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In the extreme space environment, spacecraft endure dramatic temperature variations that can impair their functionality. A VO2-based smart radiator device (SRD) offers an effective solution by adaptively adjusting its radiative properties. However, current research on VO2-based thermochromic films mainly focuses on optimizing the emissivity tunability (Δε) of single-cycle sandwich structures. Although multi-cycle structures have shown increased Δε compared to single-cycle sandwich structures, there have been few systematic studies to find the optimal cycle structure. This paper theoretically discusses the influence of material properties and cyclic structure on SRD performance using Finite-Difference Time-Domain (FDTD) software, which is a rigorous and powerful tool for modeling nano-scale optical devices. An optimal structural model with maximum emissivity tunability is proposed. The BaF2 obtained through optimization is used as the dielectric material to further optimize the cyclic resonator. The results indicate that the tunability of emissivity can reach as high as 0.7917 when the BaF2/VO2 structure is arranged in three periods. Furthermore, to ensure a longer lifespan for SRD under harsh space conditions, the effects of HfO2 and TiO2 protective layers on the optical performance of composite films are investigated. The results show that when TiO2 is used as the protective layer with a thickness of 0.1 µm, the maximum emissivity tunability reaches 0.7932. Finally, electric field analysis is conducted to prove that the physical mechanism of the smart radiator device is the combination of stacked Fabry–Perot resonance and multiple solar reflections. This work not only validates the effectiveness of the proposed structure in enhancing spacecraft thermal control performance but also provides theoretical guidance for the design and optimization of SRDs for space applications. [ABSTRACT FROM AUTHOR]

In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization. [ABSTRACT FROM AUTHOR]

Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines. [ABSTRACT FROM AUTHOR]

Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS2 with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS2 obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS2 material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS2 materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS2 materials with a reduction in the time and cost of trial and error. [ABSTRACT FROM AUTHOR]

Thermochromic smart windows are optical devices that can regulate their optical properties actively in response to external temperature changes. Due to their simple structures and as they do not require other additional energy supply devices, they have great potential in building energy-saving. However, conventional thermochromic smart windows generally have problems with high response temperatures and low response rates. Owing to their great effect in photothermal conversion, photothermal materials are often used in smart windows to assist phase transition so that they can quickly achieve the dual regulation of light and heat at room temperature. Based on this, research progress on the phase transition of photothermal material-assisted thermochromic smart windows is summarized. In this paper, the phase transition mechanisms of several thermochromic materials (VO2, liquid crystals, and hydrogels) commonly used in the field of smart windows are introduced. Additionally, the applications of carbon-based nanomaterials, noble metal nanoparticles, and semiconductor (metal oxygen/sulfide) nanomaterials in thermochromic smart windows are summarized. The current challenges and solutions are further indicated and future research directions are also proposed. [ABSTRACT FROM AUTHOR]