about
Nanotechnology-Enhanced No-Wash Biosensors for in Vitro Diagnostics of Cancer.Size-Dependent Immunochromatographic Assay with Quantum Dot Nanobeads for Sensitive and Quantitative Detection of Ochratoxin A in Corn.Folic Acid Targeting for Efficient Isolation and Detection of Ovarian Cancer CTCs from Human Whole Blood Based on Two-Step Binding Strategy.Ratiometric optical nanoprobes enable accurate molecular detection and imaging.Controllable self-assembled plasmonic vesicle-based three-dimensional SERS platform for picomolar detection of hydrophobic contaminantsAmphiphilic ligand modified gold nanocarriers to amplify lanthanide loading for ultrasensitive DELFIA detection of CronobacterComparison of three sample addition methods in competitive and sandwich colloidal gold immunochromatographic assayFunctional DNA Regulated CRISPR-Cas12a Sensors for Point-of-Care Diagnostics of Non-Nucleic-Acid TargetsGold Nanoflower-Enhanced Dynamic Light Scattering Immunosensor for the Ultrasensitive No-Wash Detection of Escherichia coli O157:H7 in MilkBiotin-Streptavidin System-Mediated Ratiometric Multiplex Immunochromatographic Assay for Simultaneous and Accurate Quantification of Three MycotoxinsMagnetic Quantum Dot Nanobead-Based Fluorescent Immunochromatographic Assay for the Highly Sensitive Detection of Aflatoxin B1 in Dark Soy SauceDramatically Enhanced Immunochromatographic Assay Using Cascade Signal Amplification for Ultrasensitive Detection of Escherichia coli O157:H7 in MilkIntegrated magneto-fluorescence nanobeads for ultrasensitive glycoprotein detection using antibody coupled boronate-affinity recognitionCore-Shell-Heterostructured Magnetic-Plasmonic Nanoassemblies with Highly Retained Magnetic-Plasmonic Activities for Ultrasensitive Bioanalysis in Complex MatrixGold nanorods etching-based plasmonic immunoassay for qualitative and quantitative detection of aflatoxin M1 in milk
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Q39356725-94265464-D8E1-43BE-A280-EB6B561CE4EBQ47209000-BCF1DC77-6DD6-4C66-B07D-73274F4E2644Q52331104-21E12B7B-D8BF-46DB-886E-1445D815B408Q52641358-44507C6C-A20D-4DEB-AA06-CE99210DF8BBQ57139806-19DA7384-36CC-4C55-92FC-046BE646A17BQ91353564-202960A0-05FD-4ABC-BB22-A6E42E31ED89Q91419601-D454E1B5-C8F4-4A27-98A0-170A15B06310Q91683302-5A6DF8FE-EAC4-45F5-85A5-913E42711C9AQ92114793-E391EAA7-8565-4D18-B653-FD6842DF30DFQ92146125-0C1040AA-0161-4F7C-B564-4A1D4020FDD1Q92165847-B4F1F5A3-56FA-4047-94A2-3318FBC03EFFQ92375890-43A898D3-949D-4069-8A42-77096DE8353DQ92519140-6A9EBCD5-7B73-4B1D-80C0-9766A0D162E5Q93015997-2FC4EA30-BB7E-4404-8939-B692C8891AFAQ96113338-6C84C220-1310-476F-9F0B-613FE9A6E10B
P50
description
researcher ORCID ID = 0000-0001-5141-0841
@en
name
Yonghua Xiong
@ast
Yonghua Xiong
@en
Yonghua Xiong
@es
Yonghua Xiong
@nl
type
label
Yonghua Xiong
@ast
Yonghua Xiong
@en
Yonghua Xiong
@es
Yonghua Xiong
@nl
prefLabel
Yonghua Xiong
@ast
Yonghua Xiong
@en
Yonghua Xiong
@es
Yonghua Xiong
@nl
P31
P496
0000-0001-5141-0841