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Vanadium dioxide (VO 2) is a promising material for energy-saving smart windows due to its reversible metal-to-insulator transition near room temperature, concomitantly with a structural phase transition between monoclinic VO 2 (M) phase and rutile VO 2 (R) phase. However, the fact that VO 2 has a complex crystalline phase makes its reliable synthesis an obstacle to its practical application. Machine learning (ML), a specific subset of artificial intelligence, can be utilized to generate virtual representations of experimental conditions and outcomes for the purpose of predicting experiments. Therefore, in the paper, four machine learning models were trained to perform optimization of the VO 2 hydrothermal synthesis. A random forest model achieved a classification accuracy of 87.27%. The synthetic parameter space was explored to filter combinations with a synthetic probability above 90%. Random forest models were used to guide the experimental synthesis, and the obtained products were characterized using X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, and differential scanning calorimetry. The results showed that phase-pure VO 2 (B) and VO 2 (M) were successfully synthesized, demonstrating the effectiveness of machine learning in optimizing material synthesis, alleviating the stochasticity of material synthesis caused by the control of synthesis conditions, and promoting the application research of VO 2 materials. [ABSTRACT FROM AUTHOR]

In recent years, radiative cooling has garnered widespread attention due to its energy-independent and environmentally friendly nature. However, one major challenge in the field of radiative cooling faces is the lack of artificial control over the cooling effect, which not only impacts cooling comfort but also leads to unnecessary energy waste during heating in cold environments. In this paper, a novel approach to address this challenge is proposed by a radiative cooling device with a three-layer structure consisting of vanadium dioxide (VO 2), polydimethylsiloxane (PDMS) and silver. The transition of VO 2 between the metallic and insulating states results in the formation and disappearance of the Fabry-Pérot resonator, thereby facilitating the transition between different cooling modes. Additionally, the stretchability of the PDMS enables the device to achieve different radiative cooling power by applying varying levels of stress. In high-temperature environments, it achieves an average net radiative cooling power of 96.2 W/m2 throughout the day, highlighting its exceptional energy-saving capabilities. In low-temperature environments, the cooling effect can be suppressed to prevent excessive cooling, achieving a temperature reduction as low as 0.4 °C. This device offers controllable cooling power, and its diverse control modes in different environments contribute to its excellent environmental adaptability. [ABSTRACT FROM AUTHOR]

Vanadium dioxide (VO 2) is a key material for thermochromic smart windows that can respond to environmental temperature to regulate near-infrared transmittance automatically. To date, VO 2 -based smart windows have made great progress, but practical applications still remain restricted by two tough challenges, i.e., low solar regulation ability and unpopular color. In view of the above two challenges, in this paper, novel warm/cool-tone switchable VO 2 -based composite films were designed and prepared. The novel composite films demonstrate outstanding optical properties: Δ T sol = 18.92% and T lum,l = 48.70%. The high solar modulation efficiency is attributed to the modulation of the thermochromic microcapsules in visible light waveband and VO 2 in near-infrared region. Meanwhile changes in color of the composite films at different temperatures were also studied in order to develop high-performance warm/cool-tone switchable thermochromic smart windows. These promising results will benefit to promote the popularization and application of smart windows. [ABSTRACT FROM AUTHOR]