Browsing by Author "Wang, Kaiwen"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Alexithymic Trait and Voluntary Control in Healthy Adults
    Gu, Xiaosi; Liu, Xun; Guise, Kevin G.; Fossella, John; Wang, Kaiwen; Fan, Jin (PLOS, 2008-11-12)
    Background: Alexithymia is a personality trait characterized by deficiency in understanding, processing, or describing emotions. Recent studies have revealed that alexithymia is associated with less activation of the anterior cingulate cortex, a brain region shown to play a role in cognitive and emotional processing. However, few studies have directly investigated the cognitive domain in relation to alexithymia to examine whether alexithymic trait is related to less efficient voluntary control. Methodology/ Principal Findings: We examined the relationship between alexithymic trait and voluntary control in a group of healthy volunteers. We used the 20-item Toronto Alexithymia Scale (TAS-20) to measure alexithymic trait. Additionally, we examined state and trait voluntary control using the revised Attention Network Test (ANT-R) and the Adult Temperament Questionnaire (ATQ), respectively. Alexithymic trait was positively correlated with the overall reaction time of the ANT-R, and negatively correlated with the Effortful Control factor of the ATQ. Conclusions/Significance: Our results suggest that alexithymic trait is associated with less efficient voluntary control.
  • Thumbnail Image
    Finite Element Modeling of Electrochemical Polishing of Niobium in Hydrofluoric-Sulfuric Acid Electrolyte
    Wang, Kaiwen; Cai, Wenjun; Tian, Hui; Reece, Charles E. (Electrochemical Soc Inc, 2022-06)
    Niobium (Nb) used in superconducting radio-frequency cavities requires smooth surface to achieve optimal performance. In this work, a finite element model that coupled electrochemistry, heat transfer, and fluid dynamics was developed to investigate the electrochemical polishing mechanisms of Nb, using experimentally measured polarization results of coupon samples as validations. The current and potential distribution, oxide growth kinetics of Nb in a complex cavity geometry was investigated as a function of temperature and coolant flow. A low temperature coolant with intermediate flow rate was found to reduce surface current and ensure oxide uniformity. These results could shed light on the design of future particle accelerators. (C) 2022 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited.
  • Thumbnail Image
    Multiphysics Modelling on the Effects of Composition and Microstructure during Tribocorrosion of Aluminum-based Metals and Structures
    Wang, Kaiwen (Virginia Tech, 2022-08-24)
    Wear and corrosion are two major threats to material integrity in multiple real-life circumstances, including oil and gas pipelines, marine and offshore infrastructures and transportations and biomedical implants. Furthermore, the synergistic effects between the two, named tribocorrosion, could cause, most of the time, severer material degradation to jeopardize materials' long-term sustainability and structural integrity. A representative case is aluminum (Al) and its alloys, which exhibit good corrosion resistance in aqueous solution due to the protection provided by the passive layer. However, these naturally formed layers are thin and delicate, leaving the materials vulnerable to simultaneous mechanical and corrosion damage, which in turn, compromise their resistance to tribocorrosion. Past research in tribocorrosion mainly relies on costly and trial-and-error experimental methods to study the materials' deformation and degradation under simultaneous wear and corrosion. In an attempt to predict tribocorrosion behavior using numerical analysis, this work developed a set of finite-element-based multiphysics models, in combination with experimental methods for parameter input and validation, focusing on different factors influencing the tribocorrosion behavior of materials. The first study developed a model with the coupling between strain and corrosion potential and investigated the effect of bulk material properties on tribocorrosion. This model was validated by existing tribocorrosion experiments of two Al-Mn alloys, to analyze the synergistic effects of mechanical and corrosion properties on the material degradation mechanisms of tribocorrosion. During consecutive passes of the counter body, significant residual stress was found to develop near the edge of the wear track, leading to highly concentrated corrosion current than elsewhere. Such non-uniform surface corrosion and stress-corrosion coupling led to variations of tribocorrosion rate over time, even though testing conditions were kept constant. Tribocorrosion rate maps were generated to predict material loss as a function of different mechanical and electrochemical properties, indicating a hard, complaint metal with high anodic Tafel slope and low