Showing papers by "National Taiwan University of Science and Technology published in 2020"

Polyamide nanofiltration membrane with highly uniform sub-nanometre pores for sub-1 Å precision separation.

Abstract: Separating molecules or ions with sub-Angstrom scale precision is important but technically challenging. Achieving such a precise separation using membranes requires Angstrom scale pores with a high level of pore size uniformity. Herein, we demonstrate that precise solute-solute separation can be achieved using polyamide membranes formed via surfactant-assembly regulated interfacial polymerization (SARIP). The dynamic, self-assembled network of surfactants facilitates faster and more homogeneous diffusion of amine monomers across the water/hexane interface during interfacial polymerization, thereby forming a polyamide active layer with more uniform sub-nanometre pores compared to those formed via conventional interfacial polymerization. The polyamide membrane formed by SARIP exhibits highly size-dependent sieving of solutes, yielding a step-wise transition from low rejection to near-perfect rejection over a solute size range smaller than half Angstrom. SARIP represents an approach for the scalable fabrication of ultra-selective membranes with uniform nanopores for precise separation of ions and small solutes. Separating molecules or ions with sub-Angstrom scale precision is important but technically challenging. Here, the authors demonstrate that precise solute-solute separation can be achieved using polyamide membranes formed via surfactant-assembly regulated interfacial polymerization.

Multiple-Attribute Group Decision-Making Based on q-Rung Orthopair Fuzzy Power Maclaurin Symmetric Mean Operators

Abstract: To be able to describe more complex fuzzy uncertainty information effectively, the concept of ${q}$ -rung orthopair fuzzy sets ( ${q}$ -ROFSs) was first proposed by Yager. The ${q}$ -ROFSs can dynamically adjust the range of indication of decision information by changing a parameter ${q}$ based on the different hesitation degree from the decision-makers, where ${q} {\ge } {1}$ , so they outperform the traditional intuitionistic fuzzy sets and Pythagorean fuzzy sets. In real decision-making problems, there is often an interaction phenomenon between attributes. For aggregating these complex fuzzy information, the Maclaurin symmetric mean (MSM) operator is more superior by considering interrelationships among attributes. In addition, the power average (PA) operator can reduce the effects of extreme evaluating data from some experts with prejudice. In this paper, we introduce the PA operator and the MSM operator based on ${q}$ -rung orthopair fuzzy numbers ( ${q}$ -ROFNs). Then, we put forward the ${q}$ -rung orthopair fuzzy power MSM ( ${q}$ -ROFPMSM) operator and the ${q}$ -rung orthopair fuzzy power weighed MSM ( ${q}$ -ROFPWMSM) operator of ${q}$ -ROFNs and present some of their properties. Finally, we present a novel multiple-attribute group decision-making (MAGDM) method based on the ${q}$ -ROFPWA and the ${q}$ -ROFPWMSM operators. The experimental results show that the novel MAGDM method outperforms the existing MAGDM methods for dealing with MAGDM problems.

Adaptive Fuzzy Control With High-Order Barrier Lyapunov Functions for High-Order Uncertain Nonlinear Systems With Full-State Constraints

Abstract: This paper focuses on the practical output tracking control for a category of high-order uncertain nonlinear systems with full-state constraints. A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems. By the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high-order uncertain nonlinear system with full-state constraints. A novel controller is designed to ensure that the tracking errors approach to an arbitrarily small neighborhood of zero, and the constraints on system states are not violated. The numerical example demonstrates effectiveness of the proposed control method.

Vision, challenges, roles and research issues of Artificial Intelligence in Education

Abstract: The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) technologies or application programs in educational settings to facilitate teaching, learning, or decision making. With the help of AI technologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions. Although AIED has been identified as the primary research focus in the field of computers and education, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disciplinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective of educational needs. We propose a framework to show the considerations of implementing AIED in different learning and teaching settings. The structure can help guide researchers with both computers and education backgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particular interest to this journal. Finally, we describe the type of articles we like to solicit and the management of the submissions.

