High-flexibility, high-toughness double-cross-linked (DC) chitin hydrogels are prepared through a sequential chemical and physical cross-linkings strategy. The incorporation of chemically and physically cross-linked domains imbues the DC chitin hydrogels with relatively high stiffness, high toughness, and toughness recoverability.

No abstract

Nanostructured conductive polymers can offer analogous environments for extracellular matrix and induce cellular responses by electric stimulation, however, such materials often lack mechanical strength and tend to collapse under small stresses. We prepared electrically conductive nanoporous materials by coating nanoporous cellulose gels (NCG) with polypyrrole (PPy) nanoparticles, which were synthesized in situ from pyrrole monomers supplied as vapor. The resulting NCG/PPy composite hydrogels were converted to aerogels by drying with supercritical CO2, giving a density of 0.41-0.53 g cm(-3), nitrogen adsorption surface areas of 264-303 m(2) g(-1), and high mechanical strength. The NCG/PPy composite hydrogels exhibited an electrical conductivity of up to 0.08 S cm(-1). In vitro studies showed that the incorporation of PPy into an NCG enhances the adhesion and proliferation of PC12 cells. Electrical stimulation demonstrated that PC12 cells attached and extended longer neurites when cultured on NCG/PPy composite gels with DBSA dopant. These materials are promising candidates for applications in nerve regeneration, carbon capture, catalyst supports, and many others.

No abstract

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available at https://github.com/i ieir-km/ComplEx-NNE_AER. 7 https://github.com/ttrouill/complex 8 https://github.com/quark0/ANALOGY 9 https://github.com/iieir-km/RUGE 10 An exception here is that ANALOGY uses asynchronous SGD with AdaGrad (Liu et al., 2017).