4.8 Article

Identifying the Chemical Origin of Oxygen Redox Activity in Li-Rich Anti-Fluorite Lithium Iron Oxide by Experimental and Theoretical X-ray Absorption Spectroscopy

Journal

JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 10, Issue 4, Pages 806-812

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.8b03271

Keywords

-

Funding

  1. Center for Electrochemical Energy Science, an Energy Frontier Research Center - U.S. Department of Energy, Office of Science, Basic Energy Sciences [DE-AC02-06CH11]
  2. U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-06CH11357]
  3. National Science Foundation [ACI-1053575]
  4. Office of Science of the U.S. Department of Energy (DOE) [DE-AC02-05CH11231]

Ask authors/readers for more resources

Harnessing oxygen redox reactions is an intriguing route to increasing capacity in Li-ion batteries (LIBs). Despite numerous experimental and theoretical attempts to unravel the mechanism of oxygen redox behavior, the electronic origin of oxygen activities in energy storage of Li-rich LIB materials remains under intense debate. In this work, the onset of oxygen activity was examined using a Li-rich material that has been reported to exhibit oxygen redox, namely, Li5FeO4. By comparing experimental measurements and first-principles Bethe-Salpeter equation calculations of oxygen K-edge X-ray absorption spectra (XAS), it was found that experimentally-observed changes in XAS originate from the non-bonding oxygen states in cation-disordered delithiated Li5FeO4, and the spectral features of oxygen dimers were also determined. This combined experimental and theoretical study offers an effective approach to disentangle the intertwined signals in XAS and can be further utilized in broader contexts for characterizing other energy storage and conversion materials.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Chemistry, Physical

Exploring the Origin of Anionic Redox Activity in Super Li-Rich Iron Oxide-Based High-Energy-Density Cathode Materials

Zhenpeng Yao, Maria K. Y. Chan, Chris Wolverton

Summary: This study investigates the delithiation and (re)lithiation reactions of super alkali-rich material Li5FeO4 using first-principles calculations. The study reveals non-equilibrium pathways during the charge and discharge processes. A phase transformation from tetrahedrally coordinated to octahedral coordinated structure is observed upon delithiation, with asymmetric kinetic barrier for Fe-ion migration explaining the difficulties in reaction reversibility.

CHEMISTRY OF MATERIALS (2022)

Article Chemistry, Physical

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Maciej P. Polak, Ryan Jacobs, Arun Mannodi-Kanakkithodi, Maria K. Y. Chan, Dane Morgan

Summary: Using multi-fidelity datasets and a machine learning approach, we have significantly reduced the time required for predicting impurity transition levels in semiconductors. The model trained on these datasets shows improved accuracy and does not require high-fidelity values, reducing computational cost. This approach has the potential to predict transition levels in various semiconductor materials.

JOURNAL OF CHEMICAL PHYSICS (2022)

Article Materials Science, Multidisciplinary

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

Arun Mannodi-Kanakkithodi, Maria K. Y. Chan

Summary: The combination of halide perovskites, high-throughput computations, and machine learning shows great promise in providing novel materials for solar cell and optoelectronic technologies. By using density functional theory (DFT) calculations and machine learning algorithms, we can predict and identify impurity atoms that have optoelectronic activity. This accelerated screening can help in identifying problematic impurities and tuning the conductivity and photovoltaic absorption of perovskite materials.

JOURNAL OF MATERIALS SCIENCE (2022)

Article Electrochemistry

Effect of Electrolytes on the Cathode-Electrolyte Interfacial Stability of Fe-Based Layered Cathodes for Sodium-Ion Batteries

Jehee Park, Kyojin Ku, Seoung-Bum Son, Jihyeon Gim, Youngsik Kim, Eungje Lee, Christopher Johnson

Summary: This study investigates the effect of electrolytes on the electrochemical performance of Fe-based layered oxide cathodes in sodium-ion batteries. The poor cathode-electrolyte interfacial stability is found to critically impact cell performance, with the reactive Fe4+ state leading to electrolyte decomposition and impedance rise. NaPF6 shows superior performance over NaClO4, and the FEC additive has a beneficial effect on cathode stability.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2022)