Operando time-resolved X-ray absorption spectroscopy reveals the chemical nature enabling highly selective CO 2 reduction

Abstract: Copper electrocatalysts have been shown to selectively reduce carbon dioxide to hydrocarbons. Nevertheless, the absence of a systematic study based on time-resolved spectroscopy renders the functional agent-either metallic or oxidative Copper-for the selectivity still undecidable. Herein, we develop an operando seconds-resolved X-ray absorption spectroscopy to uncover the chemical state evolution of working catalysts. An oxide-derived Copper electrocatalyst is employed as a model catalyst to offer scientific insights into the roles metal states serve in carbon dioxide reduction reaction (CO2RR). Using a potential switching approach, the model catalyst can achieve a steady chemical state of half-Cu(0)-and-half-Cu(I) and selectively produce asymmetric C2 products - C2H5OH. Furthermore, a theoretical analysis reveals that a surface composed of Cu-Cu(I) ensembles can have dual carbon monoxide molecules coupled asymmetrically, which potentially enhances the catalyst's CO2RR product selectivity toward C2 products. Our results offer understandings of the fundamental chemical states and insights to the establishment of selective CO2RR.

Application and theory gaps during the rise of Artificial Intelligence in Education

Abstract: Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the absence of a comprehensive review on it, this research aims to conduct a comprehensive and systematic review of influential AIEd studies. We analyzed 45 articles in terms of annual distribution, leading journals, institutions, countries/regions, the most frequently used terms, as well as theories and technologies adopted. We also evaluated definitions of AIEd from broad and narrow perspectives and clarified the relationship among AIEd, Educational Data Mining, Computer-Based Education, and Learning Analytics. Results indicated that: 1) there was a continuingly increasing interest in and impact of AIEd research; 2) little work had been conducted to bring deep learning technologies into educational contexts; 3) traditional AI technologies, such as natural language processing were commonly adopted in educational contexts, while more advanced techniques were rarely adopted, 4) there was a lack of studies that both employ AI technologies and engage deeply with educational theories. Findings suggested scholars to 1) seek the potential of applying AI in physical classroom settings; 2) spare efforts to recognize detailed entailment relationships between learners’ answers and the desired conceptual understanding within intelligent tutoring systems; 3) pay more attention to the adoption of advanced deep learning algorithms such as generative adversarial network and deep neural network; 4) seek the potential of NLP in promoting precision or personalized education; 5) combine biomedical detection and imaging technologies such as electroencephalogram, and target at issues regarding learners’ during the learning process; and 6) closely incorporate the application of AI technologies with educational theories.

Effects of peer assessment within the context of spherical video-based virtual reality on EFL students’ English-Speaking performance and learning perceptions

Abstract: English has been recognized as a means of communication around the globe. However, owing to the lack of realistic English practicing contexts, EFL (English as Foreign Language) students generally have few opportunities to communicate with people in English, not to mention to get feedback from others for making reflections. In this study, a spherical video-based virtual reality (SVVR) environment was developed to situate students in authentic English-speaking contexts; moreover, the peer assessment (PA) strategy was employed for guiding students to provide comments on peers' speaking performance and to make reflections on their own performance. To evaluate the effectiveness of the proposed approach, an experiment was conducted in a high school English course. The experiment results reveal more positive effects of the peer-assessment-based SVVR approach compared with the non-peer-assessment-based SVVR approach in terms of the learners' English speaking, learning motivation, and critical thinking skills, as well as reducing their English learning anxiety. Moreover, it was found that the ratings of the students were statistically correlated with those of the teacher. This study further analyzed the types of peer comments by categorizing them into four types: Praise, Criticism, Opinion, and Irrelevant. It was found that Praise feedback was helpful for the students' English-speaking performance, while Criticism feedback might have been unfavorable in this case. Additionally, Irrelevant feedback was not significantly correlated with the students’ performance in the earlier PA stage, but had a significantly negative correlation in the later stage.

Phosphonium Modification Leads to Ultrapermeable Antibacterial Polyamide Composite Membranes with Unreduced Thickness

Abstract: Water transport rate in network membranes is inversely correlated to thickness, thus superior permeance is achievable with ultrathin membranes prepared by complicated methods circumventing nanofilm weakness and defects. Conferring ultrahigh permeance to easily prepared thicker membranes remains challenging. Here, a tetrakis(hydroxymethyl) phosphonium chloride (THPC) monomer is discovered that enables straightforward modification of polyamide composite membranes. Water permeance of the modified membrane is ≈6 times improved, give rising to permeability (permeance × thickness) one magnitude higher than state-of-the-art polymer nanofiltration membranes. Meanwhile, the membrane exhibits good rejection (RNa2SO4 = 98%) and antibacterial properties under crossflow conditions. THPC modification not only improves membrane hydrophilicity, but also creates additional angstrom-scale channels in polyamide membranes for unimpeded transport of water. This unique mechanism provides a paradigm shift in facile preparation of ultrapermeable membranes with unreduced thickness for clean water and desalination.

