4.5 Article

The Effect of Aromatic and Sulfur Compounds on Partial Discharge Characteristics of Hexadecane

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TDEI.2013.6518950

关键词

Streamer; partial discharge; phase resolved PD pattern; dielectric fluid; additives; ionization potential; electron affinity; density functional theory; corrosive sulfur

资金

  1. BAUR Pruf- und Messtechnik GmbH

向作者/读者索取更多资源

Partial discharge (PD) characteristics of hexadecane were studied in a needle-plane electrode geometry under AC field with a 20 mu m tip radius tungsten needle. PD experiments were conducted on samples containing known concentrations of aromatic and corrosive sulfur compounds. Partial discharge inception voltage (PDIV), streamer repetition rates, and phase resolved PD (PRPD) patterns were acquired. Ionization potentials (IP) and electron affinities (EA) of hexadecane and the additives were calculated with density functional theory (DFT) and correlated with the PD characteristics. Low IP and negative EA of the additives relative to hexadecane increased the number of positive and negative streamers initiated, except for additives that contained an electron donating group on the aromatic ring. Low IP and positive EA for polyaromatic hydrocarbons (PAH) and corrosive sulfur compounds decreased the number of negative streamers initiated due to large electron capture cross sections. Copper sulfide particulates caused the greatest changes in PD characteristics as a result of its semi-conductive nature.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Materials Science, Multidisciplinary

Machine learning elastic constants of multi-component alloys

Vivek Revi, Saurabh Kasodariya, Anjana Talapatra, Ghanshyam Pilania, Alankar Alankar

Summary: This manuscript explores the application of machine learning methods in predicting elastic constants and mechanical properties of multi-component alloys. Various machine learning models are trained and tested on a dataset of binary alloys using density functional theory calculations, with a focus on feature selection and evaluating predictive performance. The study demonstrates accurate predictions and efficient approximation of elastic properties for alloys beyond binary space, and validates the model against experimentally measured elastic constants of technologically relevant multicomponent alloys. The utility of data-enabled prediction is further illustrated by predicting a wide range of elastic properties for a high-throughput manner in a five-component Ni-Cr-Fe-Mo-W alloy system.

COMPUTATIONAL MATERIALS SCIENCE (2021)

Article Materials Science, Multidisciplinary

Strong Zeeman splitting in orbital-hybridized valleytronic interfaces

Steven T. Hartman, Ghanshyam Pilania

Summary: This study investigates the valley splitting of eight low-strain interfaces of transition metal dichalcogenides stacked on 2D magnetic substrates using first-principle calculations. The results show that interlayer band hybridization plays a major role in the valley splitting, which is strongly dependent on the Hubbard U correction. The WSe2/CrGeTe3 interface has particularly strong interlayer interactions and can potentially lead to a high valley splitting.

JOURNAL OF MATERIALS SCIENCE (2022)

Article Chemistry, Physical

Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers

Karteek K. Bejagam, Jessica Lalonde, Carl N. Iverson, Babetta L. Marrone, Ghanshyam Pilania

Summary: This article discusses the potential of using machine learning techniques to establish efficient structure-property mappings in the chemical space of PHAs. An example of predicting melting temperature is used to demonstrate the promise of this approach.

JOURNAL OF PHYSICAL CHEMISTRY B (2022)

Article Polymer Science

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Karteek K. Bejagam, Nevin S. Gupta, Kwan-Soo Lee, Carl N. Iverson, Babetta L. Marrone, Ghanshyam Pilania

Summary: Polyhydroxyalkanoates (PHAs) are promising biosynthesizable, biocompatible, and biodegradable polymers that can replace petroleum-based plastics to address plastic pollution. However, the structure-property relationships and experimental data on the mechanical properties of PHAs are limited. In this study, molecular dynamics simulations were used to predict the mechanical properties of PHAs. The results show that Young's modulus and yield stress decrease with increasing carbon atom number in the side chain and polymer backbone. The mechanical properties are also strongly correlated with the chemical nature of the functional group.

POLYMERS (2022)

Article Materials Science, Multidisciplinary

A first-principles investigation of nitrogen reduction to ammonia on zirconium nitride and oxynitride surfaces

Amitava Banerjee, Bianca M. Ceballos, Cortney Kreller, Rangachary Mukundan, Ghanshyam Pilania

Summary: This study investigates the electrochemical synthesis of ammonia using density functional theory computation. The research shows that different surface types have a significant impact on the potential-determining step and surface nitrogen vacancy formation is consistently the rate-determining step.

JOURNAL OF MATERIALS SCIENCE (2022)

Editorial Material Materials Science, Multidisciplinary

Recent advances in computational materials design: methods, applications, algorithms, and informatics

Ghanshyam Pilania, Bryan R. Goldsmith, Mina Yoon, Avinash M. Dongare

JOURNAL OF MATERIALS SCIENCE (2022)

Article Chemistry, Physical

Metal Oxynitrides for the Electrocatalytic Reduction of Nitrogen to Ammonia

Samuel D. Young, Bianca M. Ceballos, Amitava Banerjee, Rangachary Mukundan, Ghanshyam Pilania, Bryan R. Goldsmith

Summary: This article discusses the potential of metal oxynitrides as a new material category for e-NRR and compares them with metal nitrides and metal oxides. The article focuses on the challenges faced by metal oxynitrides in e-NRR and provides an outlook for future research.

