M
Miaofang Chi
Researcher at Oak Ridge National Laboratory
Publications - 343
Citations - 28685
Miaofang Chi is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Catalysis & Medicine. The author has an hindex of 77, co-authored 304 publications receiving 22817 citations. Previous affiliations of Miaofang Chi include University of California, Davis & Lawrence Livermore National Laboratory.
Papers
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Journal ArticleDOI
Novel Acid Catalysts from Waste‐Tire‐Derived Carbon: Application in Waste–to‐Biofuel Conversion
Zachary D. Hood,Zachary D. Hood,Shiba P. Adhikari,Yunchao Li,Yunchao Li,Amit K. Naskar,Amit K. Naskar,Legna Figueroa-Cosme,Younan Xia,Younan Xia,Miaofang Chi,Marcus W. Wright,Abdou Lachgar,M. Parans Paranthaman,M. Parans Paranthaman +14 more
TL;DR: In this article, waste tires were converted to hard carbon, then functionalized with catalytically active -SO3H groups on the surface through an environmentally benign process that involved the sequential treatment with L-cysteine, dithiothreitol, and H2O2.
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NixWO2.72 nanorods as an efficient electrocatalyst for oxygen evolution reaction
Zheng Xi,Adriana Mendoza-Garcia,Huiyuan Zhu,Miaofang Chi,Dong Su,Daniel P. Erdosy,Junrui Li,Shouheng Sun +7 more
TL;DR: Ni x WO 2.72 nanorods (NRs) were synthesized by a one-pot reaction of Ni(acac) 2 and WCl 4.
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Segregation of Mn2+ Dopants as Interstitials in SrTiO3 Grain Boundaries
TL;DR: In this paper, the defect mediated electrical and magnetic properties of transition metal-doped perovskites have been investigated and Mn4+ is found to substitute Ti in bulk SrTiO3, but Mn2+ segregates inside grain boundaries at both Sr and interstitial sites.
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Point defect characterization in HAADF-STEM images using multivariate statistical analysis.
TL;DR: Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis, consisting of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images.
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Rapid aberration measurement with pixelated detectors
TL;DR: A method to measure aberrations based on acquiring a 4D data set on a pixelated detector is demonstrated and represents a step towards more automated electron microscopy.