Hierarchical 3D Architectured Ag Nanowires Shelled with NiMn-Layered Double Hydroxide as an Efficient Bifunctional Oxygen Electrocatalyst.

Abstract: Herein, we report hierarchical 3D NiMn-layered double hydroxide (NiMn-LDHs) shells grown on conductive silver nanowire (Ag NWs) cores as efficient, low-cost, and durable oxygen reduction reaction (ORR)/oxygen evolution reaction (OER) bifunctional electrocatalysts for metal-air batteries. The hierarchical 3D architectured Ag NW@NiMn-LDH catalysts exhibit superb OER/ORR activities in alkaline conditions. The outstanding bifunctional activities of Ag NW@NiMn-LDHs are essentially attributed to increasing both site activity and site populations. The synergistic contributions from the hierarchical 3D open-pore structure of the LDH shells, improved electrical conductivity, and small thickness of the LDHs shells are associated with more accessible site populations. Moreover, the charge transfer between Ag cores and metals of LDH shells and the formation of defective and distorted sites (less coordinated Ni and Mn sites) strongly enhance the site activity. Thus, Ag NW@NiMn-LDH hybrids exhibit a 0.75 V overvoltage difference between ORR and OER with excellent durability for 30 h, demonstrating the distinguished bifunctional electrocatalyst reported to date. Interestingly, the homemade rechargeable Zn-air battery using the hybrid Ag NW@NiMn-LDHs (1:2) catalyst as the air electrode exhibits a charge-discharge voltage gap of ∼0.77 V at 10 mA cm-2 and shows excellent cycling stability. Thus, the concept of the hierarchical 3D architecture of Ag NW@NiMn-LDHs considerably advances the practice of LDHs toward metal-air batteries and oxygen electrocatalysts.

Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay

Abstract: This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with ${e}$ -modification or ${\sigma }$ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov–Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.

Current progress on the production, modification, and applications of bacterial cellulose.

Abstract: Adoption of biomass for the development of biobased products has become a routine agenda in evolutionary metabolic engineering. Cellulose produced by bacteria is a "rising star" for this sustainable development. Unlike plant cellulose, bacterial cellulose (BC) shows several unique properties like a high degree of crystallinity, high purity, high water retention, high mechanical strength, and enhanced biocompatibility. Favored with those extraordinary properties, BC could serve as ideal biomass for the development of various industrial products. However, a low yield and the requirement for large growth media have been a persistent challenge in mass production of BC. A significant number of techniques has been developed in achieving efficient BC production. This includes the modification of bioreactors, fermentation parameters, and growth media. In this article, we summarize progress in metabolic engineering in order to solve BC growth limitation. This article emphasizes current engineered BC production by using various bioreactors, as well as highlighting the structure of BC fermented by different types of engineered-bioreactors. The comprehensive overview of the future applications of BC, aims to provide readers with insight into new economic opportunities of BC and their modifiable properties for various industrial applications. Modifications in chemical composition, structure, and genetic regulation, which preceded the advancement of BC applications, were also emphasized.

Adaptive Tracking Control of Wheeled Inverted Pendulums With Periodic Disturbances

Abstract: This paper reports our study on adaptive tracking control for a mobile-wheeled inverted pendulum with periodic disturbances and parametric uncertainties. With an appropriate reduced dynamic model, incorporating repetitive learning strategies with dynamic decoupling and related adaptive control techniques, a novel controller is successfully constructed to ensure that the output tracking errors of the system will stay within a small neighborhood around zero and all of the other signals are semiglobal uniform bounded. Meanwhile, only one parameter estimation is used for adaptive controller design, which overcomes the problem of over-parametrization. Furthermore, a required condition of period identifier mechanisms is proposed. Finally, detailed simulation results are presented to demonstrate the effectiveness of the proposed control schemes.

