Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Co3O4/Ni(OH)2 composite mesoporous nanosheet networks as a promising electrode for supercapacitor applications

Abstract: Co3O4/Ni(OH)2 composite mesoporous nanosheet networks (NNs) grown on conductive substrates were synthesized by heat treatment of Co(OH)2/Ni(OH)2 NNs that were synthesized on Ti substrates by a facile electrochemical deposition route. The prepared samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), and micro-Raman spectroscopy. The above products were directly functionalized as supercapacitor electrodes without using any ancillary materials such as carbon black or binder. Co3O4/Ni(OH)2 composite mesoporous NNs achieved a high specific capacitance (Csp) of 1144 F g−1 at 5 mV s−1 and long-term cyclability. The electrochemical measurements showed Co3O4/Ni(OH)2 composite mesoporous NNs exhibited much better electrochemical performances than single Co3O4 or Ni(OH)2. The binary redox couples of Ni2+/Ni3+ and Co2+/Co3+, nanosheet networks with porous structures, the mesoporous structure within nanosheets, the interconnections among nanosheets, together with the excellent electrical contact with the current collector (substrate) are responsible for the improved electrochemical performances of Co3O4/Ni(OH)2 composite mesoporous NNs. With the ease of large scale fabrication and superior electrochemical characteristics, Co3O4/Ni(OH)2 composite mesoporous NNs grown on Ti substrates will be good candidates for supercapacitor applications.

Double‐Stranded Helices and Molecular Zippers Assembled from Single‐Stranded Coordination Polymers Directed by Supramolecular Interactions

Abstract: Using three nonlinear dicarboxylates, isophthalate (ipa), 4,4'-oxybis(benzoate) (oba), and ethylenedi(4-oxybenzoate) (eoba), we have prepared five neutral infinite copper(II) dicarboxylate coordination polymers containing lateral aromatic chelate ligands, namely [Cu(ipa)(2,2'-bpy)]n.2nH2O (1), [Cu2(ipa)2(phen)2H2O]n (2), [Cu(oba)(phen)]n (3), [Cu(oba)(2,2'-bpy)]n (4), and [Cu(eoba)(phen)]n (5; 2,2'-bpy = 2,2'-bipyridine, phen = 1,10-phenanthroline) by hydrothermal synthesis. X-ray single-crystal structural analyses of these complexes reveal that the nonlinear flexible or V-shaped dicarboxylates can induce the helicity or flexuousity of the polymeric chains and aromatic chelate ligands are important in providing potential supramolecular recognition sites for pi-pi aromatic stacking interactions. An appropriate combination of the bridging dicarboxylate and aromatic chelate can induce a pair of single-stranded helical or flexuous chains to generate a double-stranded helix or molecular zipper through supramolecular interactions, respectively.

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Abstract: Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.