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Recent Advances in Single-Atom Catalysts for Photoelectrocatalytic Water Splitting

Carbon Energy 2025年第04期 114-142页

作者： Jiao Yang Xiaoyang Zheng Syed Shoaib Ahmad Shah Chao Wang Xueyao Li Zhishuo Yan Lishan Peng Key Laboratory of Rare Earths Ganjiang Innovation Academy Chinese Academy of Sciences Institute of Applied Physics and Materials Engineering University of Macau Department of Mechanical Engineering Graduate School of Engineering The University of Tokyo Department of Chemistry School of Natural Sciences National University of Sciences and Technology Department of Chemistry University of Connecticut School of Rare Earths University of Science and Technology of China Department of Electrical and Computer Engineering North Dakota State University

Hydrogen is a highly promising energy carrier because of its renewable and clean qualities. Among the different methods for H2production, photoelectrocatalysis（PEC） water splitting has garnered significant interest, thanks to the abundant and perennial solar energy. Single-atom catalysts（SACs）, which feature well-distributed atoms anchored on supports, have gained great attention in PEC water splitting for their unique advantages in overcoming the limitations of conventional PEC ***, we comprehensively review SAC-incorporated photoelectrocatalysts for efficient PEC water splitting. We begin by highlighting the benefits of SACs in improving charge transfer, catalytic selectivity, and catalytic activity, which address the limitations of conventional PEC reactions. Next, we provide a comprehensive overview of established synthetic techniques for optimizing the properties of SACs, along with modern characterization methods to confirm their unique structures. Finally, we discuss the challenges and future directions in basic research and advancements, providing insights and guidance for this developing field.

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Design and demonstration of Cu/Al2O3 /Cu RRAM with complementary resistance switching characteristic

Design and demonstration of Cu/Al2O3 /Cu RRAM with complemen...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Huanmei Yuan Yang Char Shunqi Dai Cristine Jin Estrada Yuqing Zhang Annan Xiong Hao Bai Guangyu Du Songhua Cai Zhimin Li Songcen Xu Mansun Chan Department of Electronic & Computer Engineering Hong Kong University of Science & Technology Kowloon Hong Kong China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing China Department of Applied Physics The Hong Kong Polytechnic University Kowloon Hong Kong China

To overcome the crosstalk problem originating from the sneak path current, a simple structure of $\text{Cu}/\text{Al}_{2}\mathrm{O}_{3}$ /Cu memristor was fabricated in this paper. Results showed that the device presented good bipolar resistive switching characteristic (BRS) as well as the endurance and retention performance. Most importantly, the complimentary resistance switching characteristic (CRS) appeared in this device by adjusting the testing parameters, which is important in crossbar array applications.

关键词： Resistance Performance evaluation Fluctuations Crosstalk Memristors Switches Manufacturing

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Visual Perception of Robot Appearance Attributes in the Peripheral Field of View Depends on Human Observer Eye-Movement Behaviors

Visual Perception of Robot Appearance Attributes in the Peri...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kolja Kü hnlenz Barbara Kü hnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Art Coburg Germany Academic Center of Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

This paper presents results from a study on the perception of robot attributes by human observers in their peripheral field of view depending on types of eye-movements of the latter. A between-subjects design is used, where a picture of a robot head is presented on a screen in the peripheral field of view of the participants with two conditions of eye-movements: static and pursuit. The two conditions are realized by a dot to be tracked by the participants, which is placed either in the center of the screen or moving linearly between left and right screen margins. As a subjective measure for appearance attributes, anthropomorphism is used using the multi-component Human-Robot Interaction Evaluation Scale (HRIES). Significant differences are revealed in the sociability and agency sub-scales of HRIES with respect to eye-movement behaviors. The results show, that humans perceive robot appearance attributes differently depending on whether or not pursuit eye-movements are conducted supporting the main hypothesis. Further, for pursuit movements, the scale center is rated for these sub-scales, which may indicate, that the particular robot characteristic is perceived less distinctively. No significant interactions are found for animacy and disturbance, which may be due to less applicability of these sub-scales to static images. The findings may have an impact on close interaction between humans and robots and potential anthropomorphism-related influences on task accomplishment to be considered in interaction design.

