A Review on Manufacturing and Post-Processing Technology of Vascular Stents

Percutaneous coronary intervention (PCI) with stent implantation is one of the most effective treatments for cardiovascular diseases (CVDs). However, there are still many complications after stent implantation. As a medical device with a complex structure and small size, the manufacture and post-processing technology greatly impact the mechanical and medical performances of stents. In this paper, the development history, material, manufacturing method, and post-processing technology of vascular stents are introduced. In particular, this paper focuses on the existing manufacturing technology and post-processing technology of vascular stents and the impact of these technologies on stent performance is described and discussed. Moreover, the future development of vascular stent manufacturing technology will be prospected and proposed.

Editorial for the Special Issue on Advanced Energy Conversion and Storage Microdevices

Advanced energy conversion and storage systems have attracted much attention in recent decades due to the increasing demand for energy and the environmental impacts of non-sustainable energy resources [...]

Microfluidic Approaches and Methods Enabling Extracellular Vesicle Isolation for Cancer Diagnostics

Advances in cancer research over the past half-century have clearly determined the molecular origins of the disease. Central to the use of molecular signatures for continued progress, including rapid, reliable, and early diagnosis is the use of biomarkers. Specifically, extracellular vesicles as biomarker cargo holders have generated significant interest. However, the isolation, purification, and subsequent analysis of these extracellular vesicles remain a challenge. Technological advances driven by microfluidics-enabled devices have made the challenges for isolation of extracellular vesicles an emerging area of research with significant possibilities for use in clinical settings enabling point-of-care diagnostics for cancer. In this article, we present a tutorial review of the existing microfluidic technologies for cancer diagnostics with a focus on extracellular vesicle isolation methods.

Thermodynamic Modeling of Solvent-Assisted Lipid Bilayer Formation Process

The solvent-assisted lipid bilayer (SALB) formation method provides a simple and efficient, microfluidic-based strategy to fabricate supported lipid bilayers (SLBs) with rich compositional diversity on a wide range of solid supports. While various studies have been performed to characterize SLBs formed using the SALB method, relatively limited work has been carried out to understand the underlying mechanisms of SALB formation under various experimental conditions. Through thermodynamic modeling, we studied the experimental parameters that affect the SALB formation process, including substrate surface properties, initial lipid concentration, and temperature. It was found that all the parameters are critically important to successfully form high-quality SLBs. The model also helps to identify the range of parameter space within which conformal, homogeneous SLBs can be fabricated, and provides mechanistic guidance to optimize experimental conditions for lipid membrane-related applications.

A Novel Bidirectional AlGaN/GaN ESD Protection Diode

Despite the superior working properties, GaN-based HEMTs and systems are still confronted with the threat of a transient ESD event, especially for the vulnerable gate structure of the p-GaN or MOS HEMTs. Therefore, there is still an urgent need for a bidirectional ESD protection diode to improve the ESD robustness of a GaN power system. In this study, an AlGaN/GaN ESD protection diode with bidirectional clamp capability was proposed and investigated. Through the combination of two floating gate electrodes and two pF-grade capacitors connected in parallel between anode or cathode electrodes and the adjacent floating gate electrodes (CGA (CGC)), the proposed diode could be triggered by a required voltage and possesses a high secondary breakdown current (IS) in both forward and reverse transient ESD events. Based on the experimental verification, it was found that the bidirectional triggering voltages (Vtrig) and IS of the proposed diode were strongly related to CGA (CGC). With CGA (CGC) increasing from 5 pF to 25 pF, Vtrig and IS decreased from ~18 V to ~7 V and from ~7 A to ~3 A, respectively. The diode’s high performance demonstrated a good reference for the ESD design of a GaN power system.

Minimized Triple-Band Eight-Element Antenna Array for 5G Metal-frame Smartphone Applications

A multiple-input-multiple-output (MIMO) antenna array for triple-band 5G metal-frame smartphone applications is proposed in this paper. Each single antenna element consists of an S-shaped feeding strip and an L-shaped radiation strip on the metal frame. The dimension of the antenna element is only 6.5 mm × 7 mm (0.076 λ0 × 0.082 λ0, λ0 is the free-space wavelength at the frequency of 3.5 GHz). The −6 dB impedance bandwidth of the proposed eight-antenna array can cover 3.3–3.8 GHz, 4.8–5 GHz, and 5.15–5.925 GHz. The evolution design and the analysis of the optimal parameters for a single antenna element are derived to investigate the principle of the antenna. The measured total efficiency is larger than 70%. The measured isolation is better than 13 dB. The measurements of the prototype agree well with the simulation results.

An Analytical Energy Harvester Model for Interdigitated Ring Electrode on Circular Elastic Membrane

Energy harvesters are devices that accumulate ambient vibrational energy from the environment, and for the time being, variable capacitance is the most widely used mechanism. Various designs were proposed to increase the power of such devices, and in particular, the interdigitated electrode (IDE) pattern is the mainstream. Nevertheless, most IDE designs focus merely on the parallel-type vibrations of electrodes. In this study, the performance of a novel harvester, which combined circular membrane and interdigitated ring electrodes (IRE), was investigated. This design allows the device to collect energy from the rotational structure motions of electrodes through the vibrating membrane. Besides, the circular structure provides a dense capacitive arrangement that is higher than that of the arrangement obtained using regular rectangular chips. The IRE diagram is composed of many capacitive rings, each of which harvests vibrated energy simultaneously. Three gaps (1, 10, and 100 μm) of the ring are investigated for the first four vibrational modes of the membrane to understand the effect of energy output. It is found that the energy outputs are approximately the same for the three gaps; however, rings with a wider gap are easier to manufacture in MEMS.

Quality Prediction and Control in Wire Arc Additive Manufacturing via Novel Machine Learning Framework

Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.

Estimation of Position and Size of a Contaminant in Aluminum Casting Using a Thin-Film Magnetic Sensor

Advanced manufacturing processes require an in-line full inspection system. A nondestructive inspection system able to detect a contaminant such as tool chipping was utilized for the purpose of detecting a defective product as well as damaged machine tools used to fabricate the product. In a previous study, a system able to detect magnetic tool steel chipping in conductive material such as aluminum was developed and tested. In this study, a method of position and size estimation for magnetic chipping was investigated and is described. An experimental confirmation of the proposed method was also carried out using an actual prototype system.

Dynamic Modeling and Experimental Validation of a Water Hydraulic Soft Manipulator Based on an Improved Newton—Euler Iterative Method

Compared with rigid robots, soft robots have better adaptability to the environment because of their pliability. However, due to the lower structural stiffness of the soft manipulator, the posture of the manipulator is usually decided by the weight and the external load under operating conditions. Therefore, it is necessary to conduct dynamics modeling and movement analysis of the soft manipulator. In this paper, a fabric reinforced soft manipulator driven by a water hydraulic system is firstly proposed, and the dynamics of both the soft manipulator and hydraulic system are considered. Specifically, a dynamic model of the soft manipulator is established based on an improved Newton–Euler iterative method, which comprehensively considers the influence of inertial force, elastic force, damping force, as well as combined bending and torsion moments. The dynamics of the water hydraulic system consider the effects of cylinder inertia, friction, and water response. Finally, the accuracy of the proposed dynamic model is verified by comparing the simulation results with the experimental data about the steady and dynamic characteristics of the soft manipulator under various conditions. The results show that the maximum sectional error is about 0.0245 m and that the maximum cumulative error is 0.042 m, which validate the effectiveness of the proposed model.