exchange current density is most resistant to tribocorrosion. Secondly, the influence of microstructural design on the tribocorrosion behavior of Al-based nanostructured metallic multilayers (NMMs) was investigated computationally. Specifically, this model accounts for elastic-plastic mechanical deformation during wear and galvanic corrosion between exposed inner layers after wear. The effects of individual layer thickness (from 10 to 100 nm) and layer orientation (horizontally and vertically aligned) on the tribocorrosion behavior of Al/Cu NMMs was studied. Both factors were found to affect the subsurface stress and plastic strain distribution and localized surface corrosion kinetics, hence affecting the overall tribocorrosion rate. This model and the obtained understanding could shed light on future design and optimization strategies of NMMs against tribocorrosion. Finally, a combined experimental and computational investigation of the crystallographic effect using Al (100), (110), and (111) single crystals as model systems, to understand the effects of crystallographic orientation on the tribocorrosion kinetics by combining tribocorrosion experiments, materials characterization, and multiphysics modeling. EBSD was exploited to characterize the crystal orientation and dislocation density of the worn samples. The tribocorrosion model was built based on the results of EBSD characterization with the coupling effect of crystal orientation and corrosion. The model successfully predicted the overall tribocorrosion current of single-crystal samples, indicating the important role played by crystal orientation and dislocation density in the acceleration of corrosion.
  • Thumbnail Image
    Partitioned Active Learning for Heterogeneous Systems
    Lee, Cheolhei; Wang, Kaiwen; Wu, Jianguo; Cai, Wenjun; Yue, Xiaowei (ASME, 2023-08)
    Active learning is a subfield of machine learning that focuses on improving the data collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate modeling facilitates cost-efficient analysis of demanding engineering systems, while the existence of heterogeneity in underlying systems may adversely affect the performance. In this article, we propose the partitioned active learning that quantifies informativeness of new design points by circumventing heterogeneity in systems. The proposed method partitions the design space based on heterogeneous features and searches for the next design point with two systematic steps. The global searching scheme accelerates exploration by identifying the most uncertain subregion, and the local searching utilizes circumscribed information induced by the local Gaussian process (GP). We also propose Cholesky update-driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method consistently outperforms existing active learning methods in three real-world cases with better prediction and computation time.
  • Thumbnail Image
    Spatially expandable fiber-based probes as a multifunctional deep brain interface
    Jiang, Shan; Patel, Dipan C.; Kim, Jongwoon; Yang, Shuo; Mills, William A. II; Zhang, Yujing; Wang, Kaiwen; Feng, Ziang; Vijayan, Sujith; Cai, Wenjun; Wang, Anbo; Guo, Yuanyuan; Kimbrough, Ian F.; Sontheimer, Harald; Jia, Xiaoting (Nature Research, 2020)
    Understanding the cytoarchitecture and wiring of the brain requires improved methods to record and stimulate large groups of neurons with cellular specificity. This requires miniaturized neural interfaces that integrate into brain tissue without altering its properties. Existing neural interface technologies have been shown to provide high-resolution electrophysiological recording with high signal-to-noise ratio. However, with single implantation, the physical properties of these devices limit their access to one, small brain region. To overcome this limitation, we developed a platform that provides three-dimensional coverage of brain tissue through multisite multifunctional fiber-based neural probes guided in a helical scaffold. Chronic recordings from the spatially expandable fiber probes demonstrate the ability of these fiber probes capturing brain activities with a single-unit resolution for long observation times. Furthermore, using Thy1-ChR2-YFP mice we demonstrate the application of our probes in simultaneous recording and optical/chemical modulation of brain activities across distant regions. Similarly, varying electrographic brain activities from different brain regions were detected by our customizable probes in a mouse model of epilepsy, suggesting the potential of using these probes for the investigation of brain disorders such as epilepsy. Ultimately, this technique enables three-dimensional manipulation and mapping of brain activities across distant regions in the deep brain with minimal tissue damage, which can bring new insights for deciphering complex brain functions and dynamics in the near future.