Comprehensive theoretical study on ionic transport numbers of triple ionic-electronic conducting oxides determined by the electromotive force method

Qiu, Ruiming; Huang, Yue; Mo, Yingyu; Dong, Lexian; Tian, Zhipeng; Wang, Junyao; Liu, Jianping; Wang, Chao; Chen, Ying; Huang, Jin; Lei, Libin

doi:10.1016/j.jpowsour.2024.234924

Journal of Power Sources

2024

DOI: 10.1016/j.jpowsour.2024.234924

|Get access via publisher |Cite

Comprehensive theoretical study on ionic transport numbers of triple ionic-electronic conducting oxides determined by the electromotive force method

Ruiming Qiu

Yue Huang

Yingyu Mo

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2025

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Numerical study on current leakage of a tubular protonic ceramic fuel cell

Huang¹,

Shi²,

Liu³

et al. 2025

Journal of Power Sources

Get access via publisher Add to dashboard Cite

Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

Numerical study on current leakage of a tubular protonic ceramic fuel cell

Huang¹,

Shi²,

Liu³

et al. 2025

Journal of Power Sources

Get access via publisher Add to dashboard Cite

Scite is an AI-powered research tool that helps researchers better discover and evaluate scientific literature through Smart Citations—a revolutionary system that shows whether articles support, contrast, or simply mention a given claim. Founded in 2018, and now part of Research Solutions, Scite has indexed over 1.3 billion citations and partnered with more than 30 major publishers to provide researchers with unparalleled access to scientific literature. With its Scite Assistant, Smart Citation Index, and advanced search capabilities, the platform addresses critical challenges such as information overload and research reproducibility. Trusted by two million active users worldwide, Scite is reshaping how researchers interact with scholarly content—building ethical, transparent AI tools that support rigorous, copyright-compliant research.