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Synergetic effects of multiple junction and surface hydroxyl in Cu/CuO/Cu2O/TiO2 heterostructures towards highly efficient photocatalysts for hydrogen generation

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Materials Science for Energy Technologies 2025年 8卷 131-142页

作者： Subagyo, Riki Anindika, Garcelina Rizky Aufkani, Afif Akmal Zhang, Lei Ardhyananta, Hosta Burhan, R.Y. Perry Nugraheni, Zjahra Vianita Akhlus, Syafsir Bahruji, Hasliza Prasetyoko, Didik Wellia, Diana Vanda Ivansyah, Atthar Luqman Arramel Kusumawati, Yuly Department of Chemistry Faculty of Science and Analytic Data Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Center of Excellence Applied Physics and Chemistry Nano Center Indonesia Jl PUSPIPTEK South Tangerang Banten15314 Indonesia Department of Physics National University of Singapore Singapore117551 Singapore Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Andalas Padang25163 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jalan Ganesha No. 10 Bandung40132 Indonesia Instrumentation and Computational Physics Research Group Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha No.10 West Java Bandung40132 Indonesia

The implementation of titanium dioxide (TiO2) as a photocatalyst material in hydrogen (H2) evolution reaction (HER) has embarked renewed interest in the past decade. Rapid electron-hole pairs recombination and wide band gap of a photo-sensitive material of TiO2 are detrimental toward the targeted catalytical reaction. In this study, we present the rational design, fabrication, photocatalytic performance of TiO2-Cu/CuO/Cu2O heterostructures (CuTi) using viable chemical reduction method. The Z-scheme and S-scheme are succesfully generated across the TiO2/CuO/Cu2O interfaces, while the Schottky junction arises on the Cu perimeters. This is evidenced from the blue shifted about 0.3 eV of Cu 2p core level determined by using X-ray photoemission spectroscopy (XPS), in combination with the formation of inverse V-shape of the Mott-Schottky plots. In addition, we find that Cu/CuO/Cu2O facilitates photon absorption range up to the visible region. The multiple heterojunction and the large number of OHsurface enhanced charge carrier transfer are associated to the suppression of photoluminescence (PL) intensity, high surface hydroxyl (OHsurface) density in CuTi probed by XPS, and fast electron transfer based on the electrochemical measurements. The presence of OHsurface inhibits the recombination of electron. A significant H2 photogeneration rate enhancement is achieved when an optimized 5 wt% Cu/CuO/Cu2O concentration is used on TiO2 to achieve 7,157.19 μmol·g−1 (1,789.30 μmol·g−1·h−1). Based on this finding, zero emission energy innitiative could be materialized under multiple heterojunctions in photocatalytic process is beneficial for enhancing the H2 production. © 2025 The Authors

关键词： Titanium dioxide

Unbiased Parameter Estimation for Bayesian Inverse Problems

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

Drivetrain simulation using variational autoencoders

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arXiv 2025年

作者： Sharma, Pallavi Urrea-Quintero, Jorge-Humberto Bogdan, Bogdan Ciotec, Adrian-Dumitru Vasilie, Laura Wessels, Henning Skull, Matteo Porsche Engineering Etzelstraße 1 Bietigheim-Bissingen74321 Germany Graduate Program Computational Sciences in Engineering Technische Universität Braunschweig Braunschweig Germany Fraunhofer Institute for Energy Economics and Energy System Technology Joseph-Beuys-Straße 8 Kassel34117 Germany Institute of Applied Mechanics Division Data-Driven Modeling of Mechanical Systems Technische Universität Braunschweig Pockelsstraße 3 Braunschweig38106 Germany

This work proposes variational autoencoders (VAEs) to predict a vehicle’s jerk from a given torque demand, addressing the limitations of sparse real-world datasets. Specifically, we implement unconditional and conditional VAEs to generate jerk signals that integrate features from different drivetrain scenarios. The VAEs are trained on experimental data collected from two variants of a fully electric SUV, which differ in maximum torque delivery and drivetrain configuration. New meaningful jerk signals are generated within an engineering context through the interpretation of the VAE’s latent space. A performance comparison with baseline physics-based and hybrid models confirms the effectiveness of the VAEs. We show that VAEs bypass the need for exhaustive manual system parametrization while maintaining physical plausibility by conditioning data generation on specific inputs. © 2025, CC BY.

关键词： Signal encoding

BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: dataSETS, MODELS, TOOLS, AND METRICS FOR CROSS-dataSET GENERALIZATION ANALYSIS

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

Biomedical data and AI

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Science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Mesoscale Wind Farm Placement via Linear Optimization Constrained by Power System and Techno-economics

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 356-366页

作者： Ali Erduman Bahri Uzunoglu Bedri Kekezoglu Ali Durusu the Electrical Engineering Department Hakkari UniversityHakkari30000 Turkey Department of Engineering Sciences Department of Electrical EngineeringComputational and Data-enabled Science and Engineering in Energy SystemsUppsala University75121 UppsalaSweden Department of Mathematics Florida State UniversityTallahasseeFL 32310USA the Electrical Engineering Department Yildiz Technical UniversityIstanbul34220 Turkey

The objective of this study is to develop a wind farm placement and investment methodology based on a linear optimization *** problem has a major significance for the investment success for the projects of renewable energy such as wind *** this study,a mesoscale approach is adopted whereby the wind farm location is investigated in comparison with a microscale approach where the location of each individual turbine is *** study focuses on the placement of a wind farm by economical optimization constrained by the power system,wind resources,and *** optimization is introduced in this context at the power system which is constrained by wind farm planning.

