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Han Dianpeng

Researcher at Academy of Military Science

Publications -  18
Citations -  320

Han Dianpeng is an academic researcher from Academy of Military Science. The author has contributed to research in topics: Quenching (fluorescence) & Aptamer. The author has an hindex of 6, co-authored 18 publications receiving 100 citations.

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A zirconium-porphyrin MOF-based ratiometric fluorescent biosensor for rapid and ultrasensitive detection of chloramphenicol.

TL;DR: A ratiometric fluorescent sensing strategy based on an aptamer labeled with a fluorescent dye and a highly stable zirconium-porphyrin MOF as a fluorescence quencher for the high-efficiency detection of chloramphenicol (CAP).
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A fluorescence aptasensor for the sensitive detection of T-2 toxin based on FRET by adjusting the surface electric potentials of UCNPs and MIL-101.

TL;DR: In this paper, an aptasensor for the sensitive detection of T-2 toxin, which was based on fluorescence resonance energy transfer (FRET), and acted by adjusting the electric potential on the surface of upconversion nanoparticles (UCNPs) and MIL-101(Cr).
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Development of a fast and ultrasensitive black phosphorus-based colorimetric/photothermal dual-readout immunochromatography for determination of norfloxacin in tap water and river water.

TL;DR: A rapid and ultrasensitive method for colourimetric/photothermal dual-readout detection was developed using an 808 nm NIR laser and a thermal imaging app on mobile phone and can achieve the detection of norfloxacin in water samples within 20 min, and the detection limits of colorimetric and photothermal readout can reach 45 pg mL-1.
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Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria.

TL;DR: In this article, a method based on Raman spectroscopy combined with generative adversarial network and multiclass support vector machine was proposed to classify foodborne pathogenic bacteria. But it is still a challenge to overcome the cumbersome culture process of bacteria and the need for a large number of samples, which hinder qualitative analysis, to obtain a high classification accuracy.