Showing papers by "Simon S. Park published in 2022"

Optimized 3D Bioprinting Technology Based on Machine Learning: A Review of Recent Trends and Advances

Abstract: The need for organ transplants has risen, but the number of available organ donations for transplants has stagnated worldwide. Regenerative medicine has been developed to make natural organs or tissue-like structures with biocompatible materials and solve the donor shortage problem. Using biomaterials and embedded cells, a bioprinter enables the fabrication of complex and functional three-dimensional (3D) structures of the organs or tissues for regenerative medicine. Moreover, conventional surgical 3D models are made of rigid plastic or rubbers, preventing surgeons from interacting with real organ or tissue-like models. Thus, finding suitable biomaterials and printing methods will accelerate the printing of sophisticated organ structures and the development of realistic models to refine surgical techniques and tools before the surgery. In addition, printing parameters (e.g., printing speed, dispensing pressure, and nozzle diameter) considered in the bioprinting process should be optimized. Therefore, machine learning (ML) technology can be a powerful tool to optimize the numerous bioprinting parameters. Overall, this review paper is focused on various ideas on the ML applications of 3D printing and bioprinting to optimize parameters and procedures.

Manipulating Active Sites of 2D Metal–Organic Framework Nanosheets with Fluorescent Materials for Enhanced Colorimetric and Fluorescent Ammonia Sensing

Abstract: 2D metal–organic frameworks (MOFs) offer high surface area and unique accessibility to active adsorption sites making them appealing for gas sensing applications. 2D MOFs‐based sensors are gaining traction for detecting hazardous flu‐gases such as ammonia selectively at low concentrations. Fluorescent and colorimetric sensing are promising techniques offering high sensitivity, selectivity, and rapid response in simple applications. In this work, Zn‐BTC is synthesized as 2D‐MOFs nanosheet with approximate thickness of 2.52 nm via a fast, facile, direct synthesis technique. The introduction of 8‐hydroxyquinoline during synthesis forms fluorescent compounds with zinc (ZnQ) which is encapsulated and decorated onto Zn‐BTC. Inherent charges on ZnQ lead to the agglomeration of multiple 2D‐flakes forming ZnQ@Zn‐BTC multi‐flaked nano‐discs. The synthesized material shows visible color change upon exposure to ammonia from white to ivory. In addition, selective fluorescence quenching is observed under ultraviolet illumination (λex = 365 nm) when ZnQ@Zn‐BTC is exposed to ammonia. The limit of detection reaches 0.27 ppm as a dried film for gaseous sensing and 60.8 nm in liquid phase fluorescence quenching, respectively. The observed high sensitivity and selectivity are attributed to the manipulation of active sites of 2D‐MOFs nanosheet with ZnQ. Functionalization also limits the degradation and breakdown of ZnQ@Zn‐BTC.

Wearable multifunctional soft sensor and contactless 3D scanner using supersonically sprayed silver nanowires, carbon nanotubes, zinc oxide, and PEDOT:PSS

Abstract: Abstract The multifunctional soft sensor developed here is capable of simultaneously sensing six stimuli, including pressure, bending strain, temperature, proximity, UV light, and humidity, with high accuracy and without interference among the respective built-in components. The sensor is fabricated via a facile, scalable, and cost-effective supersonic cold-spraying method using silver nanowires (AgNWs), carbon nanotubes (CNTs), zinc oxide (ZnO), and conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). A mask and laser cutter are used in conjunction with the supersonic cold-spraying method to produce miniaturized multifunctional sensors that can be readily installed on various substrates; for example, the production of gloves capable of multifunctional sensing. In particular, the proximity sensor of the multifunctional glove sensor can produce a three-dimensional (3D) image of a scanned object, showing high potential for use in military, medical, and industrial applications.

Identification and characterization of circulating immune complexes in IgA nephropathy

Abstract: The underlying pathology of immunoglobulin A (IgA) nephropathy (IgAN), the most common glomerulonephritis worldwide, is driven by the deposition of immune complexes containing galactose-deficient IgA1 [Tn(+)IgA1] in the glomerular mesangium. Here, we report that novel anti-Tn circulating immune complexes (anti-Tn CICs) contain predominantly IgM, representing large macromolecular complexes of ~1.2 megadaltons to several megadalton sizes together with Tn(+)IgA1 and some IgG. These complexes are significantly elevated in sera of patients with IgAN, which contains higher levels of complement C3, compared to healthy individuals. Anti-Tn CICs are bioactive and induce specific proliferation of human renal mesangial cells. We found that these anti-Tn CICs can be dissociated with small glycomimetic compounds, which mimic the Tn antigen of Tn(+)IgA1, releasing IgA1 from anti-Tn CICs. This glycomimetic compound can also significantly inhibit the proliferative activity of anti-Tn CICs of patients with IgAN. These findings could enhance both the diagnosis of IgAN and its treatment, as specific drug treatments are now unavailable.

