Shape-Memory-Reduced Graphene/Chitosan Cryogels for Non-Compressible Wounds.

In this study, an antibacterial and shape-memory chitosan cryogel with high blood absorption and fast recovery from non-compressible wounds was prepared using a one-step method. Herein, we prepared a shape-memory-reduced graphene/chitosan (rGO-CTS) cryogel using a one-step method with a frozen mixing solution of chitosan, citric acid, dopamine, and graphene oxide, before treating it with alkaline solutions. The alkaline solution not only promoted the double cross-linking of chitosan but also induced dopamine to form polydopamine-reducing graphene oxide. Scanning electron microscope (SEM) images showed that the rGO-CTS cryogel possessed a uniform porous network structure, attributing excellent water-induced shape-memory properties. Moreover, the rGO-CTS cryogel exhibited good mechanical properties, antibacterial activity, and biocompatibility. In mouse liver trauma models, the rGO-CTS cryogel showed good blood clotting and hemostatic capabilities. Therefore, this composite cryogel has great potential as a new hemostatic material for application to non-compressible wounds.

Graph Neural Networks With High-Order Polynomial Spectral Filters.

General graph neural networks (GNNs) implement convolution operations on graphs based on polynomial spectral filters. Existing filters with high-order polynomial approximations can detect more structural information when reaching high-order neighborhoods but produce indistinguishable representations of nodes, which indicates their inefficiency of processing information in high-order neighborhoods, resulting in performance degradation. In this article, we theoretically identify the feasibility of avoiding this problem and attribute it to overfitting polynomial coefficients. To cope with it, the coefficients are restricted in two steps, dimensionality reduction of the coefficients' domain and sequential assignment of the forgetting factor. We transform the optimization of coefficients to the tuning of a hyperparameter and propose a flexible spectral-domain graph filter, which significantly reduces the memory demand and the adverse impacts on message transmission under large receptive fields. Utilizing our filter, the performance of GNNs is improved significantly in large receptive fields and the receptive fields of GNNs are multiplied as well. Meanwhile, the superiority of applying a high-order approximation is verified across various datasets, notably in strongly hyperbolic datasets. Codes are publicly available at: https://github.com/cengzeyuan/TNNLS-FFKSF.

The removal of azo dye from aqueous solution by oxidation with peroxydisulfate in the presence of granular activated carbon: Performance, mechanism and reusability.

Granular activated carbon (GAC) was used as catalyst for the activation of peroxydisulfate (PDS) to decolorize and degrade Acid Orange 7 (AO7) in water. EPR spectra and radical quencher experiments were employed to identify the active species for AO7 oxidation in the PDS/GAC system. Linear sweep voltammetry (LSV) and chronoamperometry test were carried out to identify the contribution of nonradical mechanism for AO7 decay. The investigation of crucial operational parameters on the decolorization indicated 100 mg/L AO7 can be almost totally decolorized in a broad range of pH. Common inorganic anions adversely affect the AO7 decolorization process and the inhibition was in the order of: HCO3- > H2PO4- > SO42- > Cl- > NO3-. UV-vis spectra showed the destruction of the aromatic moiety of AO7 molecule during the oxidation reaction of the PDS/GAC system. The transformation of nitrogen related to the azo bond in AO7 molecule in this system was observed by monitoring the released N-containing inorganic ions. Recycle experiments showed GAC cannot be reused directly but its catalytic ability can be restored by using electrochemical method.