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Theoretical study on adsorption and reaction of polymeric formic acid on the Cu(111) surface

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Physical Review Materials 2021年第7期5卷 075801-075801页

作者： Septia Eka Marsha Putra Fahdzi Muttaqien Yuji Hamamoto Kouji Inagaki Akitoshi Shiotari Jun Yoshinobu Yoshitada Morikawa Ikutaro Hamada Department of Precision Engineering Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan Department of Engineering Physics Institut Teknologi Sumatera Jalan Terusan Ryacudu Way Hui Jati Agung South Lampung 35365 Indonesia Instrumentation and Computational Physics Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung West Java 40132 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung West Java 40132 Indonesia Elements Strategy Initiative for Catalysts and Batteries (ESICB) Kyoto University Goryo-Ohara Nishikyou-ku Katsura Kyoto 615-8245 Japan Department of Advanced Materials Science The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8561 Japan The Institute for Solid State Physics The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8581 Japan Research Center for Precision Engineering Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan

We investigate the polymeric adsorption of formic acid (HCOOH) on the Cu(111) surface by means of density functional theory. We present structural models for the polymeric form of HCOOH on Cu(111) and characterize their stability, electronic, and vibrational properties. Based on the energetics and dynamics, as well as simulated scanning tunneling microscopy and atomic force microscopy images, we propose that the α polymorph is likely to be formed on the Cu(111) surface and can explain the experimental findings. We also study the initial step of the catalytic dehydrogenation of HCOOH and find that the O-H bond dissociation at the edge of the polymer is facilitated, rather than that forming hydrogen bonding, agreeing well with the experiments.

关键词： Adsorption Chemical bonding Chemical kinetics, dynamics & catalysis Electronic structure Surface & interfacial phenomena Surfaces Density functional theory First-principles calculations

The role of students’ non-cognitive factors and school resources in predicting mathematics achievement using PISA 2018 Indonesia data

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AIP Conference Proceedings 2022年第1期2575卷

作者： Ari Kusdinar Kismiantini 1Graduate Program of Mathematics Education Universitas Negeri Yogyakarta Jl. Colombo No. 1 Sleman Yogyakarta 55281 Indonesia 2Department of Statistics Universitas Negeri Yogyakarta Jl. Colombo No. 1 Sleman Yogyakarta 55281 Indonesia

Many factors affect students’ mathematics achievement. In addition to cognitive factors, many studies also highlight and show that non-cognitive factors of students and school resources become important factors in influencing students’ mathematical achievement. This study analyzed the relationship between students’ non-cognitive factors and school resources to mathematics achievement. The data were taken from 9,620 of the 338 schools in Indonesia involved in participating in the 2018 program for International Student Assessment (PISA). The results of the multilevel analysis found that the students’ happy feelings, the students’ cooperation and the students’ belief were a statistically significant relationship to mathematics achievement with all positive relationships. The low quality of educational materials and teaching staff in schools were statistically significant and negatively correlated with mathematics achievement. This study showed that the non-cognitive factors and low quality of teaching staff had an important role for students in achieving mathematics.

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Uncovering the topology of time-varying fmri data using cubical persistence

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arXiv 2020年

作者： Rieck, Bastian Wolf, Guy Yates, Tristan Bock, Christian Borgwardt, Karsten Turk-Browne, Nicholas Krishnaswamy, Smita Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Psychology Yale University New HavenCT United States Department of Mathematics & Statistics Université de Montréal MontréalQC Canada Mila – Quebec AI Institute MontréalQC Canada

Functional magnetic resonance imaging (fMRI) is a crucial technology for gaining insights into cognitive processes in humans. Data amassed from fMRI measurements result in volumetric data sets that vary over time. However, analysing such data presents a challenge due to the large degree of noise and person-to-person variation in how information is represented in the brain. To address this challenge, we present a novel topological approach that encodes each time point in an fMRI data set as a persistence diagram of topological features, i.e. high-dimensional voids present in the data. This representation naturally does not rely on voxel-by-voxel correspondence and is robust towards noise. We show that these time-varying persistence diagrams can be clustered to find meaningful groupings between participants, and that they are also useful in studying within-subject brain state trajectories of subjects performing a particular task. Here, we apply both clustering and trajectory analysis techniques to a group of participants watching the movie ‘Partly Cloudy’. We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Prediction of the activity of carbohydrate moiety of bromelain as immunomodulator using an in silico approach

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AIP Conference Proceedings 2023年第1期2480卷

