Poor compatibility of polymer/ceramic composites used as high-pulse capacitors with high permittivity suffers from the reduced breakdown strength ( E) and lowered energy density ( U). Herein, mussel-inspired poly(dopamine) (PDA)-modified BaSrTiO (mBST) nanoparticle and poly(vinylidene fluoride) (PVDF) matrix are bonded together to fabricate nanocomposites with a cross-linked network and enhanced compatibility. The significantly improved E of 466 MV/m and the highest U of 11.0 J/cm for PVDF-based polymer/BST composites have been obtained. By comparing the properties of the three series of composites with different structures, the contribution of ferroelectric relaxation, interface polarization, and leakage conduction to the dielectric loss has been well addressed. Notably, the surface modification of BST with PDA could remarkably enhance the compatibility of the two components and the structural homogeneity of the composite. The improved bonding between the polymer matrix and the filler chemically or physically is responsible for the reduced dielectric loss from both conduction loss and interfacial polarization, which is the key to improve the E, U, and η of the composite. It has been revealed that enhancing the homogeneity of the composites by modifying ceramics and constructing cross-linked networks between the polymer matrix and the filler might be a facile strategy to achieve high energy storage performance in polymer composites.

BackgroundTo detect the expression of isocitrate dehydrogenase 1 (IDH1) and transformation-related protein 53 (p53) in osteosarcoma and analyze the correlation between them and the clinico-pathological features.MethodsThe expressions of IDH1 and p53 were detected in human osteosarcoma cell lines (MG-63 and U2OS) by immunocytochemistry, Real-time PCR and Western Blotting. The expressions of IDH1 and p53 in formalin-fixed paraffin-embedded tissue sections from 44 osteosarcoma patients were determined by immunohistochemistry, and the correlation between them and clinicopagthological features were analyzed. None of these patients received chemotherapy prior to surgery.ResultsIDH1 is detected in osteosarcoma cell lines and biopsies. IDH1 expresses higher in U2OS cells with wild type p53 than in MG-63 cells with mutation p53. IDH1 correlates with histological Rosen grade and metastasis negatively. P53 correlates with histological Rosen grade, metastasis and overall survival in clinical osteosarcoma biopsies. Osteosarcoma patients with High IDH1 expression have a very high p53 expression.ConclusionIDH1 may correlate with p53 and be a candidate biomarker for osteosarcoma correlate with histological Rosen grade and metastasis.

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An intelligent algorithm was developed based on backpropagation artificial neural network for the acoustic emission source localization. The established and trained methods of the algorithm were stated with the time difference of arrival detected by a fiber optic acoustic emission sensor array and the coordinates of acoustic emission source. The response characteristic of fiber optic acoustic emission sensor was calibrated with the commercial piezoelectric ceramic transducer (PZT) acoustic emission sensor, which provided that the fiber optic acoustic emission sensor was better suited to detect the low frequency of stress wave than the PZT sensor. Four fiber optic acoustic emission sensors were deployed in a square array in an aluminum plate for comparisons between different algorithms of source localization. Comparison results of acoustic emission source location provided that the intelligent algorithm improved the accuracy by reducing the nonlinear errors. For the anisotropic materials, a sensor array deployed in a diamond pattern was adopted. The velocities of stress waves in orthogonal directions were measured as the basic performance for both algorithms of source localization. Four sensors were integrated into a carbon fiber–reinforced polymer plate as a perfect structure for locating the acoustic emission source impacting on its surface. The experiment results provide that the maximum error is only 6.3 mm using the intelligent algorithm.