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Current research on ferroelectric polymers centers predominantly on poly(vinylidene fluoride) (PVDF)–based fluoropolymers because of their superior performance. However, they are considered “forever chemicals” with environmental concerns. We describe a family of rationally designed fluorine-free ferroelectric polymers, featuring a polyoxypropylene main chain and disulfonyl alkyl side chains with a C3 spacer: −SO₂CH₂CHRCH₂SO₂− (R = −H or −CH₃). Both experimental and simulation results demonstrate that strong dipole-dipole interactions between neighboring disulfonyl groups induce ferroelectric ordering in the condensed state, which can be tailored by changing the R group: ferroelectric for R = −H or relaxor ferroelectric for R = −CH₃. At low electric fields, the relaxor polymer exhibits electroactuation and electrocaloric performance comparable with those of state-of-the-art PVDF-based tetrapolymers. 

method for structure determination. Despite the substantial growth in deposited cryo-EM maps driven by advances in microscopy and image processing, accurately constructing models from these maps remains challenging. Extracting secondary structure information from EM maps is valuable for cryo-EM modeling. In this context, we introduce a novel deep learning secondary structure annotation framework specifically designed for intermediate-resolution cryo-EM maps, employing a three-dimensional Inception architecture. Testing it on diverse datasets, including maps with authentic intermediate resolutions, demonstrates its accuracy and robustness in identifying secondary structures in cryo-EM maps. We conducted a comparative analysis of our results against frameworks that exist in the state-of-the-art, and our framework demonstrated superior performance across nearly all secondary structure elements. We employed the F1 accuracy metric, yielding an average F1 score of 0.657 for helix, 0.712 for coil, and 0.596 for sheet predictions. Notably, certain helix and sheet predictions achieved an impressive F1 score of 0.881. 

Innovation in biomedical science is always a field of interest for researchers. Drug delivery, being one of the key areas of biomedical science, has gained considerable significance. The utilization of simple yet effective techniques such as electrospinning has undergone significant development in the field of drug delivery. Various polymers such as PEG (polyethylene glycol), PLGA (Poly(lactic-co-glycolic acid)), PLA(Polylactic acid), and PCA (poly(methacrylate citric acid)) have been utilized to prepare electrospinning-based drug delivery systems (DDSs). Polyvinyl alcohol (PVA) has recently gained attention because of its biocompatibility, biodegradability, non-toxicity, and ideal mechanical properties as these are the key factors in developing DDSs. Moreover, it has shown promising results in developing DDSs individually and when combined with natural and synthetic polymers such as chitosan and polycaprolactone (PCL). Considering the outstanding properties of PVA, the aim of this review paper was therefore to summarize these recent advances by highlighting the potential of electrospun PVA for drug delivery systems. 

Abstract: Surface acoustic wave (SAW) sensors with increasingly unique and refined designed patterns are often developed using the lithographic fabrication processes. Emerging applications of SAW sensors often require novel materials, which may present uncharted fabrication outcomes. The fidelity of the SAW sensor performance is often correlated with the ability to restrict the presence of defects in post-fabrication. Therefore, it is critical to have effective means to detect the presence of defects within the SAW sensor. However, labor-intensive manual labeling is often required due to the need for precision identification and classification of surface features for increased confidence in model accuracy. One approach to automating defect detection is to leverage effective machine learning techniques to analyze and quantify defects within the SAW sensor. In this paper, we propose a machine learning approach using a deep convolutional autoencoder to segment surface features semantically. The proposed deep image autoencoder takes a grayscale input image and generates a color image segmenting the defect region in red, metallic interdigital transducing (IDT) fingers in green, and the substrate region in blue. Experimental results demonstrate promising segmentation scores in locating the defects and regions of interest for a novel SAW sensor variant. The proposed method can automate the process of localizing and measuring post-fabrication defects at the pixel level that may be missed by error-prone visual inspection. 

Polymeric microparticles have been shown to have great impacts in the area of drug delivery, biosensing, and tissue engineering. Electrospray technology, which provides a simple yet effective technique in the creation of microparticles, was utilized in this work. In addition, altering the electrospray experimental parameters such as applied voltage, flow rate, collector distance, solvents, and the polymer-solvent mixtures can result in differences in the size and morphology of the produced microparticles. The effects of the flow rate at (0.15, 0.3, 0.45, 0.6, 0.8, and 1 mL/h) and N, N-Dimethylformamide (DMF)/acetone solvent ratios (20:80, 40:60, 60:40, 80:20, 100:0 v/v) in the production of polyvinylidene fluoride (PVDF) microparticles were studied. Scanning electron microscopy (SEM) was used to observe changes in the morphology of the microparticles, and this revealed that a higher acetone to DMF ratio produces deformed particles, while flow rates at (0.3 and 0.45 mL/h) and a more optimized DMF to acetone solvent ratio (60:40 v/v) produced uniform spherical particles. We discovered from the Raman spectroscopy results that the electrosprayed PVDF microparticles had an increase in piezoelectric β phase compared to the PVDF pellet used in making the microparticles, which in its original form is α phase dominant and non-piezoelectric.