In vitro corrosion of Mg–1.21Li–1.12Ca–1Y alloy

AbstractThe influence of the microstructure on mechanical properties and corrosion behavior of the Mg–1.21Li–1.12Ca–1Y alloy was investigated using OM, SEM, XRD, EPMA, EDS, tensile tests and corrosion measurements. The results demonstrated that the microstructure of the Mg–1.21Li–1.12Ca–1Y alloy was characterized by α-Mg substrate and intermetallic compounds Mg2Ca and Mg24Y5. Most of the fine Mg2Ca particles for the as-cast alloy were distributed along the grain boundaries, while for the as-extruded along the extrusion direction. The Mg24Y5 particles with a larger size than the Mg2Ca particles were positioned inside the grains. The mechanical properties of Mg–1.21Li–1.12Ca–1Y alloy were improved by the grain refinement and dispersion strengthening. Corrosion pits initiated at the α-Mg matrix neighboring the Mg2Ca particles and subsequently the alloy exhibited general corrosion and filiform corrosion as the corrosion product layer of Mg(OH)2 and MgCO3 became compact and thick

Corrosion resistance of a magnesium hydroxide/stearic acid composite coating fabricated by vapor diffusion method on Mg-Li-Ca alloy

A composite coating of magnesium hydroxide/stearic acid on surface of Mg-1Li-1Ca alloy was prepared using vapor diffusion method to improve the corrosion resistance. The surface morphology and chemical composition of the films were characterized by high resolution scanning electron microscopy (HRSEM), Fourier transform infrared spectrometer (FTIR) and X-ray diffraction analysis (XRD). Electrochemical experiments and immersion test were conducted to study corrosion resistance of the obtained composite coating.The formation and degradation mechanisms of the composite coating were proposed.The results show that the magnesium hydroxide coating has a petal-like structure and is well-adhered to the surface of Mg-1Li-1Ca alloy.The stearic acid coating does not change the petal-like structure of the magnesium hydroxide coating. However, the stearic acid can effectively prevent the aggressive medium penetrating into the internal coating due to its low surface energy and good hydrophobicity.The corrosion current density of the magnesium hydroxide/stearic acid (Mg (OH) 2/SA) composite coating (1.45 × 10–7A/cm2) is decreased by two orders of magnitude compared to that of the Mg-1Li-1Ca alloy substrate (2.70 × 10–5 A/cm2), leading to the enhanced corrosion resistance of magnesium alloy

Influence of microstructure on tensile properties and fatigue crack growth in extruded magnesium alloy AM60

The microstructure, mechanical properties and fatigue crack propagation (FCP) of extruded magnesium alloy AM60 were investigated and compared with rolled AM60. The extruded AM60 has an inhomogeneous microstructure characterized by alpha-matrix, beta phases and Al-Mn precipitates and denuded zones as well, whereas rolled AM60 has fine grains. The change in strain-hardening exponent of extruded AM60 with strain rate is ascribed to inhomogeneous microstructure. In situ double twinning: (10 (1) over bar2) - (01 (1) over bar2) and {10 (1) over bar1} - {10 (1) over bar2} occurred during FCP of extruded alloy. Its crack initiation and growth are related to slip bands, double twinning and intermetallic compounds. Small cracks resulted from oxide and intermetallic compounds in rolled AM60 may be responsible for oscillatory crack growth and crack arrest. Extruded AM60 has a slower FCP rate than rolled one. (C) 2009 Elsevier Ltd. All rights reserved

Antibacterial Activity and Biocompatibility of Ag-Montmorillonite/Chitosan Colloidal Dressing in a Skin Infection Rat Model: An In Vitro and In Vivo Study

(1) Background: Traditional dressings can only superficially cover the wound, they have widespread issues with inadequate bacterial isolation and liquid absorption, and it is simple to inflict secondary wound injury when changing dressings. Therefore, it is crucial for wound healing to develop a new kind of antimicrobial colloidal dressing with good antibacterial, hygroscopic, and biocompatible qualities. (2) Methods: Ag-montmorillonite/chitosan (Ag-MMT/CS) colloid, a new type of antibacterial material, was prepared from two eco-friendly materials—namely, montmorillonite and chitosan—as auxiliary materials, wherein these materials were mixed with the natural metal Ag, which is an antibacterial agent. The optimum preparation technology was explored, and Ag-MMT/CS was characterized. Next, Staphylococcus aureus, which is a common skin infection bacterium, was considered as the experimental strain, and the in vitro antibacterial activity and cytocompatibility of the Ag-MMT/CS colloid were investigated through various experiments. Subsequently, a rat skin infection model was established to explore the in vivo antibacterial effect. (3) Results: In vitro studies revealed that the Ag-MMT/CS colloid had a good antibacterial effect on S. aureus, with an inhibition zone diameter of 18 mm and an antibacterial rate of 99.18%. After co-culture with cells for 24 h and 72 h, the cell survival rates were 88% and 94%, respectively. The cells showed normal growth and proliferation, and no evident dead cells were observed under the laser confocal microscope. After applying the colloid to the rat skin infection model, the Ag-MMT/CS treatment group exhibited faster wound healing and better local exudation and absorption in the wound than the control group, suggesting that the Ag-MMT/CS colloid exhibited a better antibacterial effect on the S. aureus. (4) Conclusions: Ag+, chitosan, and MMT present in the Ag-MMT/CS colloid dressing exert synergistic effects, and it has good antibacterial effects, cytocompatibility, and hygroscopicity, indicating that this colloid has the potential to become a next-generation clinical antibacterial dressing

In Vitro Degradation and Photoactivated Antibacterial Activity of a Hemin-CaP Microsphere-Loaded Coating on Pure Magnesium

Photoactivated sterilization has received more attention in dealing with implant-associated infections due to its advantages of rapid and effective bacteriostasis and broad-spectrum antibacterial activity. Herein, a micro-arc oxidation (MAO)/polymethyltrimethoxysilane (PMTMS)@hemin-induced calcium-bearing phosphate microsphere (Hemin-CaP) coating was prepared on pure magnesium (Mg) via MAO processing and dipping treatments. The morphology and composition of the coating were characterized via scanning electron microscopy, Fourier transform infrared spectrometer, X-ray diffractometer and X-ray photoelectron spectrometer. Corrosion behavior was evaluated through electrochemical and hydrogen evolution tests. The release of Fe3+ ions at different immersion times was measured with an atomic absorption spectrophotometer. Antibacterial performance and cytotoxicity were assessed using the spread plate method, MTT assay and live/dead staining experiment. The results showed that the corrosion current density of the MAO/PMTMS@(Hemin-CaP) coating (4.41 × 10−8 A·cm−2) was decreased by two orders of magnitude compared to that of pure Mg (3.12 × 10−6 A·cm−2). Photoactivated antibacterial efficiencies of the Hemin-CaP microspheres and MAO/PMTMS@(Hemin-CaP) coating reached about 99% and 92%, respectively, which we attributed to the photothermal and photodynamic properties of hemin with a porphyrin ring. Moreover, based on the release of Fe3+ ions, the MC3T3-E1 pre-osteoblasts’ viability reached up to 125% after a 72 h culture, indicating a positive effect of the coating in promoting cell growth. Thus, this novel composite coating holds a promising application as bone implants

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction