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Quantifying Electrochemical Processes in Batteries and Beyond

Energy & Environmental materials 2022年第4期5卷 1001-1004页

作者： Emma N.Antonio Michael F.Toney Materials Science and Engineering Program University of Colorado BoulderBoulderColorado 80309USA Joint Center for Energy Storage Research(JCESR) University of Colorado BoulderBoulderColorado 80309USA Department of Chemical and Biological Engineering University of Colorado BoulderBoulderColorado 80309USA Renewable and Sustainable Energy Institute University of Colorado BoulderBoulderColorado 80309USA

The correlation of electrochemical measurements with materials characterization has advanced our understanding of operation and degradation mechanisms in electrochemical energy storage and many other ***,often these correlations are qualitative,preventing the unambiguous identification of both operational principles and the root causes of performance *** we suggest quantitative approaches to define competing mechanisms and determine their relative *** illustrate the importance of quantitative methodologies over a range of electrochemical systems and highlight the need to consider the effect of the experimental design and measurement *** approaches will reveal the most detrimental degradation mechanisms and enable the development of strategies to suppress,stabilize or eliminate them,leading to materials and devices with longer lifetimes,reduced environmental impact,and improved performance.

关键词： electrochemistry in situ and operando characterization batteries quantification materials science

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Optothermal Manipulation at Fluid Interfaces

Optothermal Manipulation at Fluid Interfaces

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Conference on Lasers and Electro-Optics (CLEO)

作者： Youngsun Kim Carolina Ponce Zhihan Chen Yuebing Zheng Materials Science & Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX USA Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX USA

Optothermal manipulation of particles at fluid interfaces was studied with a film of gold nanoparticles assembled at aqueous/oil interfaces. The unique optothermal behaviors of particles and fluid interfaces under opt... 详细信息

ISBN: (纸本)9781957171258

关键词： Gold Laser beams Laser irradiation Light scattering Optical force Optical tweezers

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Growth, optimization and high-temperature performance of GeO2/Ga2O3 MOSCAPs

SSRN

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SSRN 2024年

作者： Manikanthababu, N. Karmakar, Subrata Shiam, Ishtiaq Firoz Emu, Injamamul Hoque Haque, Ariful Droopad, Ravi Electrical Engineering Ingram School of Engineering Texas State University San MarcosTX78666 United States Materials Science Engineering & Commercialization Program Texas State University San MarcosTX78666 United States

In this study, GeO2 thinfilms were grown by pulsed laser deposition (PLD) technique on β-Ga2O3, (―201) single crystal substrates to fabricate metal-oxide-semiconductor capacitors (MOSCAPs), and test these devices at high temperatures. The GeO2 thinfilms with bandgap of 5.11 eV are amorphous in nature, confirmed by X-ray diffraction, and the X-ray photoelectron spectroscopy (XPS) confirms the formation of GeO2. The Ge 3d deconvoluted peak at 32.4 eV confirms the GeO2 bonding, with an additional peak found at 30.9 eV resembling a small portion of GeO2-x. The Au/GeO2/Ga2O3 MOSCAPs were fabricated to test the high-temperature performance up to 300 oC. The I–V graphs showed an increase in the reverse leakage current by 3.9×104 A (nearly four orders of magnitude) as the temperature raised from room temperature (RT) to 300 oC. Since the GeO2-x presence, the current conduction mechanism of Poole-Frenkel emission was utilized to get the trap level of 0.8 V (below the conduction band of GeO2) with an activation energy of 0.55 eV. C–V measurements also show a significant contribution of defects in the flat-band voltage shift and the change of the slopes. This indicates an increase in the oxide and interface-trapped charges. The density of oxide-trapped charges (DO) increased from 3.9×1012 cm-2 to 1.3×1013 cm-2, as the temperature reached 300 oC. Similarly, the density of interface-trapped charges (DI) increased from 3.4×1012 cm-2 at RT to 1.1×1013 cm-2 at 300 oC. © 2024, The Authors. All rights reserved.

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Rice Variety Identification Using Edge Feature Extraction and Texture-Based Segmentation

Rice Variety Identification Using Edge Feature Extraction an...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Setiawan, Aji Adi, Kusworo Widodo, Catur Edi Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Faculty of Engineering Darma Persada University Department of Information Technology Jakarta Indonesia Diponegoro University School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia

ISBN: (纸本)9798350339215

This research proposes a methodology for identifying the optimal feature combination using Support Vector Machine (SVM) based on edge and texture features. Canny, Sobel, and Prewitt for edge detection and GLCM, Gabor, and LBP for texture analysis are popular feature extraction techniques widely employed in various cases. The approach combines edge and texture features with SVM to classify rice types into five categories: Ipsala, Jasmine, Karacadag, Arborio, and Basmati. Experimental results demonstrate that SVM using Canny, Sobel, and Prewitt combined Gabor texture features outperforms the comparative methods. A comparative analysis based on accuracy scores using confusion matrices is conducted. Combining all feature edge methods, Canny, Sobel, and Prewitt with Gabor on SVM classification is the best model for rice object classification, achieving an accuracy of 96%. © 2023 IEEE.

关键词： Support vector machines

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Vortex Formation of Active Particles within a Confined Geometry by Velocity Alignment

Vortex Formation of Active Particles within a Confined Geome...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Zhihan Chen Hongru Ding Yuebing Zheng Materials Science & Engineering Program and Texas Materials Institute The University of Texas at Austin Austin TX USA Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX USA

we have developed an optical feedback control system with synchronous control of active particles to study collective motion in nature. Fish-like vortex is observed within a circular confined geometry based on the par... 详细信息

ISBN: (纸本)9781957171258

关键词： Coherence Light fields Optical feedback Polarization Rubidium Spatial light modulators

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Piezoelectricity of strain-induced overall water splitting of Ni(OH)2/MoS2 heterostructure

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Journal of materials Chemistry A 2023年第7期11卷 3481-3492页

作者： Le, Kim Tuyen Pham, Nguyet N. T. Liao, Yin-Song Ranjan, Ashok Lin, Hsun-Yen Chen, Po-Han Nguyen, Hoang Lu, Ming Yen Lee, Seung Geol Wu, Jyh Ming Department of Materials Science and Engineering National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan Department of Chemistry University of Science Vietnam National University Ho Chi Minh City Viet Nam School of Chemical Engineering Pusan National University 2 Busandaehak-ro 63 beon-gil Geumjeong-gu Busan46241 Korea Republic of Department of Organic Material Science and Engineering Pusan National University 2 Busandaehak-ro 63 beon-gil Geumjeong-gu Busan46241 Korea Republic of PhD Program in Prospective Functional Materials Industry National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan High Entropy Materials Center National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City700000 Viet Nam

In this study, we synthesized several active sites of molybdenum disulfide (MoS2) nanoflowers (NFs) decorated with Ni(OH)2 nanoparticles (NPs) to form heterostructures. Our results revealed that Ni(OH)2/MoS2 NF heterostructures caused the splitting of water into hydrogen (H2) and oxygen (O2) gas. The Ni(OH)2/MoS2 NF interface exhibited strain-induced piezoelectric potential (piezopotential) that promoted charge separation, reducing the kinetic barrier in the water splitting process and promoting the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). In high-resolution transmission microscopy images, the measured lattice fringe d spacing was approximately 0.67 nm, which was higher than the reported value. The calculated tensile strain of Ni(OH)2/MoS2 NF heterostructures was approximately 3.5%. The findings based on density-functional theory and using the finite element method indicated that the interfacial piezopotential generated between Ni(OH)2 and MoS2 NFs enhanced charge transfer and electrochemical reactions. To obtain a current density up to 10 mA cm−2, we set the overpotential and Tafel slope of Ni(OH)2/MoS2 NF heterostructures at 155 mV and 62.1 mV dec−1 for the HER and 328 mV and 69.3 mV dec−1 for the OER, respectively. Moreover, a high Faraday efficiency (84.1%) was observed over 48 h, with no significant decline in the HER and OER. © 2023 The Royal Society of Chemistry.

关键词： Molybdenum disulfide

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Machine Learning for Identifying Grain Boundaries in Scanning Electron Microscopy (SEM) Images of Nanoparticle Superlattices

arXiv

引用

arXiv 2025年

作者： Paruchuri, Aanish Thrasher, Carl Hart, A.J. Macfarlane, Robert Jayaraman, Arthi Science in Data Science Program University of Delaware NewarkDE19713 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering University of Delaware 150 Academy St NewarkDE19713 United States Department of Materials Science and Engineering University of Delaware NewarkDE19713 United States Data Science Institute University of Delaware NewarkDE19713 United States

Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes. © 2025, CC BY.

关键词： Grain boundaries

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A Runaway Electron Avalanche Surrogate for Partially Ionized Plasmas

arXiv

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arXiv 2025年

作者： Arnaud, Jonathan S. McDevitt, Christopher J. Tang, Xian-Zhu Nuclear Engineering Program Department of Materials Science and Engineering University of Florida GainesvilleFL32611 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

A physics-constrained deep learning surrogate that predicts the exponential "avalanche" growth rate of runaway electrons (REs) for a plasma containing partially ionized impurities is developed. Specifically, a physics-informed neural network (PINN) that learns the adjoint of the relativistic Fokker-Planck equation in steady-state is derived, enabling a rapid surrogate of the RE avalanche for a broad range of plasma parameters, motivating a path towards an ML-accelerated integrated description of a tokamak disruption. A steady-state power balance equation together with atomic physics data is embedded directly into the PINN, thus limiting the PINN to train across physically consistent temperatures and charge state distributions. This restricted training domain enables accurate predictions of the PINN while drastically reducing the computational cost of training the model. In addition, a novel closure for the relativistic electron population used when evaluating the secondary source of REs is developed that enables improved accuracy compared to a Rosenbluth-Putvinski source. The avalanche surrogate is verified against Monte Carlo simulations, where it is shown to accurately predict the RE avalanche growth rate across a broad range of plasma parameters encompassing distinct tokamak disruption scenarios. © 2025, CC BY.

关键词： Fokker Planck equation

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Infrared Spectra Prediction for Diazo Groups Utilizing a Machine Learning Approach with Structural Attention Mechanism

arXiv

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arXiv 2024年

作者： Liu, Chengchun Mo, Fanyang School of Materials Science and Engineering Peking University Beijing100871 China -Preferred Program Peking University Shenzhen Graduate School Shenzhen518055 China

Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, the intricate molecular fingerprints characterized by unique vibrational and rotational patterns present substantial analytical challenges. Here, we present a machine learning approach employing a Structural Attention Mechanism tailored to enhance the prediction and interpretation of infrared spectra, particularly for diazo compounds. Our model distinguishes itself by honing in on chemical information proximal to functional groups, thereby significantly bolstering the accuracy, robustness, and interpretability of spectral predictions. This method not only demystifies the correlations between infrared spectral features and molecular structures but also offers a scalable and efficient paradigm for dissecting complex molecular interactions. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular structure

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Decision Support System in Determining the Achievement of Widyaiswara Value Using WASPAS and TOPSIS Algorithm 1

Decision Support System in Determining the Achievement of Wi...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Pratama, Yudhistira Adhitya Iswara, Bagus Ajie Nababan, Adli Abdillah Information System Studies Program Pelita Harapan University Medan Campus Faculty Of Computer Science Medan Indonesia Master's Study Program Informatics Engineering Universitas Sumatera Utara Faculty Of Computer Science And Information Technology Medan Indonesia Digital Business Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Widyaiswara is required to show the best performance to fulfill his duties and obligations. Therefore, it is very important to measure the performance of the Widyaiswara, so that it can be used as evaluation material by the BPSDM PROVSU. However, the assessment process that occurs is still not precise and time-consuming. Therefore, to minimize this, a decision support system is needed that can minimize time errors and data processing in decision-making so that the assessment runs well and accurately. This system was built by applying the WASPAS method and the TOPSIS method. Based on the results of tests carried out five times, the results of the WASPAS method processing time resulted in an average processing time of 0.00029791 ms and an average memory usage value of 2.836.44 kb, while the results of the TOPSIS method processing time test resulted in an average processing time - an average of 0.000296428 ms and an average memory usage value of 2.862.34 kb. © 2022 IEEE.

关键词： Decision support systems

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