To construct a polyetherimide (PEI)-reinforced polyvinylidene fluoride (PVdF) composite membrane with multicore-shell structure, a ternary solution was prepared and electrospun by single-nozzle electrospinning. A theoretical prediction was made for the feasibility of complete distinction of two phases. The diameters of the membrane fibers and the PEI multi-core fibrils varied with the PEI ratio and the spinning time, respectively. The tensile strength and modulus were improved to 48 MPa and 1.5 GPa, respectively. The shrinkage of the membrane was only 6.6% at 180 °C, at which temperature the commercial PE separator melted down. The reinforcement in mechanical and thermal properties is associated with multiple PEI nanofibrils oriented along the fiber axis. Indeed, the unique morphology of self-assembled multicore-shell fibers plays an important role in their properties. All in all, PEI/PVdF membranes are appropriate for a lithium-ion battery application due to their high mechanical strength, excellent thermal stability, and controllable textural properties.

BackgroundInvasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi‐automatic segmentation tools require labor‐intensive and time‐consuming manual correction, limiting their application in the catheterization room.PurposeThis study aims to propose rank‐based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep‐learning segmentation of ICA.MethodsTwo selective ensemble methods proposed in this work integrated the weighted ensemble approach with per‐image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo‐ground truth generated from a meta‐learner (ESEN). Five‐fold cross‐validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients.ResultsThe selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one‐sixth of a second.ConclusionProposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real‐time QCA‐based diagnostic methods in routine clinical settings.