JOURNAL OF PHYSICAL CHEMISTRY C (2022)

Article Chemistry, Multidisciplinary

Defect thermodynamics in spinel oxides leading to plasmonic behavior

Steven T. Hartman, Ghanshyam Pilania

Summary: This study investigates the doping mechanisms of spinel-structured oxide materials using first-principles calculations. It finds that Ga2FeO4 and Cd2SnO4 can be doped by cation antisites, while Fe3O4 easily forms cation vacancies. By controlling the chemical potentials of different species, the defect concentration in these materials can be widely tuned. The less dopable nature of Al2FeO4 highlights the importance of careful design based on factors such as cation radius.

JOURNAL OF PHYSICS AND CHEMISTRY OF SOLIDS (2022)

Article Astronomy & Astrophysics

Physics-informed Machine Learning for Modeling Turbulence in Supernovae

Platon I. Karpov, Chengkun Huang, Iskandar Sitdikov, Chris L. Fryer, Stan Woosley, Ghanshyam Pilania

Summary: The article discusses the importance of turbulence in astrophysical phenomena and the challenges in simulating it. The authors have developed a physics-informed convolutional neural network using machine learning to predict turbulent pressure accurately. The study tests the applicability of this method in different turbulent conditions and aims to use it in core-collapse supernova simulations in the future.

ASTROPHYSICAL JOURNAL (2022)

Article Materials Science, Multidisciplinary

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Kamal Choudhary, Kevin F. Garrity, Steven T. Hartman, Ghanshyam Pilania, Francesca Tavazza

Summary: The authors developed a computational database, website applications, and machine-learning models to accelerate the design and discovery of 2D heterostructures. They generated possible bilayer heterostructures using density functional theory and classified them into three types based on band alignments. They analyzed the chemical trends and validated the results using experimental and hybrid-functional predictions. Web-apps and ML tools were developed for property prediction and band-alignment information. The analysis, results, and applications can be valuable in screening and designing alternative photocatalysts, photodetectors, and high-WF 2D-metal contacts.

PHYSICAL REVIEW MATERIALS (2023)

Article Materials Science, Multidisciplinary

Band gap predictions of double perovskite oxides using machine learning

Anjana Talapatra, Blas Pedro Uberuaga, Christopher Richard Stanek, Ghanshyam Pilania

Summary: The compositional and structural variety of oxide perovskites allows for a wide range of applications. Machine learning models are used to predict the band gap of perovskite compounds and identify stable and synthesizable compounds with desired band gaps.

COMMUNICATIONS MATERIALS (2023)

Article Materials Science, Multidisciplinary

Predicting and accessing metastable phases

V. Kocevski, J. A. Valdez, B. K. Derby, Y. Q. Wang, G. Pilania, B. P. Uberuaga

Summary: Introduces the importance of metastable forms of matter in our everyday lives, and explains that synthesizing these forms is more of an art than a science. Calculates metastable phase diagrams and extracts the metastability threshold to aid in their fabrication. Uses lanthanide sesquioxides (Ln(2)O(3)) as a case study to demonstrate the insight provided by metastable phase diagrams and predict the sequence of metastable phases induced by irradiation in Lu2O3.

MATERIALS ADVANCES (2023)

Article Chemistry, Physical

How inversion relates to disordering tendencies in complex oxides

Vancho Kocevski, Ghanshyam Pilania, Blas P. Uberuaga

Summary: Complex oxides exhibit great functionality due to their varied chemistry and structures. This study introduces a simple metric that correlates the propensity for cation disordering in perovskites, pyrochlores, and spinels with the energy to invert the cation structure. The metric provides a fast and robust way to determine the ease or difficulty of cation disordering, enabling quick screening of compounds for cation-ordering-dependent functionalities.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2023)

Article Materials Science, Multidisciplinary

Bioplastic design using multitask deep neural networks

Christopher Kuenneth, Jessica Lalonde, Babetta L. L. Marrone, Carl N. N. Iverson, Rampi Ramprasad, Ghanshyam Pilania

Summary: This study develops multitask deep neural network property predictors to identify potential replacements for petroleum-based commodity plastics. By using the predictors, 14 PHA-based bioplastics are identified from a diverse set of chemistries, with possible synthesis routes discussed.

COMMUNICATIONS MATERIALS (2022)

Article Materials Science, Multidisciplinary

Accurately predicting optical properties of rare-earth, aluminate scintillators: influence of electron-hole correlation

Christopher N. Singh, Ghanshyam Pilania, Jan Barta, Blas Pedro Uberuaga, Xiang-Yang Liu

Summary: This study emphasizes the importance of many-particle corrections in simulating luminescent profiles of rare-earth perovskite scintillators, revealing significant differences in excitation wavelengths compared to traditional approaches. Understanding the trade-off between accuracy and performance of various theoretical tools is crucial in defining search parameters for new scintillator development.

JOURNAL OF MATERIALS CHEMISTRY C (2021)

暂无数据