Zwitterionic coating assisted by dopamine with metal-phenolic networks loaded on titanium with improved biocompatibility and antibacterial property for artificial heart

Introduction: Titanium (Ti) and Ti-based alloy materials are commonly used to develop artificial hearts. To prevent bacterial infections and thrombus in patients with implanted artificial hearts, long-term prophylactic antibiotics and anti-thrombotic drugs are required, and this may lead to health complications. Therefore, the development of optimized antibacterial and antifouling surfaces for Ti-based substrate is especially critical when designing artificial heart implants.Methods: In this study, polydopamine and poly-(sulfobetaine methacrylate) polymers were co-deposited to form a coating on the surface of Ti substrate, a process initiated by Cu2+ metal ions. The mechanism for the fabrication of the coating was investigated by coating thickness measurements as well as Ultraviolet–visible and X-ray Photoelectron (XPS) spectroscopy. Characterization of the coating was observed by optical imaging, scanning electron microscope (SEM), XPS, atomic force microscope (AFM), water contact angle and film thickness. In addition, antibacterial property of the coating was tested using Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) as model strains, while the material biocompatibility was assessed by the antiplatelet adhesion test using platelet-rich plasma and in vitro cytotoxicity tests using human umbilical vein endothelial cells and red blood cells.Results and discussion: Optical imaging, SEM, XPS, AFM, water contact angle, and film thickness tests demonstrated that the coating was successfully deposited on the Ti substrate surface. The biocompatibility and antibacterial assays showed that the developed surface holds great potential for improving the antibacterial and antiplatelet adhesion properties of Ti-based heart implants

Biodegradable polycarbonates from lignocellulose based 4-pentenoic acid and carbon dioxide

The production of biodegradable polycarbonate by copolymerizing CO2 with epoxides has emerged as an effective method to utilize CO2 in response to growing concerns about CO2 emissions and plastic pollution. Previous studies have mainly focused on the preparation of CO2-based polycarbonates from petrochemical-derived propylene oxide (PO) or cyclohexene oxide (CHO). However, to reduce dependence on fossil fuels, the development of 100% bio-based polymers has gained attention in polymer synthesis. Herein, we reported the synthesis of glycidyl 4-pentenoate (GPA) from lignocellulose based 4-pentenoic acid (4-PA), which was further copolymerized with CO2 using a binary catalyst SalenCoCl/PPNCl to produce bio-based polycarbonates with vinyl side chains and molecular weights up to 17.1 kg/mol. Introducing a third monomer, PO, allows for the synthesis of the GPA/PO/CO2 terpolymer, and the glass transition temperature (Tg) of the terpolymer can be adjusted from 2°C to 19°C by controlling the molar feeding ratio of GPA to PO from 7:3 to 3:7. Additionally, post-modification of the vinyl side chains enables the production of functional polycarbonates, providing a novel approach to the preparation of bio-based materials with diverse side chains and functions

Diagenesis of the first member of Canglangpu Formation of the Cambrian Terreneuvian in northern part of the central Sichuan Basin and its influence on porosity

In this paper, taking the first Member of the Canglangpu Formation of the Cambrian Terreneuvian in the northern central Sichuan Basin as an example, the diagenesis and its influence on porosity are systemically studied based on the observations and identifications of cores, casts and cathodoluminescence thin sections. The results show that the rock types of the first member of Canglangpu Formation are various, including mixed rocks, carbonate rocks and clastic rocks. The specific lithology is dominated by sand-bearing oolitic dolomite, sandy oolitic dolomite, sparry oolotic dolomite and fine-grained detrital sandstone. At the same time, the Cang 1 Member has experienced five types of diagenetic environments, including seawater, meteoric water, evaporative seawater, shallow burial, and medium-deep burial diagenetic environments. Moreover, the main diagenetic processes under different diagenetic environments include cementation, dissolution, compaction, chemical compaction, dolomitization and structural fractures. According to the analysis, fabric-selective dissolution in meteoric water diagenetic environment, dolomitization in evaporative seawater environment, and non-fabric-selective dissolution, dolomitization and structural fractures in buried diagenetic environment are beneficial to the development of pores. However, cementation, compaction and chemical compaction in medium and deep burial environments, are unfavorable for the development of pores

How generative adversarial networks promote the development of intelligent transportation systems: A survey

In current years, the improvement of deep learning has brought about tremendous changes: As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) have been widely employed in various fields including transportation. This paper reviews the development of GANs and their applications in the transportation domain. Specifically, many adopted GAN variants for autonomous driving are classified and demonstrated according to data generation, video trajectory prediction, and security of detection. To introduce GANs to traffic research, this review summarizes the related techniques for spatio-temporal, sparse data completion, and time-series data evaluation. GAN-based traffic anomaly inspections such as infrastructure detection and status monitoring are also assessed. Moreover, to promote further development of GANs in intelligent transportation systems (ITSs), challenges and noteworthy research directions on this topic are provided. In general, this survey summarizes 130 GAN-related references and provides comprehensive knowledge for scholars who desire to adopt GANs in their scientific works, especially transportation-related tasks

Tesseral superconducting shim coil design with quasi-saddle geometry for use in high-field magnet system