关键词： Generation expansion grid integration mesoscale wind farm linear optimization wind power

Hierarchical data Reduction and Learning∗

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arXiv 2019年

作者： Shekhar, Prashant Patra, Abani Computational and Data Enabled Sciences University at Buffalo Computational and Data Enabled Sciences Department of Mechanical and Aerospace Engineering University at Buffalo

Paper proposes a hierarchical learning strategy for generation of sparse representations which capture the information content in large datasets and act as a model. The hierarchy arises from the approximation spaces considered at successively finer data dependent scales. Paper presents a detailed analysis of stability, convergence and behavior of error functionals associated with the approximations and well chosen set of applications. Results show the performance of the approach as a data reduction mechanism on both synthetic (univariate and multivariate) and real datasets (geo-spatial, computer vision and numerical model outcomes). The sparse model generated is shown to efficiently reconstruct data and minimize error in prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： data reduction

GRINS: A multiphysics framework based on the libMesh finite element library

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GRINS: A multiphysics framework based on the libMesh finite ...

作者： Bauman, Paul T. Stogner, Roy H. Mechanical and Aerospace Engineering Computational and Data-Enabled Science and Engineering University at Buffalo State University of New York BuffaloNY14260-4400 United States Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712 United States

This paper describes a flexible C++ software framework, called GRINS, for simulating complex multiphysics systems of partial differential equations using the finite element method. GRINS is designed to facilitate the rapid development and reuse of finite element formulation code as well as other features of complex simulations, including quantity-of-interest functionals, solvers, boundary conditions, initial conditions, and postprocessing. GRINS is built on the FEMSystem framework that is part of the libMesh finite element library. GRINS, through FEMSystem and libMesh, supports a number of contemporary algorithms and computing features including hybrid distributed and shared memory parallelism, parallel adaptive mesh refinement on unstructured grids, goal-oriented refinement driven by dual-weighted residual error estimates in quantities of interest, and adjoint-based parameter sensitivities. GRINS is deployed both as an application binary, allowing immediate use of existing features, and as a library to which the user may build and link a stand-alone application, extending the framework to their needs. Several examples demonstrate the effectiveness of the design, including uncertainty quantification applications. Planned developments are also discussed. © 2016 Society for Industrial and Applied Mathematics.

关键词： Multiphysics

PREDICTING MICROSTRUCTURE-PROPERTY OF SILICA AEROGEL MATERIALS VIA BAYESIAN CONVOLUTIONAL NEURAL NETWORKS SURROGATE MODEL

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PREDICTING MICROSTRUCTURE-PROPERTY OF SILICA AEROGEL MATERIA...

ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Islam, Md Azharul Deighan, Dwyer Scout Faghihi, Danial Department of Mechanical Aerospace Engineering University at Buffalo BuffaloNY14260 United States Department of Computational Data Enabled Sciences University at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9780791888681

Deep neural networks have become essential for developing data-driven surrogate models of complex multiscale and multiphysics simulations. Trained with high-fidelity simulation data, these surrogate models enable computational predictions with significantly reduced time and resources compared to physics-based simulations. Surrogate models based on Convolutional Neural Networks (CNNs) are emerging as powerful tools for learning the complex relationships between microstructural images and the corresponding macroscopic properties of materials, facilitating tasks once computationally prohibitive, such as optimal design and synthesizing materials with target properties. However, the common neural network training method, relying on maximum likelihood parameter estimation, limits CNNs’ ability to handle uncertainty due to sparse and limited high-fidelity data generated by physics-based simulations. This often leads to overfitting and overly confident predictions, compromising the reliability of CNNs, especially in high-consequence tasks such as model-based material design. This contribution proposes a Bayesian CNN for surrogate modeling by treating the network’s training as statistical inference to overcome the formidable challenge of uncertainty assessment in predictions provided by neural network-based models. We employ Variational Inference to introduce probability distributions over the CNN’s weights, ensuring accurate uncertainty estimation. The proposed Bayesian CNN surrogate model is applied to learn microstructure image-mechanical property relations in silica aerogel porous materials, known for its superior insulation properties but suffer from low mechanical strength. Training data is obtained from elastic deformation simulations of the solid phase in the porous materials governed by stochastic partial differential equations. Results demonstrate the effectiveness of the Bayesian CNN in predicting the strain energy corresponding to a given microstructure image whi

关键词： Maximum likelihood estimation