarized the applications of flexible TE technology in wearable devices, waste heat utilization of industrial heat pipes, medical sensors, Internet of Things, etc. Finally, the current research status of flexible TE technology and the prospect of the potential development of the flexible TE field are discussed.

Combustion is the main source of energy and environmental pollution.The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions.In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field.In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed.Combustion studies combined with ML can be divided into theoretical and industrial aspects.Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel.ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation.ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation.ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields.ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants.In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines.Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters.The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.

The collection and reuse of thermal radiation energy generated by high-temperature objects has always been the focus of attention and research. Here we designed and fabricated a compound parabolic concentrator (CPC) that can be used for infrared radiation energy collection based on non-imaging optical technology. The energy gathered by CPC has a significant effect on the improvement of the surface temperature of objects. The thermoelectric (TE) generator is a good choice to utilize this thermal energy. This paper analyses and discusses the effects of CPC on the performance of thermoelectric model by simulation. The result has well demonstrated that the TE model with CPC has not only a considerable reduction in structure size and material consumption, but also ensures higher output power and efficiency. In addition, we propose that the array of CPC shall prominently enhance the performance of thermoelectric device.

Non-Imaging Optics for Improving Waste Heat Collection with ThermoelectricsWaste heat is often dumped into the environment and there have not been many cost-effective ways so far to harness it especially if it is of a low grade.Thermal radiation is the predominant form of heat transport between Space and Earth but it is often not considered to be efficient for waste heat applications.Here, we utilize the concept of non-imaging optics to enhance the capture of waste heat from a hot object in the form of thermal radiation.We designed and fabricated a compound parabolic concentrator (CPC) that is purely reflective in the mid-infrared using geometric non-imaging optics.With the CPC present, in between a hot plate and a target separated 10 cm apart under ambient o conditions, we were able to increase the temperature of the target by as much as 20 C. At the same time, our simulations and experiment demonstrate remarkable improvement in thermoelectric (TE) efficiency and output power compared to a bare TE exposed to the same radiative heat flux.Our work serves as a proof-of-concept for demonstrating the potential to collect waste heat remotely using pure thermal radiation.