Superconducting shim coils are frequently used in high-field magnetic resonance imaging or nuclear magnetic resonance system for their high sensitivity and shimming strength. The design of superconducting shim coils is based on the spherical harmonic decomposition, and each shim coil is normally dedicated for correcting one specific harmonic component. Conventional superconducting shim coil with a saddle loop has observable winding error near the corner, which gives rise to arc transformation when winding layer by layer. Simulation analysis shows that the arc corner transformation will induce the magnetic field deviation by more than double of the theoretical design ±1%, which may be up to ±3% after real winding. An improved shim coil design method with a quasisaddle geometry was proposed to correct the winding error. With the consideration of both the rounded corner of the saddle loop and the arc side, the new design offers the magnetic field deviation within ±1%. In addition to reducing the winding error, the proposed design also facilitates the winding process

Highly shielded gradient coil design for a superconducting planar MRI system

In a planar, superconducting magnetic resonance imaging (MRI) system, the gradient assembly is placed into the groove of the SC magnet. Conventional gradient coil design method considers the shielding of pole face only, but neglects the surrounding metal structures at the coil side, thus leading to large stray field leakage and resulting in serious eddy current artefact. A novel coil shielding method was proposed in this work by a full consideration of the stray fields on the pole face and also the coil ends. The gradient coil design exemplification of a 0.7T planar superconducting MRI system was presented. In the new design, the maximum stray field at the surface of the ambient structure was reduced more than six times for the transverse coils and around four times for the longitudinal coil. The highly shielded gradient coil also produced linear gradient fields

A novel mixed integer programming scheme for passive shimming in MRI

In a magnetic resonance imaging system, the magnetic field homogeneity plays a decisive role on the image quality. As an effective technique, passive shimming (PS) uses ferromagnetic material (shim pieces) to eliminate the field impurity in the imaging area. In existing PS algorithms, most attempt to efficiently find continuous solutions using standard linear programming (LP). However, discrete solution has to be used in shimming practice. Direct rounding off of the continuous solution may lead to an unacceptable homogeneity loss. In theory, integer programming (IP) can be used to find discrete solution, however, it usually requires a huge amount of computing time, and the optimization procedure can hardly to converge. In this work, a mixed integer programming scheme was proposed, which combines the LP and IP algorithms to find the practical shimming solution. In the hybrid optimization algorithms, the first step is to obtain the initial continuous solution using an improved linear programming model, which not only minimize the peak-peak field error, but also seek the optimal target magnetic field. The second step is to re-optimize those shim pieces having a significant influence on the field homogeneity using an integer linear programming model. In the established optimization model (step 1), the LP algorithm guarantees a global minimum solution for the given problem, without extra computational parameters involved in the transition from first-step to second-step optimization. Compared with other integer programming strategies, the hybrid algorithm can greatly save the computing time and maintain excellent shimming results achieved by continuous solution. This has been demonstrated by a shimming study for the 1.5 T superconducting magnets

Sidechain Metallopolymers with Precisely Controlled Structures: Synthesis and Application in Catalysis

Inspired by the cooperative multi-metallic activation in metalloenzyme catalysis, artificial enzymes as multi-metallic catalysts have been developed for improved kinetics and higher selectivity. Previous models about multi-metallic catalysts, such as cross-linked polymer-supported catalysts, failed to precisely control the number and location of their active sites, leading to low activity and selectivity. In recent years, metallopolymers with metals in the sidechain, also named as sidechain metallopolymers (SMPs), have attracted much attention because of their combination of the catalytic, magnetic, and electronic properties of metals with desirable mechanical and processing properties of polymeric backbones. Living and controlled polymerization techniques provide access to SMPs with precisely controlled structures, for example, controlled degree of polymerization (DP) and molecular weight dispersity (Đ), which may have excellent performance as multi-metallic catalysts in a variety of catalytic reactions. This review will cover the recent advances about SMPs, especially on their synthesis and application in catalysis. These tailor-made SMPs with metallic catalytic centers can precisely control the number and location of their active sites, exhibiting high catalytic efficiency

H∞ design of 2D controller for batch processes with uncertainties and interval time-varying delays

Based on robust feedback incorporated with iterative learning control scheme, this paper proposes a 2D controller design for a kind of batch process with uncertainties and interval time-varying delay. The batch process is first transformed into a two-dimensional Fornasini-Marchsini (2D-FM) model with a delay varying in a range. Then the design of 2D controller scheme is cast as a robust H∞ control problem for uncertain 2D systems. Based on the 2D control system theory, a design framework of 2D controller is formulated, which consists of two control actions: a robust feedback control for ensuring the performances over time and a P-type iterative learning control (P-type ILC) for improving the tracking performance from cycle to cycle. The proposed control law can guarantee closed-loop convergence along both the time and the cycle directions to satisfy H∞ performance. Conditions for the existence of the proposed 2D control design scheme are given in terms of linear matrix inequalities. The minimum H∞ norm bound γ is obtained by solving linear objective optimization problems. The generality of the proposed design method is shown by the results converted to constant delay case. Application to injection velocity control demonstrates the effectiveness and advantages of the proposed approach. © 2013 Elsevier Ltd