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Computing Autotopism Groups of Partial Latin Rectangles

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ACM Journal of Experimental Algorithmics 2020年第pp1–39期25卷 1-39页

作者： Stones, Rebecca J. Falcón, Raúl M. Kotlar, Daniel Marbach, Trent G. College of Computer Science Nankai University Tianjin China Department of Mathematics Beijing Jiaotong University Beijing100044 China School of Building Engineering Department of Applied Mathematics I Universidad de Sevilla Seville41012 Spain Computer Science Department TelHai College Upper Galilee1220800 Israel Department of Mathematics Ryerson University 350 Victoria St. TorontoON Canada

Computing the autotopism group of a partial Latin rectangle (PLR) can be performed in multiple ways. This study has two aims: comparing some of these methods experimentally to identify those that are competitive;and identifying design goals for developing practical software. We compare six families of algorithms (two backtracking and four graph-theoretic methods), with and without using entry invariants (EIs), in a range of settings. Two EIs are considered: frequencies of row, column, and symbol representatives;and 2 × 2 submatrices. The best approach to computing autotopism groups varies. When PLRs have many autotopisms (such as having very few entries or being a group table), the McKay, Meynert, and Myrvold (MMM) method computes generators for the autotopism group efficiently. (The MMM method is the standard way to compute autotopisms.) Otherwise, PLRs ordinarily have trivial or small autotopism groups, and the task is to verify this. The so-called PLR graph method is slightly more efficient in this setting than the MMM method (in some circumstances, around twice as fast). With an intermediate number of entries, the quick-to-compute strong EIs are effective at reducing the need for computation without introducing significant overhead. With a full or almost-full PLR, a more sophisticated EI is needed to reduce down-the-line computation. These results suggest a hybrid approach to computing autotopism groups: The software decides on suitable EIs based on the input;and the user chooses between the MMM or the PLR graph methods, depending on their dataset. This article expands the authors' previous article Computing autotopism groups of PLRs: a pilot study. © 2020 ACM.

关键词： Geometry

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Bayesian polynomial neural networks and polynomial neural ordinary differential equations

arXiv

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arXiv 2023年

作者： Fronk, Colby Yun, Jaewoong Singh, Prashant Petzold, Linda Department of Chemical Engineering University of California Santa BarbaraCA93106 United States Department of Statistics and Applied Probability University of California Santa BarbaraCA93106 United States Department of Geography University of California Santa BarbaraCA93106 United States Science for Life Laboratory Department of Information Technology Uppsala University Uppsala Sweden Department of Mechanical Engineering University of California Santa BarbaraCA93106 United States Department of Computer Science University of California Santa BarbaraCA93106 United States

Polynomial neural ordinary differential equations (ODEs) are a recent approach for symbolic regression of dynamical systems governed by polynomials. However, they are limited in that they provide maximum likelihood point estimates of the model parameters. The domain expert using system identification often desires a specified level of confidence or range of parameter values that best fit the data. In this work, we use Bayesian inference to provide posterior probability distributions of the parameters in polynomial neural ODEs. To date, there are no studies that attempt to identify the best Bayesian inference method for neural ODEs and symbolic neural ODEs. To address this need, we explore and compare three different approaches for estimating the posterior distributions of weights and biases of the polynomial neural network: the Laplace approximation, Markov Chain Monte Carlo (MCMC) sampling, and variational inference. We have found the Laplace approximation to be the best method for this class of problems. We have also developed lightweight JAX code to estimate posterior probability distributions using the Laplace approximation. Copyright © 2023, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Power Quality Enhancement of Solar Based Microgrids Using ML and AI

Power Quality Enhancement of Solar Based Microgrids Using ML...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： K. Paul Joshua C. Vennila Kurikyala Prasad Yadav D. Kirubakaran Vartika Kulshrestha B.R. Praveen Department of Electronics and Communication Engineering St. Peter's College of Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Alagappa Chettiar Government College of Engineering and Technology Tamil Nadu India Engineering Department University of Technology and Applied Sciences-Ibri (UTAS-Ibri) Dept. of Electrical and Electronics Eng. St. Joseph's Institute of Technology Tamil Nadu India Computer Science & Engineering ACED Alliance University-Central Campus Karnataka India Dept. of Electrical and Electronics Engineering Sai Vidya Institute of Technology Karnataka India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Solar-powered micro grids are gaining popularity as a cost-effective and environmentally friendly way to provide localized electricity. Microgrids struggle with solar power's fluctuation and intermittent nature. Sun-based microgrids' power quality is improved via machine learning (ML) and synthetic intelligence (AI). The recommended solution employs modern ML algorithms to predict solar power generation and micro grid energy output variations. ML models can accurately forecast changes in solar irradiance, temperature, and other crucial aspects by evaluating ancient and real-time data, enabling proactive micro grid power float management. The proposed ML and AI-based technology offers superior power best, proactive power control, and better predictions than standard management methods. Simulation and experimental results show that the technique reduces load interruptions, improves balance, and reduces solar energy oscillations on the micro grid. Its capabilities are excellent. to provide reliable and excellent power supply, large-scale renewable energy adoption, and sustainable power ecosystems with ML and AI in solar-based micrgarids.

