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Witkop EM, Van Wassenbergh S, Heideman PD, Sanderson SL. Biomimetic models of fish gill rakers as lateral displacement arrays for particle separation. BIOINSPIRATION & BIOMIMETICS 2023; 18:056009. [PMID: 37487501 DOI: 10.1088/1748-3190/acea0e] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 07/24/2023] [Indexed: 07/26/2023]
Abstract
Ram suspension-feeding fish, such as herring, use gill rakers to separate small food particles from large water volumes while swimming forward with an open mouth. The fish gill raker function was tested using 3D-printed conical models and computational fluid dynamics simulations over a range of slot aspect ratios. Our hypothesis predicting the exit of particles based on mass flow rates, dividing streamlines (i.e. stagnation streamlines) at the slots between gill rakers, and particle size was supported by the results of experiments with physical models in a recirculating flume. Particle movement in suspension-feeding fish gill raker models was consistent with the physical principles of lateral displacement arrays ('bump arrays') for microfluidic and mesofluidic separation of particles by size. Although the particles were smaller than the slots between the rakers, the particles skipped over the vortical region that was generated downstream from each raker. The particles 'bumped' on anterior raker surfaces during posterior transport. Experiments in a recirculating flume demonstrate that the shortest distance between the dividing streamline and the raker surface preceding the slot predicts the maximum radius of a particle that will exit the model by passing through the slot. This theoretical maximum radius is analogous to the critical separation radius identified with reference to the stagnation streamlines in microfluidic and mesofluidic devices that use deterministic lateral displacement and sieve-based lateral displacement. These conclusions provide new perspectives and metrics for analyzing cross-flow and cross-step filtration in fish with applications to filtration engineering.
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Affiliation(s)
- Erin M Witkop
- Department of Biology, William and Mary, 540 Landrum Dr, Williamsburg, VA 23185, United States of America
| | - Sam Van Wassenbergh
- Departement Biologie, Universiteit Antwerpen, Universiteitsplein 1, B-2610 Antwerpen, Belgium
| | - Paul D Heideman
- Department of Biology, William and Mary, 540 Landrum Dr, Williamsburg, VA 23185, United States of America
| | - S Laurie Sanderson
- Department of Biology, William and Mary, 540 Landrum Dr, Williamsburg, VA 23185, United States of America
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Luo Y, Zhang Y, Liu T, Yu A, Wu Y, Ozcan A. Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning. ACS Sens 2022; 7:3885-3894. [PMID: 36414385 DOI: 10.1021/acssensors.2c01890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Exposure to bio-aerosols such as pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various types of pollen. To address this need, we present a mobile and cost-effective label-free sensor that takes holographic images of flowing particulate matter (PM) concentrated by a virtual impactor, which selectively slows down and guides particles larger than 6 μm to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a complementary metal-oxide semiconductor image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse, and triplicate holograms of the same particle are recorded at a single frame before it exits the imaging field-of-view, revealing different perspectives of each particle. The particles within the virtual impactor are localized through a differential detection scheme, and a deep neural network classifies the pollen type in a label-free manner based on the acquired holographic images. We demonstrated the success of this mobile pollen detector with a virtual impactor using different types of pollen (i.e., bermuda, elm, oak, pine, sycamore, and wheat) and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ∼700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods since it is based on a cartridge-free virtual impactor that does not capture or immobilize PM.