作者： Trina Ekawati Tallei Fatimawali Afriza Yelnetty Nurdjannah Jane Niode Diah Kusumawaty Billy Johnson Kepel Souvia Rahimah Yunus Effendi Rinaldi Idroes Sefren Geiner Tumilaar Shafi Mahmud Talha Bin Emran Department of Biology Faculty of Mathematics and Natural Sciences Sam Ratulangi University Manado 95115 North Sulawesi Indonesia Pharmacy Study Program Faculty of Mathematics and Natural Sciences Sam Ratulangi University Manado 95115 North Sulawesi Indonesia Animal Products Technology Study Program Faculty of Animal Husbandry Sam Ratulangi University Manado 95115 North Sulawesi Indonesia Department of Dermatology and Venereology Faculty of Medicine University of Sam Ratulangi RD Kandou Hospital Manado 95163 North Sulawesi Indonesia Department of Biology Education Faculty of Mathematics and Science Education Universitas Pendidikan Indonesia Bandung 40154 Jawa Barat Indonesia Department of Chemistry Faculty of Medicine Sam Ratulangi University Manado 95115 North Sulawesi Indonesia Food Technology Study Program Department of Food Industrial Technology Faculty of Agroindustrial Technology Universitas Padjadjaran Jatinangor 45363 West Java Indonesia Biology Study Program Faculty of Science and Technology Universitas Al Azhar Jakarta 12110 Indonesia Department of Pharmacy Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Kopelma Darussalam Banda Aceh 23111 Indonesia Microbiology Laboratory Department of Genetic Engineering and Biotechnology University of Rajshahi Rajshahi 6205 Bangladesh Department of Pharmacy BGC Trust University Bangladesh Chittagong 4381 Bangladesh

Bromelain is a glycoprotein that contains a single carbohydrate moiety group and displays proteolytic activity. Bromelain has long been known to act as an immunomodulator; however, the immunomodulatory activity of its carbohydrate moiety has been very rarely studied and is not well understood. This study aimed to predict the immunomodulatory activity of the carbohydrate moiety of bromelain (CMB) by evaluating it using the PASS server, and the binding affinities between CMB and various pro-inflammatory cytokines were analyzed using a molecular docking approach. This research was conducted by docking the CMB ligand to the following receptor proteins: interleukin-6 (IL-6), IκBβ/nuclear factor (NF)-κB p65 homodimer complex (IκBα/NF-κB p65), tumor necrosis factor-alpha (TNF-α), and IL-1β. The biological potential of CMB was predicted using the online PASS server. The results of the computational analysis indicated that the highest binding energy was identified between CMB and IκBβ/NF-κB p65 (−10.3 kcal/mol), followed by TNF-α (−9.7 kcal/mol), and IL-6 and IL-1β, with values of −8.2 kcal/mol for both. The CMB activity predicted by the Way2Drug PASS server indicated that this molecule is likely to display significant immunostimulant activity. These findings suggested that CMB can be considered a potential lead molecule for further drug development to identify agent that can prevent or cure inflammation and restore balance to the immune system. However, the predictions determined in this study remain to be validated in future in vitro and in vivo studies.

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Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

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arXiv 2020年

作者： Huang, Jianxing Zhang, Linfeng Wang, Han Zhao, Jinbao Cheng, Jun Weinan, E. State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Department of Mathematics Princeton University PrincetonNJ08544 United States

Solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here we implement the Deep Potential Generator (DP-GEN) to set up an efficient protocol to automatically generate interatomic potentials for Li10GeP2S12-type solid-state electrolyte materials (Li10GeP2S12, Li10SiP2S12 and Li10SnP2S12). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of diffusion process to a wide temperature range (300K-1000K) and systems with large size (~1000 atoms). Important technical aspects like the statistical error and size effect are carefully investigated and benchmark tests including the effect of density functional, thermal expansion and configurational disorder are performed. The computed data that considering these factors agrees well with the experimental results and we find that the 3 structures shows different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Solid electrolytes

Bridging the gap: Machine learning to resolve improperly modeled dynamics

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arXiv 2020年

作者： Qraitem, Maan Kularatne, Dhanushka Forgoston, Eric Hsieh, M. Ani Department of Computer Science Colby College WatervilleME04901 United States Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States Department of Applied Mathematics and Statistics Montclair State University MontclairNJ07043 United States