CNN-Based Flow Pattern Identification Based On Flow-Induced Vibration Characteristics For Multiphase Flow Pipelines

Abstract: Structural health monitoring (SHM) techniques designed for single phase flow pipelines can be difficult to apply to multiphase flow pipelines. Data collected for pipeline SHM can be affected by intermittent changes in flow characteristics associated with flow patterns. Therefore, determining the flow pattern can help to better predict the unsteadiness of the flow parameters associated with pipeline integrity management. It is known that flow-induced vibration (FIV) in multiphase flow is highly correlated with flow pattern. In this paper, the characterization of FIV under various flow patterns is investigated experimentally, and a CNN-based flow pattern identification method is presented for horizontal gas-liquid pipe flow. Measurements were performed using two wall-mounted triaxial accelerometers with a high-speed camera, which simultaneously acquired both images and accelerometer signals. The difference of flow-induced vibration under various flow patterns can be explicitly shown by extracting morphological features using the Hilbert-Huang Transform (HHT) and Short Time Fourier Transform (STFT). In this paper, the HHT with 1st to 3rd IMFs is intended to emphasize the portion of FIV due to the unsteady fluctuations, while the STFT is intended to examine the FIV contributed to by both steady and unsteady fluctuations. To eliminate human interpretation errors, a convolutional neural network (CNN)-based machine learning model is built for the flow pattern identification task. To increase the size of the dataset used for flow pattern identification, the original time-series database is augmented by applying a series of data augmentation methods including sliding window, window stretch, denoising, and noise enhancement. These enable the flow pattern identification process to be more robust under various void fractions and flow geometries. The flow pattern identification results show that both HHT and STFT trained model have promising performance with overall accuracy above 97%. A comparison of identification results using HHT and STFT extracted morphological features from different accelerometer axes is performed. The results show that model trained by HHT images has a higher level of generalization.

Dynamic Stress Analysis of Multi-Section Curved Pipes Subjected to a Moving ILI Tool

Abstract: Pipelines are susceptible to degradation over time due to different types of defects caused by environmental and loading conditions. In-line inspection (ILI) is an assessment method widely used for pipeline degradation monitoring. The passage of an ILI tool through a section of a pipeline can generate significant dynamic stress within the pipe. Pipelines can pass through water, muskeg, excavated, or free-span sections, which provide less support. These partially-supported pipe segments are more prone to dynamic stress with the passage of an ILI tool. This research aims to study the effects of passing an ILI tool through multiple pipe bends in series constituted of both straight and curved segments. The passage of an ILI tool can excite a pipeline close to its natural frequencies where the amplitude of vibrations and consequently the dynamic stress increase rapidly. A 3D finite element (FE) model based on the Timoshenko beam theory is developed to model curved pipes subject to the passage of an ILI tool. Lab-scale experiments are performed to verify the results of the developed FE model. The developed model is further verified using finite element analysis (FEA) performed in ABAQUS™ Implicit. A comparison of the simulation and experimental results shows that the proposed model predicts the dynamic stress and displacements of multi-section pipe segments during the passage of an ILI tool effectively and accurately.

Manufacturing of Pitch Based Carbon Fibers through Microwave Treatments

Abstract: Carbon fibers (CFs) are characterized by their excellent mechanical properties including high tensile strength and elastic modulus. In terms of manufacturing of CFs, the conventional thermal treatment processes, including stabilization and carbonization, account for at least 46% of the cost, which drastically restricts the widespread application of CFs. This study investigates the use of mesophase pitch-based CFs from different synthetic approaches based on the conventional thermal post-treatment versus microwave hybrid post-treatment processes. CFs were studied by using SEM, FTIR, TGA, XRD, and mechanical properties characterization. Compared with the CFs prepared from the conventional thermal post-treatment process (CFs-T), those by the microwave thermal hybrid post-treatment process (CFs-M) show similar crystalline degree which is confirmed by XRD analysis. The tensile strength and modulus of the CFs-T are 0.290 and 38.6 GPa, and those of the CFs-M are 0.176 and 18.5 GPa, respectively. Microwave treatment is expected to be a much more energy- and time-efficient way for CFs post-treatment and can serve as a potential alternative to the conventional thermal post-treatment process used in carbon fiber manufacturing. Implications of the findings, including the oxygen diffusion, and crystalline structure are highlighted and discussed.