Chenhao Ge
University of Arizona
10 Papers
81 Citations
Chenhao Ge is an academic researcher from University of Arizona. The author has contributed to research in topics: Environmental science & Biology. The author has an hindex of 4, co-authored 5 publications.
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Papers
pH-sensing properties of poly(aniline) ultrathin films self-assembled on indium-tin oxide.
TL;DR: The structural and functional properties of ultrathin (<5 nm) poly(aniline) (PANI) films deposited on indium-tin oxide (ITO) have been investigated using electrochemical and attenuated total reflection (ATR) spectroscopy methods.
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Voltammetric and waveguide spectroelectrochemical characterization of ultrathin poly(aniline)/poly(acrylic acid) films self-assembled on indium-tin oxide.
TL;DR: The ATR spectra indicate that during an anodic sweep, the leucoemeraldine form of PANI in these films is oxidized to generate both the emeraldine and pernigraniline forms simultaneously.
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Compound and successive events of extreme precipitation and extreme runoff under heatwaves based on CMIP6 models.
Peng Sun,Yifan Zou,R. Yao,Zi Qiang Ma,Yaojin Bian,Chenhao Ge,Yin-Liang Lv +6 more
TL;DR: In this article , the authors revealed the evolution characteristics of compound heatwave and extreme precipitation/runoff events (CHP/CHR), successive heatwave (SHP/SHR), and SHR/CHR events.
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Characterization of proton transport across a waveguide-supported lipid bilayer.
Todd W. McBee,Liying Wang,Chenhao Ge,Brooke M. Beam,Ana L. Moore,Devens Gust,Thomas A. Moore,Neal R. Armstrong,S. Scott Saavedra +8 more
TL;DR: A novel conductive polymer/planar waveguide assembly that provides for highly sensitive transduction of proton transport across a planar-supported lipid bilayer (PSLB), adaptable to many different types of transmembrane transport chemistries, particularly light-activated systems.
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Improving recommender system via knowledge graph based exploring user preference
TL;DR: An end-to-end framework to improve the recommender system via a knowledge graph based on fusing entity relation(KGFER), which can sufficiently capture the users’ preferences and outperforms state-of-the-art baselines.
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