Microplasmas for Advanced Materials and Devices

Abstract: Microplasmas are low-temperature plasmas that feature microscale dimensions and a unique high-energy-density and a nonequilibrium reactive environment, which makes them promising for the fabrication of advanced nanomaterials and devices for diverse applications. Here, recent microplasma applications are examined, spanning from high-throughput, printing-technology-compatible synthesis of nanocrystalline particles of common materials types, to water purification and optoelectronic devices. Microplasmas combined with gaseous and/or liquid media at low temperatures and atmospheric pressure open new ways to form advanced functional materials and devices. Specific examples include gas-phase, substrate-free, plasma-liquid, and surface-supported synthesis of metallic, semiconducting, metal oxide, and carbon-based nanomaterials. Representative applications of microplasmas of particular importance to materials science and technology include light sources for multipurpose, efficient VUV/UV light sources for photochemical materials processing and spectroscopic materials analysis, surface disinfection, water purification, active electromagnetic devices based on artificial microplasma optical materials, and other devices and systems including the plasma transistor. The current limitations and future opportunities for microplasma applications in materials related fields are highlighted.

3GPP NR Sidelink Transmissions Toward 5G V2X

Abstract: Featuring direct communications between two user equipments (UEs) without signal relay through a base station, 3GPP sidelink transmissions have manifested their crucial roles in the Long-Term Evolution (LTE) Advanced (LTE-A) for public safety and vehicle-to-everything (V2X) services. With this successful development in LTE-A, the evolution of sidelink transmissions continues in 3GPP New Radio (NR), which renders sidelink an inevitable component as well as downlink and uplink. Targeting at offering low latency, high reliability and high throughout V2X services for advanced driving use cases, a number of new sidelink functions not provided in the LTE-A are supported in NR, including the feedback channel, grant-free access, enhanced channel sensing procedure, and new control channel design. To fully comprehend these new functions, this paper therefore provides essential knowledge of 3GPP NR sidelink transmissions, including the physical layer structure, resource allocation mechanisms, resource sensing and selection procedures, synchronization, and quality-of-service (QoS) management. Furthermore, this paper also provides performance evaluation to assess the gains brought from the new control channel design. As NR sidelink transmissions have been regarded as a foundation to provide advanced services other than V2X in future releases (e.g., advanced relay), potential enhancements are also discussed to serve the urgent demand in corresponding normative works.

Selective Electro-oxidation of Glycerol to Dihydroxyacetone by a Non-precious Electrocatalyst – CuO

Abstract: An earth abundant and non-precious electrocatalyst, CuO, is developed for the high selectivity (∼60 %) towards the glycerol electro-oxidation to dihydroxyacetone (DHA) at high current density (3 mA/cm2) under mild basic condition, pH 9. CuO demonstrates the catalytic ability towards the secondary hydroxyl group oxidation of glycerol. However, under strong basic condition, pH 13, DHA would transform to glyceraldehyde (GLAD) spontaneously without applying potential. Thus, under strong basic condition, the glycerol oxidation usually results with other two-carbon and one-carbon products deriving from GLAD oxidation. Based on HPLC, in-situ Raman spectra, and electrochemical studies, the glycerol electro-oxidation pathway was proposed. With this study, the waste by-product from biodiesel plant, glycerol, can be converted to the valuable DHA and formate at the anode while water is split to hydrogen at the cathode. As a result, both biodiesel and water splitting hydrogen generation industries can be beneficial and the system can be more sustainable.

Biaxial Strains Mediated Oxygen Reduction Electrocatalysis on Fenton Reaction Resistant L10-PtZn Fuel Cell Cathode

Abstract: Proton exchange membrane fuel cells (PEMFCs) are considered as promising energy conversion devices for electric vehicles, large scale electronic devices, and stationary power sources.[1–3] However, the performance of a PEMFC is severely limited by the sluggish kinetics of oxygen reduction reaction (ORR) at the cathode.[4] Carbon-supported platinum (Pt/C) nanoparticles (NPs) with sizes of 3–5 nm are commercially used catalysts for ORR.[5,6] Unfortunately, Pt/C suffers from high cost (≈50 000 $ per kg), limited activity (mass activity ≈0.11 A mgPt at 0.9 V), and poor stability (≈60% activity loss after 30 000 voltage cycles),[7,8] which is far below U.S. Department of Energy (DOE) 2020 targets and strictly hinders the commercialization of PEMFCs. Therefore, highperformance cathode catalysts have been extensively studied including PtM alloys (M = Fe, Co, Ni, etc.),[9,10] nano-structured PtM alloy catalysts (e.g., PtFe, PtCo), especially in an intermetallic L10 structure, have attracted considerable interest due to their respectable activity and stability for the oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). However, metal-catalyzed formation of ·OH from H2O2 (i.e., Fenton reaction) by Feor Co-containing catalysts causes severe degradation of PEM/catalyst layers, hindering the prospects of commercial applications. Zinc is known as an antioxidant in Fenton reaction, but is rarely alloyed with Pt owing to its relatively negative redox potential. Here, sub-4 nm intermetallic L10-PtZn nanoparticles (NPs) are synthesized as high-performance PEMFC cathode catalysts. In PEMFC tests, the L10PtZn cathode achieves outstanding activity (0.52 A mgPt at 0.9 ViR-free, and peak power density of 2.00 W cm−2) and stability (only 16.6% loss in mass activity after 30 000 voltage cycles), exceeding the U.S. DOE 2020 targets and most of the reported ORR catalysts. Density function theory calculations reveal that biaxial strains developed upon the disorder-order (A1L10) transition of PtZn NPs would modulate the surface PtPt distances and optimize PtO binding for ORR activity enhancement, while the increased vacancy formation energy of Zn atoms in an ordered structure accounts for the improved stability.

