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The relationship between fine particulate matter (PM2.5) and blood pressure (BP) is a controversial issue. We conducted a two-sample Mendelian randomization (MR) analysis and identified 58 genome-wide significant single-nucleotide polymorphisms associated with PM2.5 as instrument variables. Inverse-variance weighted (IVW) was used as the primary analysis approach. MR-Egger, weighted median, simple model, and weighted model methods were selected for quality control. We found a significant negative causal association of higher genetically predicted PM2.5 levels with lower systolic BP (SBP), while no causal relationship was identified between PM2.5 and diastolic BP (DBP). For each 1 standard deviation increase in genetically predicted PM2.5 levels, the beta value (95% CI) of SBP was -0.14 (-0.25, -0.03) for IVW (p=0.02), and -0.13 (-0.22, -0.04) for weighted median (p=0.005). Increased PM2.5 concentrations can lead to decreased SBP levels. Our findings provided novel insights into the controversial topic on the causal relationship between PM2.5 and BP. [ABSTRACT FROM AUTHOR]

The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems in the HEA field. To address this issue, a transferable meta-learning model (MTL-AMWO) based on an adaptive migration walrus optimizer is proposed to predict the phases of HEAs. Firstly, a transferable meta-learning algorithm frame is proposed, which consists of meta-learning based on adaptive migration walrus optimizer, balanced-relative density peaks clustering, and transfer strategy. Secondly, an adaptive migration walrus optimizer model is proposed, which adaptively migrates walruses according to the changes in the average fitness value of the population over multiple iterations. Thirdly, balanced-relative density peaks clustering is proposed to cluster the samples in the source and target domains into several clusters with similar distributions, respectively. Finally, the transfer strategy adopts the maximum mean discrepancy to find the most matching historical problem and transfer its optimal solution information to the target domain. The effectiveness of MTL-AMWO is validated on 986 samples from six datasets, including 323 quinary HEAs, 366 senary HEAs, and 297 septenary HEAs. The experimental results show that the MTL-AMWO achieves better performance than other algorithms. [ABSTRACT FROM AUTHOR]

Demolding is a necessary process in polymer micro molding, which requires energy for fracture of the bonds between the mold and the polymer. This often leads to structural defects due to an inappropriate combination of process parameters, including demolding temperature, embossing force or injection pressure, separation rate, and separation mode. In this article, we focus on the theoretical analysis and experimental optimization of the process parameters for polymer micro-part demolding from a zirconium (Zr)-based bulk metallic glass mold. We developed a theoretical model to predict the demolding work, in which we considered the effects of sidewall friction combined with deformation and adhesion. We also optimized the parameters for the demolding process based on the Taguchi method. The experimental results showed that the embossing force and the separation mode had significant effects on the demolding work of hot embossing. We determined the optimum process parameters and found that with these parameters the demolding work could be decreased to 10.36 mJ in this case., INTRODUCTION Plastic is a favorable material for fabricating microfluidic devices due to its low cost, available molding technologies, as well as wide range of mechanical properties and amenability to mass [...]

Screening promising hydrogen evolution reaction (HER) electrocatalysts for water splitting is crucial for the industrial scalability of sustainable energy storage. As HER catalysts, two‐dimensional (2D) MXenes are promising substitution materials for platinum. Tuning the surface termination and loading a single atom can help to improve the electrocatalytic performance of 2D MXenes. We utilized density functional theory (DFT) calculations to explore the catalyst activity, thermal stability, and dynamic stability of 2D single atom‐loaded MXenes with surface terminations. We demonstrate that 21 uninvestigated 2D single‐atom MXene catalysts, among 264 promising candidates, show an electrocatalytic activity surpassing that of platinum. Among the 21 most promising HER catalysts, 7 (Ti3C2I2Ir, Ti3C2Br2Cu, Ti3C2Br2Pt, Ti3C2Cl2Cu, Ti3C2Cl2Pt, Ti3C2Se2Au, and Ti3C2Te2Nb) are dynamically and thermally stable. Furthermore, machine learning tools predicted the catalyst activity and thermal stability using elemental properties that are easily available in chemical data repositories. [ABSTRACT FROM AUTHOR]