Ultrabright Quasi-Compact Quantum Dot Assembly for Sensitive and Color-Fixed Immunoassays
Abstract Home-based immunoassay platforms constitute critical tools for the rapid screening of infectious respiratory viruses. Notably, viral loads can remain low at certain stages yet vary over several orders of magnitude during infection, and such variations are closely associated with the disease progression and transmission potential. This self-testing demand necessitates diagnostic kits that combine high sensitivity with accurate human-vision discriminability and portable terminal quantification across the entire detectable range. In response, an ultrabright label was fabricated via template-confined, quasi-compact stacking of quantum dots (QDs). This architecture retained high photoluminescence quantum yield with an elevated QD accumulation number, substantially amplifying the immunosignal output and thereby lowering the limit of detection (LOD). Inspired by the highly discernible visualization principle of multi-index universal pH strips, a color-fixed lateral flow immunoassay (c-LFIA) was established by antigen-driven production of composite colors through narrow-emissive dual QD constituent emitters. This strategy enabled sensitive and discriminable chromatic gradients that were assigned unique identification codes to successive antigen-concentration intervals. Accordingly, the c-LFIA could visually categorize SARS-CoV-2 antigen concentration into 9 discrete intervals spanning 0.1–2000 ng mL−1 with an average interpretation accuracy of 90.19%, while portable terminal analysis achieved a quantitative LOD of 47 pg mL−1. Clinical validation further demonstrated an accurate 6-tier viral abundance-associated category stratification by user visual inspection, complemented by companion one-click smartphone reporting of diagnostic information, thus facilitating decentralized detection and scientific response to respiratory pathogen infections.
Authors
- Liang Huang (ORCID: https://orcid.org/0000-0002-4789-7068)
- Lijiao Ao
- Jun Hu (ORCID: https://orcid.org/0000-0002-3178-4130)
- Dinglv Zhuo
- Jing Wang (ORCID: https://orcid.org/0000-0003-3185-1724)
- Yefeng Zhu
- Meng Xu
- Bianlong Wang
- Sai Chu
Institutions
- Zhejiang University of Science and Technology (CN)
- Centre for Social Innovation (UA)
- Hangzhou Center for Disease Control and Prevention (CN)
- Zhejiang University of Technology (CN)
Publication Details
- Journal
- ACS Nano
- Published
- 2026-09-11
- DOI
- https://doi.org/10.1021/acsnano.6c13166
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Zhejiang Province