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Affiliation(s)
- Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.,Bioengineering Department, University of California, Los Angeles, California 90095, United States.,California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.,Bioengineering Department, University of California, Los Angeles, California 90095, United States.,California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.,Bioengineering Department, University of California, Los Angeles, California 90095, United States.,California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
| | - Alan Yu
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.,Computer Science Department, University of California, Los Angeles, California 90095, United States
| | - Yichen Wu
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.,Bioengineering Department, University of California, Los Angeles, California 90095, United States.,California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.,Bioengineering Department, University of California, Los Angeles, California 90095, United States.,California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
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Ström OE, Beech JP, Tegenfeldt JO. High-Throughput Separation of Long DNA in Deterministic Lateral Displacement Arrays. MICROMACHINES 2022; 13:1754. [PMID: 36296107 PMCID: PMC9611613 DOI: 10.3390/mi13101754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/06/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
Length-based separation of DNA remains as relevant today as when gel electrophoresis was introduced almost 100 years ago. While new, long-read genomics technologies have revolutionised accessibility to powerful genomic data, the preparation of samples has not proceeded at the same pace, with sample preparation often constituting a considerable bottleneck, both in time and difficulty. Microfluidics holds great potential for automated, sample-to-answer analysis via the integration of preparatory and analytical steps, but for this to be fully realised, more versatile, powerful and integrable unit operations, such as separation, are essential. We demonstrate the displacement and separation of DNA with a throughput that is one to five orders of magnitude greater than other microfluidic techniques. Using a device with a small footprint (23 mm × 0.5 mm), and with feature sizes in the micrometre range, it is considerably easier to fabricate than parallelized nano-array-based approaches. We show the separation of 48.5 kbp and 166 kbp DNA strands achieving a significantly improved throughput of 760 ng/h, compared to previous work and the separation of low concentrations of 48.5 kbp DNA molecules from a massive background of sub 10 kbp fragments. We show that the extension of DNA molecules at high flow velocities, generally believed to make the length-based separation of long DNA difficult, does not place the ultimate limitation on our method. Instead, we explore the effects of polymer rotations and intermolecular interactions at extremely high DNA concentrations and postulate that these may have both negative and positive influences on the separation depending on the detailed experimental conditions.
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Tang H, Niu J, Pan X, Jin H, Lin S, Cui D. Topology Optimization Based Deterministic Lateral Displacement Array Design for Cell Separation. J Chromatogr A 2022; 1679:463384. [DOI: 10.1016/j.chroma.2022.463384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 10/16/2022]
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Tang H, Niu J, Jin H, Lin S, Cui D. Geometric structure design of passive label-free microfluidic systems for biological micro-object separation. MICROSYSTEMS & NANOENGINEERING 2022; 8:62. [PMID: 35685963 PMCID: PMC9170746 DOI: 10.1038/s41378-022-00386-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/27/2022] [Accepted: 03/18/2022] [Indexed: 05/05/2023]
Abstract
Passive and label-free microfluidic devices have no complex external accessories or detection-interfering label particles. These devices are now widely used in medical and bioresearch applications, including cell focusing and cell separation. Geometric structure plays the most essential role when designing a passive and label-free microfluidic chip. An exquisitely designed geometric structure can change particle trajectories and improve chip performance. However, the geometric design principles of passive and label-free microfluidics have not been comprehensively acknowledged. Here, we review the geometric innovations of several microfluidic schemes, including deterministic lateral displacement (DLD), inertial microfluidics (IMF), and viscoelastic microfluidics (VEM), and summarize the most creative innovations and design principles of passive and label-free microfluidics. We aim to provide a guideline for researchers who have an interest in geometric innovations of passive label-free microfluidics.
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Affiliation(s)
- Hao Tang
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240 China
| | - Jiaqi Niu
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240 China
| | - Han Jin
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240 China
- National Engineering Research Center for Nanotechnology, Shanghai Jiao Tong University, 28 Jiangchuan Easternroad, Shanghai, 200241 China
| | - Shujing Lin
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240 China
- National Engineering Research Center for Nanotechnology, Shanghai Jiao Tong University, 28 Jiangchuan Easternroad, Shanghai, 200241 China
| | - Daxiang Cui
- Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai, 200240 China
- National Engineering Research Center for Nanotechnology, Shanghai Jiao Tong University, 28 Jiangchuan Easternroad, Shanghai, 200241 China
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