Article Chemistry, Multidisciplinary

Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into Experiments

Eric Schwenker, Venkata Surya Chaitanya Kolluru, Jinglong Guo, Rui Zhang, Xiaobing Hu, Qiucheng Li, Joshua T. Paul, Mark C. Hersam, Vinayak P. Dravid, Robert Klie, Jeffrey R. Guest, Maria K. Y. Chan

Summary: This paper introduces an open-source automation framework called ingrained, which solves the correspondence between simulation and experimental images and fuses atomic resolution image simulations into the corresponding experimental images.

SMALL (2022)

Article Energy & Fuels

Origin and regulation of oxygen redox instability in high-voltage battery cathodes

Xiang Liu, Gui-Liang Xu, Venkata Surya Chaitanya Kolluru, Chen Zhao, Qingtian Li, Xinwei Zhou, Yuzi Liu, Liang Yin, Zengqing Zhuo, Amine Daali, Jing-Jing Fan, Wenjun Liu, Yang Ren, Wenqian Xu, Junjing Deng, Inhui Hwang, Dongsheng Ren, Xuning Feng, Chengjun Sun, Ling Huang, Tao Zhou, Ming Du, Zonghai Chen, Shi-Gang Sun, Maria K. Y. Chan, Wanli Yang, Minggao Ouyang, Khalil Amine

Summary: The presence of domain boundaries in single-crystal cathodes hinders the redox stability of oxygen at high voltages. Eliminating domain boundaries enhances reversible lattice oxygen redox and inhibits irreversible oxygen release, leading to improved electrochemical performance.

NATURE ENERGY (2022)

Article Instruments & Instrumentation

AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning

In Hui Hwang, Mikhail A. Solovyev, Sang Wook Han, Maria K. Y. Chan, John P. Hammonds, Steve M. Heald, Shelly D. Kelly, Nicholas Schwarz, Xiaoyi Zhang, Cheng Jun Sun

Summary: The Argonne X-ray Emission Analysis Package (AXEAP) can convert X-ray emission spectroscopy (XES) data in real time, reducing the amount of stored data, and includes data processing for non-resonant and resonant XES images.

JOURNAL OF SYNCHROTRON RADIATION (2022)

Article Multidisciplinary Sciences

Discovery of chalcogenides structures and compositions using mixed fluxes

Xiuquan Zhou, Venkata Surya Chaitanya Kolluru, Wenqian Xu, Luqing Wang, Tieyan Chang, Yu-Sheng Chen, Lei Yu, Jianguo Wen, Maria K. Y. Chan, Duck Young Chung, Mercouri G. Kanatzidis

Summary: Advancements in modern technologies rely on materials discovery, which in turn requires the development of synthesis science. This study presents an efficient methodology for rational material discovery using high-temperature solutions or fluxes with adjustable solubility. By systematically varying temperature and flux ratios, the researchers were able to synthesize previously unreported compounds and determine their structural characteristics. This methodology provides a general strategy for the rational discovery of inorganic solids.

NATURE (2022)

Article Chemistry, Physical

Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9

Srisuda Rojsatien, Arun Mannodi-Kanakkithodi, Trumann Walker, Tara Nietzold, Eric Colegrove, Barry Lai, Zhonghou Cai, Martin Holt, Maria K. Y. Chan, Mariana Bertoni

Summary: X-ray absorption near edge structure (XANES) coupled with X-ray microscopy is a powerful tool to probe the fingerprint of local structures. This study investigates the use of linear combination fitting (LCF) of XANES spectra for Cu doping in CdTe. The results show that the experimental data can be accurately represented by Cu2Te and Cu1.43Te standards. The study also proposes a framework to semi-quantitatively study local structures using binary mixtures of simulated standards.

