Prominent Crystallization Promotion Effect of Montmorillonite on PTT/PC Blends with PTT as the Continuous Phase

To regulate the crystallization of poly(trimethylene terephthalate) (PTT) retarded by melt blending with polycarbonate (PC), the crystallization of the PTT/PC blend was investigated employing nano-montmorillonite (MMT) as a crystallization promoter with PTT as the continuous phase. The results showed that MMT exhibits a significant promoting effect on PTT crystallization; the presence of 1 wt. % MMT shifts the initial and peak crystallization temperatures of the 70/30 PTT/PC blend to ~17 °C and ~32 °C, respectively. Additionally, the full width at half maximum (FWHM) narrows by ~45%, and the ΔHc increases by 3.7 J.g−1. The accelerating effect of MMT is determined by its distribution and dispersion which depends on the shear intensity, mixing mode, and loading. MMT is easier to exfoliate via the two-step method than by the one-step method. The distribution in the PTT phase is enriched along the phase interface forming an MMT layer. This endows sections of the PTT with abundant nuclei and thus crystallization is promoted markedly compared with the one-step method. Moreover, the finer MMT migrates more readily to the interface to cause a much smoother phase interface. However, a secondary crystallization peak appears when the shear force is not sufficient enough to make MMT finely dispersed, in case of the two-step method and the MMT content is increased to 3 wt. %. The mixing temperature shows little effect on the acceleration of MMT on the crystallization of PTT/PC compared with the shear force. Only when MMT did not exfoliate or uncomplete did the presence of epoxy resin help to promote crystallization because of the improved MMT dispersion

Effects of the Laser Micromelting Process Parameters on the Preparation of Micron-Sized FeCrAl Coatings on Zr Alloy Surfaces

Laser micromelting (LMM) technology allows for the remelting of pre-positioned coatings on the surface of a specimen to form a metallurgical bond with the substrate material, significantly improving the coating’s film–base bond. However, the high energy input from the laser modification process can cause severe element diffusion, rendering the coating susceptible to deformation and cracking. This can be mitigated by controlling the laser power, scanning speed, and offset of the LMM process. The temperature and stress fields of the samples in the LMM process were analyzed via finite element simulation. The effects of the LMM process parameters on the coating morphology were analyzed in conjunction with experiments. The results indicated that the laser power significantly affected the morphology of the coating after remelting, and a higher scanning speed was more likely to cause the coating to accumulate stress. Additionally, a smaller offset inhibited crack generation. At a laser power of 30 W, a scanning speed of 1200 mm/min, and a scanning spacing of 0.035 mm, the surface of the coating had no obvious defects and was relatively flat, and the adhesion and corrosion resistance were significantly improved. This study provides valuable guidance for improving the preparation of micron-sized protective coatings on Zr alloy surfaces

The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases

With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers’ or adults’ face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus