Search

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]

Ultrafiltration (UF) can effectively remove large particles, suspended solids, and colloidal substances from anaerobic digestate. However, membrane fouling is a technical challenge in the purification of the digestate by UF. In this study, polyethersulfone (PES) membranes with four pore sizes (50.0, 20.0, 10.0 and 5.0 kDa) were employed to filter anaerobic digestate from swine manure. The effects of temperature, transmembrane pressure (TMP), and cross-flow velocity (CFV) on flux were investigated. The purification effects and fouling characteristics of the four membranes were analyzed. The results revealed that the increase of temperature and CFV can effectively promote UF separation efficiency, but as the TMP exceeded 3.0 bar, the flux increase rates of the four membranes were almost zero. The larger membrane pore size caused the faster flux increase with the increase in pressure. During the batch experiment, the 20.0 kDa membrane showed the lowest flux maintenance ability, while the 5.0 kDa showed the highest ability due to the smaller pore size. All four membranes can effectively remove tetracyclines residues. Elements C, O, and S were the major membrane foulant elements. The dominant bacteria orders of membrane fouling were Pseudomonadales, Xanthomonadales and Burkholderiales. Compared with tap water and citric acid, the membrane cleaning by NaOH and NaClO showed higher flux recovery rates. The 50.0 kDa membrane achieved the best cleaning effects under all cleaning methods. [ABSTRACT FROM AUTHOR]

Liquid-ammonia mercerization is commonly used to enhance the quality of cotton fabric in the textile industry, resulting in a large amount of liquid-ammonia mercerization wastewater (LMWW) containing high concentration of ammonia to be disposed of. This study proposes a partial nitritation/anammox (PN/A) process based on stable nitritation by a zeolite sequencing batch reactor (ZSBR) for the nitrogen removal of LMWW. The ZSBR could quickly achieve stably full nitritation with a nitrite accumulation ratio higher than 97% and an ammonia removal rate of 0.86 kg N·m−3·d−1 for the raw LMWW with an ammonia level of 1490 mg/L. In order to avoid anammox inhibition by free nitrous acid, the ZSBR was successfully changed to PN operation with diluted LMWW for effluent meeting anammox requirements. The next anammox reactor (an up-flow blanket filter (UBF)) realized a total nitrogen removal efficiency of 70.0% with a NLR (nitrogen loading rate) of 0.82 kg N·m−3·d−1 for LMWW. High-throughput sequencing analysis results indicated that Nitrosomonas and Candidatus Kuenenia were the dominant bacteria in ZSBR and UBF, respectively. All results revealed that the PN/A process based on ZSBR as the PN pretreatment process was feasible for LMWW, facilitating cost-effective and low-carbon nitrogen removal for LMWW treatment in the textile industry in the future. [ABSTRACT FROM AUTHOR]

Livestock manure is one of the major sources of volatile organic compound (VOC) emissions; however, characteristics of VOCs emitted from biogas digestate (BD) storage, which is a common manure practice, remain unclear. The objective of this study was to characterize VOC emissions during BD storage through the dynamic emission vessel method, to identify the VOC emissions that have potential odor and/or toxic effects. The results revealed the detection of 49 VOCs with seven classes, whose total concentration varied from 171.35 to 523.71 μg m−3. The key classes of the 49 VOCs included Oxygenated VOCs (OVOCs), olefins and halogenated hydrocarbons. The top four compositions, accounting for 74.38% of total VOCs (TVOCs), included ethanol, propylene, acetone and 2-butanone. The top four odorous VOCs, accounting for only 5.15% of the TVOCs, were toluene, carbon disulfide, ethyl acetate and methyl sulfide, with the concentration ranging from 13.25 to 18.06 μg m−3. Finally, 11 main hazardous air pollutant VOCs, accounting for 32.77% of the TVOCs, were propylene, 2-butanone, toluene, methyl methacrylate, etc., with the concentration ranging from 81.05 to 116.96 μg m−3. Results could contribute to filling the knowledge gaps in the characteristics of VOC emissions from biogas digestate (BD), and provide a basis for exploring mitigation strategies on odor and hazardous air pollutions. [ABSTRACT FROM AUTHOR]