Using virtual reality-based training to improve cognitive function, instrumental activities of daily living and neural efficiency in older adults with mild cognitive impairment.

Abstract: BACKGROUND A combination of physical and cognitive training appears to be the effective intervention to improve cognitive function in older adults with mild cognitive impairment (MCI). Computing technology such as virtual reality (VR) may have the potential to assist rehabilitation in shaping brain health. However, little is known about the potential of VR-based physical and cognitive training designed as an intervention for cognition and brain activation in elderly patients with MCI. Moreover, whether a VR program designed around functional tasks can improve their instrumental activities of daily living (IADL) requires further investigation. AIM This study investigated the effects of 12 weeks of VR-based physical and cognitive training on cognitive function, brain activation and IADL and compared the VR intervention with combined physical and cognitive training. DESIGN A single-blinded randomized controlled trial. SETTING Communities and day care centers in Taipei, Taiwan. POPULATION Older adults with mild cognitive impairment. METHODS Thirty-four community-dwelling older adults with MCI were randomized into either a VR-based physical and cognitive training (VR) group or a combined physical and cognitive training (CPC) group for 36 sessions over 12 weeks. Participants were assessed for their cognitive function (global cognition, executive function and verbal memory) and IADL at pre- and postintervention. Changes in prefrontal cortex activation during the global cognition test were also captured by functional near-infrared spectroscopy (NIRS) to identify the potential mediating pathway of the intervention. RESULTS Both groups showed improved executive function and verbal memory (immediate recall). However, only the VR group showed significant improvements in global cognition (P<0.001), verbal memory (delayed recall, P=0.002), and IADL (P<0.001) after the intervention. The group × time interaction effects further demonstrated that IADL were more significantly improved with VR training than with CPC training (P=0.006). The hemodynamic data revealed decreased activation in prefrontal areas after training (P=0.0015), indicative of increased neural efficiency, in the VR-trained subjects. CONCLUSIONS VR-based physical and cognitive training improves cognitive function, IADL and neural efficiency in older adults with MCI. CLINICAL REHABILITATION IMPACT VR training could be implemented for older adults with MCI.

Ternary Electrification Layered Architecture for High-Performance Triboelectric Nanogenerators.

Abstract: The triboelectric nanogenerator (TENG) has been proved to be a green and efficient energy harnessing technology for electricity generation from ambient mechanical motions based on its ability to leverage the triboelectrification process. Enhancing TENG output performance through rational structural design still triggers increasing research interest. Here, we report a ternary electrification layered architecture beyond the current binary TENG systems, with improved performance for mechanical energy harvesting. Introducing a ternary Kapton layer into the traditional binary electrification layered architecture of TENGs consisting of copper and fluorinated ethylene propylene, yields a 2.5 times enhancement of peak power output, representing a 6.29-fold increase compared to the TENG composed of copper and Kapton. A wide-range of material configurations were systematically tested using this ternary electrification layered architecture to prove its practical effectiveness. The ternary electrification layered architecture invented in this work provides an alternative strategy to enhance TENG output performance, which represents a solid step for TENGs application in high-performance mechanical energy harvesting.

High-Rate and Long-Cycle Stability with a Dendrite-Free Zinc Anode in an Aqueous Zn-Ion Battery Using Concentrated Electrolytes

Abstract: Recently, metallic zinc (Zn) is becoming a promising ideal anode material for rechargeable aqueous batteries by providing high theoretical capacity (820 mA h/g) with divalent reaction, environmenta...