High-Throughput And Label-Free Single Nanoparticle Sizing Based On Time-Resolved On-Chip Microscopy
- 技术优势
- Portable, low-cost, large-field of view technology allowing for high-throughput sample analysisTime-resolved measurements are significantly more reliableLarge dynamic range at various sample concentrations
- 详细技术说明
- UCLA researchers led by Prof. Aydogan Ozcan have developed a novel approach that combines holographic on-chip microscopy with vapor-condensed nanolens self-assembly technology inside a cost-effective hand-held device to size nanoparticles. Using this approach and capturing time-resolved in situ images of the particles results in a significant signal enhancement for the label-free detection in a large sample field-of-view. This high-throughput and label-free nanoparticle sizing platform will make highly advanced nanoscopic measurements readily accessible to researchers and technicians in developing countries, and might especially be valuable for environmental and biomedical applications.
- *Abstract
-
UCLA researchers in the Department of Electrical Engineering have developed a rapid, low-cost, and label-free methodology for nanoparticle sizing.
- *IP Issue Date
- Feb 22, 2018
- *Principal Investigation
-
Name: Tevfik Umut Dincer
Department:
Name: Euan McLeod
Department:
Name: Aydogan Ozcan
Department:
- 申请号码
- 20180052425
- 其他
-
State Of Development
Experimentally demonstrated the sizing of individual nanoparticles as well as viruses, monodisperse samples, and complex polydisperse mixtures, where the sample concentrations can span ∼5 orders-of-magnitude and particle sizes can range from 40 nm to millimeter-scale.
Background
The ability to detect and size nanoparticles is important in the analysis of liquid and aerosol samples for medical, biological, and environmental studies. For example, nanomaterial synthesis, air and water quality monitoring, and virology rely on the ability to size nanoparticles. Various nanoparticle detection and sizing methods are available however, there is a lack of high-throughput devices that can cover a large dynamic range of particle sizes within a portable, cost-effective and rapid technology. Existing methods are typically very accurate and provide a gold standard for particle sizing; though, they are bulky, slow, expensive, may require significant prior knowledge of sample identity, and provide extremely restricted fields of view (FOVs) which limit throughput for particle sizing.
Related Materials
Additional Technologies by these Inventors
- Ultra-Large Field-of-View Fluorescent Imaging Using a Flatbed Scanner
- Detection and Spatial Mapping of Mercury Contamination in Water Samples Using a Smart-Phone
- Automated Semen Analysis Using Holographic Imaging
- Tunable Vapor-Condensed Nano-Lenses
- Single Molecule Imaging and Sizing of DNA on a Cell Phone
- Rapid, Portable And Cost-Effective Yeast Cell Viability And Concentration Analysis Using Lensfree On-Chip Microscopy And Machine Learning
- Wide-Field Imaging Of Birefringent Crystals In Synovial Fluid Using Lens-Free Polarized Microscopy For Crystal Arthropathy Diagnosis
- Quantitative Fluorescence Sensing Through Highly Autofluorescent And Scattering Media Using Cost-Effective Mobile Microscopy
- Demosaiced Pixel Super-Resolution For Multiplexed Holographic Color Imaging
- Microscopic Color Imaging And Calibration
- Pixel Super-Resolution Using Wavelength Scanning
- Fluorescent Imaging Of Single Nano-Particles And Viruses On A Smart-Phone
- Holographic Opto-Fluidic Microscopy
- Lensfree Wide-Field Fluorescent Imaging On A Chip Using Compressive Decoding
- Revolutionizing Micro-Array Technologies: A Microscopy Method and System Incorporating Nanofeatures
- Sparsity-Based Multi-Height Phase Recovery In Holographic Microscopy
- Computational Out-Of-Focus Imaging Increases The Space-Bandwidth Product In Lens-Based Coherent Microscopy
- Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning
- Deep Learning Microscopy
- Mobile Phone Based Fluorescence Multi-Well Plate Reader
- Phase Recovery And Holographic Image Reconstruction Using Neural Networks
- Lensfree Tomographic Imaging
- Air Quality Monitoring Using Mobile Microscopy And Machine Learning
Tech ID/UC Case
27522/2015-476-0
Related Cases
2015-476-0
- 国家/地区
- 美国
