On-Chip Light-Scattering Enhancement Enabled by a Microlens Array for High-Performance Single-Particle Tracking under Conventional Bright-Field Microscopy.
Pengcheng Zhang, Tingting Zhan, Guoqiang Gu, Changle Li, Xiaotian Tan, Yi Zhang, Hui Yang
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引用次数: 0
Abstract
Scattering-based single-particle tracking (S-SPT) has revolutionized the label-free detection and characterization of nanoscopic objects, offering immense potential for diverse analytical applications. However, the high technical demands placed on optical systems have long impeded its widespread adoption. To address this, an on-chip microlens-based approach that significantly enhances light scattering, thereby reducing the requirements on back-end optical systems, is introduced. Unlike existing field enhancement techniques, which are limited by their highly localized field, this approach leverages enhanced long-range optical fields and complex interactions between nanoparticles and the microlens to achieve an enhancement range ten times greater. This method enables high-performance S-SPT using a conventional bright-field microscope under incoherent light sources with relatively low illumination powers. The approach achieves nanometer localization precision for 60 nm gold nanoparticles in an aqueous medium within a substantial 750 µm2 field of view at a 200 µs exposure time. This advancement will significantly facilitate the practical application of S-SPT in biosensors and related fields, making it more accessible and versatile for a broad range of research and industrial applications.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.