G
Gert Tinggaard Svendsen
Researcher at Aarhus University
Publications - 6
Citations - 50
Gert Tinggaard Svendsen is an academic researcher from Aarhus University. The author has contributed to research in topics: Chemistry & Engineering. The author has an hindex of 4, co-authored 6 publications receiving 50 citations. Previous affiliations of Gert Tinggaard Svendsen include Guangzhou University.
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Journal ArticleDOI
Enhanced energy harvesting performance of triboelectric nanogenerator via efficient dielectric modulation dominated by interfacial interaction
TL;DR: In this paper , two kinds of typical two-dimensional transition metal carbide and carbonitride (Ti3C2Tx) MXene were introduced into poly(vinylidene difluoride) (PVDF) ferroelectric polymers to achieve efficient dielectric modulation.
Journal ArticleDOI
Enhanced Energy Harvesting Performance of Triboelectric Nanogenerator via Efficient Dielectric Modulation Dominated by Interfacial Interaction
Dr Sachin Napate,Zhongqian Song,Gert Tinggaard Svendsen,Weiyan Li,Huijun Kong,Huijun Kong,Yu Bao,Ning Wang,Wei Wang,Ma Yingming,He Ying,Shiyu Gan,Li Niu +12 more
TL;DR: In this article, two kinds of typical two-dimensional transition metal carbide and carbonitride (Ti3C2Tx) MXene were introduced into poly(vinylidene difluoride) (PVDF) ferroelectric polymers to achieve efficient dielectric modulation.
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Single-Molecule Nanocatalysis Reveals the Kinetics of the Synergistic Effect Based on Single-AuAg Bimetal Nanocatalysts
TL;DR: In this article , the authors used single-molecule fluorescence microscopy (SMFM) to reveal the mechanism of the synergy of the Au and Ag bimetal catalyst, showing that the incorporation of Au changes the reaction pathway of Amplex Red and H2O2 from a noncompetitive to a competitive reaction mechanism.
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Conductive metal organic framework for ion-selective membrane-free solid-contact potentiometric Cu2+ sensing
TL;DR: In this paper , an ISM-free solid contact ion-selective electrodes (SC-ISEs) based on conductive metal organic framework (MOF) is presented.
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Hallucinating uncertain motion and future for static image action recognition
TL;DR: In this paper , a multi-modal motion feature generator (MMG) and a multidomain future feature generator were proposed to hallucinate multiple plausible motion features and future visual features for a static image, which could significantly facilitate the image action recognition task.