RADIATION PHYSICS AND CHEMISTRY (2023)

Article Chemistry, Physical

High-voltage deprotonation of layered-type materials as a newly identified cause of electrode degradation

Junghoon Yang, Sungwon Park, Sungsik Lee, Jungpil Kim, Di Huang, Jihyeon Gim, Eungje Lee, Gilseob Kim, Kyusung Park, Yong-Mook Kang, Eunsu Paek, Sang-Don Han

Summary: Further development of electrochemical devices and electric vehicles requires advanced secondary batteries with higher energy density, longer lifetime and enhanced thermal safety. Increasing the cell operating voltage can extend the energy density but may lead to irreversible structural changes, mechanical failure, and parasitic reactions at the electrode-electrolyte interface, causing capacity fading, battery failure, and safety issues.

JOURNAL OF MATERIALS CHEMISTRY A (2023)

Article Chemistry, Physical

Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns

Joydeep Munshi, Alexander Rakowski, Benjamin H. Savitzky, Steven E. Zeltmann, Jim Ciston, Matthew Henderson, Shreyas Cholia, Andrew M. Minor, Maria K. Y. Chan, Colin Ophus

Summary: A fast and robust pipeline for strain mapping of crystalline materials is crucial for technological applications. We propose a deep-learning method using a Fourier space, complex-valued deep-neural network to invert complex electron diffraction patterns. Our method, trained with a large number of samples, outperforms conventional analysis methods in both simulated and experimental datasets.

NPJ COMPUTATIONAL MATERIALS (2022)

Article Chemistry, Multidisciplinary

Multifunctional Coatings on Sulfide-Based Solid Electrolyte Powders with Enhanced Processability, Stability, and Performance for Solid-State Batteries

Zachary D. Hood, Anil U. Mane, Aditya Sundar, Sanja Tepavcevic, Peter Zapol, Udochukwu D. Eze, Shiba P. Adhikari, Eungje Lee, George E. Sterbinsky, Jeffrey W. Elam, Justin G. Connell

Summary: Sulfide-based solid-state electrolytes (SSEs) have high ionic conductivity and favorable mechanical properties, making them promising for next-generation solid-state batteries. Thin Al2O3 coatings grown on Li6PS5Cl powders using atomic layer deposition simultaneously address the stability issues and improve cell performance. These coated powders exhibit higher ionic conductivities, lower electronic conductivities, and improved stability at the Li-SSE interface, leading to significantly improved battery cycle life.

ADVANCED MATERIALS (2023)

Article Materials Science, Multidisciplinary

Theory plus AI/ML for microscopy and spectroscopy: Challenges and opportunities

Davis Unruh, Venkata Surya Chaitanya Kolluru, Arun Baskaran, Yiming Chen, Maria K. Y. Chan

Summary: Advances in instrumentation have resulted in a vast amount of information on materials chemistry, structures, and transformations, but interpreting microscopy and spectroscopy data is becoming more challenging due to their growing volume and complexity. This article discusses the use of theoretical modeling, artificial intelligence/machine learning (AI/ML), and AI/ML combined with theory for interpreting microscopy and spectroscopy data.

MRS BULLETIN (2023)

Article Chemistry, Multidisciplinary

Data-driven design of novel halide perovskite alloys

Arun Mannodi-Kanakkithodi, Maria K. Y. Chan

Summary: This study develops a framework based on high-throughput computations and machine learning to design and predict mixed cation halide perovskite alloys. By simulating and computing multiple properties, including stability and optical properties, 392 promising compounds were screened as potential absorbers, revealing compositional trends in mixed cation halide perovskites.

ENERGY & ENVIRONMENTAL SCIENCE (2022)

Review Chemistry, Multidisciplinary

Understanding, discovery, and synthesis of 2D materials enabled by machine learning

Byunghoon Ryu, Luqing Wang, Haihui Pu, Maria K. Y. Chan, Junhong Chen

Summary: Machine learning is a valuable tool in the study of 2D materials, enabling predictions and discoveries that lead to a better understanding of their properties. It accelerates research and reduces costs.

CHEMICAL SOCIETY REVIEWS (2022)

No Data Available