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Cellulose-based conductive composite fibers hold great promise in smart wearable applications, given cellulose's desirable properties for textiles. Blending conductive fillers with cellulose is the most common means of fiber production. Incorporating a high content of conductive fillers is demanded to achieve desirable conductivity. However, a high filler load deteriorates the processability and mechanical properties of the fibers. Here, developing wet-spun cellulose-based fibers with a unique side-by-side (SBS) structure via sustainable processing is reported. Sustainable sources (cotton linter and post-consumer cotton waste) and a biocompatible intrinsically conductive polymer (i.e., polyaniline, PANI) were engineered into fibers containing two co-continuous phases arranged side-by-side. One phase was neat cellulose serving as the substrate and providing good mechanical properties; another phase was a PANI-rich cellulose blend (50 wt%) affording electrical conductivity. Additionally, an eco-friendly LiOH/urea solvent system was adopted for the fiber spinning process. With the proper control of processing parameters, the SBS fibers demonstrated high conductivity and improved mechanical properties compared to single-phase cellulose and PANI blended fibers. The SBS fibers demonstrated great potential for wearable e-textile applications. 

This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.