We present a data-driven modeling strategy to overcome improperly modeled dynamics for systems exhibiting complex spatio-temporal behaviors. We propose a Deep Learning framework to resolve the differences between the true dynamics of the system and the dynamics given by a model of the system that is either inaccurately or inadequately described. Our machine learning strategy leverages data generated from the improper system model and observational data from the actual system to create a neural network to model the dynamics of the actual system. We evaluate the proposed framework using numerical solutions obtained from three increasingly complex dynamical systems. Our results show that our system is capable of learning a data-driven model that provides accurate estimates of the system states both in previously unobserved regions as well as for future states. Our results show the power of state-of-the-art machine learning frameworks in estimating an accurate prior of the system’s true dynamics that can be used for prediction up to a finite horizon. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

Localization transition in three-dimensional stealthy hyperuniform disordered systems

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arXiv 2021年

作者： Sgrignuoli, F. Torquato, S. Dal Negro, L. Department of Electrical and Computer Engineering & Photonics Center Boston University 8 Saint Mary’s Street BostonMA02215 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Division of Material Science and Engineering Boston University 15 Saint Mary’s Street BrooklineMA02446 United States Department of Physics Boston University 590 Commonwealth Avenue BostonMA02215 United States

The purpose of this work is to understand the fundamental connection between structural correlations and light localization in three-dimensional (3D) open scattering systems of finite size. We numerically investigate the transport of vector electromagnetic waves scattered by resonant electric dipoles spatially arranged in 3D space by stealthy hyperuniform disordered point patterns. Three-dimensional stealthy hyperuniform disordered systems (3D-SHDS) are engineered with different structural correlation properties determined by their degree of stealthiness χ. Such fine control of exotic states of amorphous matter enables the systematic design of optical media that interpolate in a tunable fashion between uncorrelated random structures and crystalline materials. By solving the electromagnetic multiple scattering problem using Green’s matrix spectral method, we establish a transport phase diagram that demonstrates a clear delocalization-localization transition beyond a critical scattering density that depends on χ. The transition is characterized by studying the Thouless number and the spectral statistics of the scattering resonances. In particular, by tuning the χ parameter, we demonstrate large spectral gaps and suppressed sub-radiant proximity resonances, facilitating light localization. Moreover, consistently with previous studies, our results show a region of the transport phase diagram where the investigated scattering systems become transparent. Our work provides a systematic description of the transport and localization properties of light in stealthy hyperuniform structures and motivates the engineering of novel photonic systems with enhanced light-matter interactions for applications to both classical and quantum devices. Copyright © 2021, The Authors. All rights reserved.

关键词： Phase diagrams

Knowledge-Based Learning of Nonlinear Dynamics and Chaos

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arXiv 2020年

作者： Jiahao, Tom Z. Hsieh, M. Ani Forgoston, Eric Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA19104 United States Department of Applied Mathematics and Statistics Montclair State University MontclairNJ07043 United States

Extracting predictive models from nonlinear systems is a central task in scientific machine learning. One key problem is the reconciliation between modern data-driven approaches and first principles. Despite rapid advances in machine learning techniques, embedding domain knowledge into data-driven models remains a challenge. In this work, we present a universal learning framework for extracting predictive models from nonlinear systems based on observations. Our framework can readily incorporate first principle knowledge because it naturally models nonlinear systems as continuous-time systems. This both improves the extracted models' extrapolation power and reduces the amount of data needed for training. In addition, our framework has the advantages of robustness to observational noise and applicability to irregularly sampled data. We demonstrate the effectiveness of our scheme by learning predictive models for a wide variety of systems including a stiff Van der Pol oscillator, the Lorenz system, and the Kuramoto-Sivashinsky equation. For the Lorenz system, different types of domain knowledge are incorporated to demonstrate the strength of knowledge embedding in data-driven system identification. Copyright © 2020, The Authors. All rights reserved.

关键词： Domain Knowledge

The cardinality of the sublattice of closed ideals of operators between certain classical sequence spaces

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arXiv 2020年

作者： Freeman, D. Schlumprecht, T.H. Zsák, A. Department of Mathematics and Statistics St Louis University St LouisMO63103USA Department of Mathematics Texas A&M University College StationTX77843 United States Faculty of Electrical Engineering Czech Technical University in Prague Zikova 4 Prague166 27 Czech Republic Peterhouse CambridgeCB2 1RD United Kingdom

Theorem A and Theorem B of [1] state that for 1 p, c0), L(p, ∞) and of L(1, p) are at least of cardinality 2ω. Here we show that the cardinality of the lattice of closed ideals of L(p, c0), L(p, ∞) and of L(1, p), ...

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