关键词： Solar irradiance Renewable energy sources Temperature distribution Statistical analysis Power quality Microgrids Real-time systems Solar power generation Standards Systematic literature review

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Observation of topological frequency combs

arXiv

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arXiv 2024年

作者： Flower, Christopher J. Mehrabad, Mahmoud Jalali Xu, Lida Moille, Gregory Suarez-Forero, Daniel G. Örsel, Oğulcan Bahl, Gaurav Chembo, Yanne Srinivasan, Kartik Mittal, Sunil Hafezi, Mohammad Joint Quantum Institute National Institute of Standards and Technology University of Maryland College ParkMD United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States

On-chip generation of optical frequency combs using nonlinear ring resonators has opened the route to numerous novel applications of combs that were otherwise limited to mode-locked laser systems. Nevertheless, even after more than a decade of development, on-chip nonlinear combs still predominantly rely on the use of single-ring resonators. Recent theoretical investigations have shown that generating combs in a topological array of resonators can provide a new avenue to engineer comb spectra. Here, we experimentally demonstrate the generation of such a novel class of frequency combs, topological frequency combs, in a two-dimensional (2D) lattice of hundreds of nonlinear ring resonators. Specifically, the lattice hosts topological edge states that exhibit fabrication-robust linear dispersion and spatial confinement at the boundary of the lattice. Upon optical pumping of the topological edge band, these unique properties of the edge states lead to the generation of a nested frequency comb that is spectrally confined within the edge bands across ≈40 longitudinal modes. Moreover, using spatial imaging of our topological lattice, we confirm that light generated in the comb teeth is indeed spatially confined at the lattice edge, characteristic of linear topological systems. Our results bring together the fields of topological photonics and optical frequency combs, providing an opportunity to explore the interplay between topology and nonlinear systems in a platform compatible with commercially available nanofabrication processes. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical pumping

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Design of a Mobile Data Collection Robot for Learning-based Localization and Autonomous Driving

Design of a Mobile Data Collection Robot for Learning-based ...

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Innovations in Intelligent Systems and Applications Conference

作者： Ali Ömer Baykar Jens Lambrecht Ayhan Kural Selçuk Eray Uygur Ahmet Yıldız Department of Mechanical Engineering Istanbul Technical University Istanbul Türkiye Department of Mechanical Engineering Turkish-German University Istanbul Türkiye Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany Turkish-German University Istanbul Türkiye Macromedia University of Applied Sciences Hamburg Germany

This study introduces a mobile robot capable of collecting position and corresponding visual data seamlessly from both indoor and outdoor settings within the same sequence. The mobile robot has been specifically designed to navigate obstacles such as stairs and steps during transitions between indoor and outdoor environments. To accomplish this, the robot incorporates differential driving dynamics and is equipped with essential sensors including two stereo cameras, LIDAR, IMU, and GNSS. The entire system operates on the Robot Operating System (ROS). Consequently, it becomes possible to create a comprehensive dataset that encompasses not only the routes traversed by mobile vehicles but also includes all vehicle and pedestrian roads, as well as indoor spaces, found within a campus environment.

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Solution of Branched Network Problems Using Meta-heuristics 3rd

Solution of Branched Network Problems Using Meta-heuristics

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3rd International Conference on Soft Computing: Theories and Applications, SoCTA 2018

作者： Bilal Vig, Garima Pant, Millie Department of Applied Science and Engineering Indian Institute of Technology Roorkee Saharanpur Campus Roorkee247667 India Department of Computer Science and Engineering ASET Amity University Noida201303 India

ISBN: (纸本)9789811507502

Over the last few years, meta-heuristics algorithms are very popular for solving different types of optimization problems in various fields. In the present study, three nature-inspired algorithms (PSO, ABC, CS), two evolutionary algorithms (GA, DE) and one classical method (SA) are applied in branched network problem to find optimal design cost of network. The branched network consists of two types of problems: first is single load branched network problem and second is multiple load branched network problem. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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A Hypergraph Approach to Distributed Broadcast

arXiv

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arXiv 2024年

作者： Cao, Qi Shao, Yulin Yang, Fan Dobre, Octavia A. Xidian-Guangzhou Research Institute Xidian University Guangzhou China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China The Faculty of Engineering and Applied Science Memorial University St. John'sNLA1B 3X5 Canada

This paper explores the distributed broadcast problem within the context of network communications, a critical challenge in decentralized information dissemination. We put forth a novel hypergraph-based approach to address this issue, focusing on minimizing the number of broadcasts to ensure comprehensive data sharing among all network users. The key contributions of this work include the establishment of a general lower bound for the problem using the min-cut capacity of hypergraphs, and a distributed broadcast for quasi-trees (DBQT) algorithm tailored for the unique structure of quasi-trees, which is proven to be optimal. This paper advances both network communication strategies and hypergraph theory, with implications for a wide range of real-world applications, from vehicular and sensor networks to distributed storage systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Bibliographies

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