Nanyang Technological University

About: Nanyang Technological University is a education organization based out in Singapore, Singapore. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 48003 authors who have published 112815 publications receiving 3294199 citations. The organization is also known as: NTU & Universiti Teknologi Nanyang.

Is the Free Energy Change of Adsorption Correctly Calculated

Abstract: In the study of adsorption, changes in free energy (ΔG°), enthalpy (ΔH°), and entropy (ΔS°) have been most frequently calculated from the Langmuir equilibrium constant. In a strict theoretical sense, the Langmuir equilibrium constant with units of liters per mole and the thermodynamic equilibrium constant without units are not the same. Moreover, the equilibrium constants for thermodynamic calculation have also been derived in different ways in the literature, for example, Frumkin isotherm, Flory−Huggins isotherm, distribution constants, and so on. As a result, values of ΔG°, ΔH°, and ΔS° of adsorption reported in the literature are very confusing. This study shows that for a dilute solution of charged adsorbates or for a solution of uncharged adsorbates at any concentration, the thermodynamic equilibrium constant of adsorption would be reasonably approximated by the Langmuir equilibrium constant, and thus the use of the Langmuir equilibrium constant for calculation of ΔG° and subsequent determination of ...

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

A Survey on Software-Defined Networking

Abstract: Emerging mega-trends (e.g., mobile, social, cloud, and big data) in information and communication technologies (ICT) are commanding new challenges to future Internet, for which ubiquitous accessibility, high bandwidth, and dynamic management are crucial. However, traditional approaches based on manual configuration of proprietary devices are cumbersome and error-prone, and they cannot fully utilize the capability of physical network infrastructure. Recently, software-defined networking (SDN) has been touted as one of the most promising solutions for future Internet. SDN is characterized by its two distinguished features, including decoupling the control plane from the data plane and providing programmability for network application development. As a result, SDN is positioned to provide more efficient configuration, better performance, and higher flexibility to accommodate innovative network designs. This paper surveys latest developments in this active research area of SDN. We first present a generally accepted definition for SDN with the aforementioned two characteristic features and potential benefits of SDN. We then dwell on its three-layer architecture, including an infrastructure layer, a control layer, and an application layer, and substantiate each layer with existing research efforts and its related research areas. We follow that with an overview of the de facto SDN implementation (i.e., OpenFlow). Finally, we conclude this survey paper with some suggested open research challenges.

High Efficiency Solid-State Sensitized Solar Cell-Based on Submicrometer Rutile TiO2 Nanorod and CH3NH3PbI3 Perovskite Sensitizer

Abstract: We report a highly efficient solar cell based on a submicrometer (∼0.6 μm) rutile TiO2 nanorod sensitized with CH3NH3PbI3 perovskite nanodots. Rutile nanorods were grown hydrothermally and their lengths were varied through the control of the reaction time. Infiltration of spiro-MeOTAD hole transport material into the perovskite-sensitized nanorod films demonstrated photocurrent density of 15.6 mA/cm2, voltage of 955 mV, and fill factor of 0.63, leading to a power conversion efficiency (PCE) of 9.4% under the simulated AM 1.5G one sun illumination. Photovoltaic performance was significantly dependent on the length of the nanorods, where both photocurrent and voltage decreased with increasing nanorod lengths. A continuous drop of voltage with increasing nanorod length correlated with charge generation efficiency rather than recombination kinetics with impedance spectroscopic characterization displaying similar recombination regardless of the nanorod length.