Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM
Abstract Contactless manipulation of samples, particularly the ability to dynamically handle multiple fragile specimens while maintaining their integrity and viability, is crucial for various applications in biology, medicine, engineering, and physics. While hydrodynamic tweezers have emerged as a p...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-05-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-025-00907-5 |
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| _version_ | 1850190935377313792 |
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| author | Haodong Zhu Wenjun Yu Neil Upreti Tony Jun Huang |
| author_facet | Haodong Zhu Wenjun Yu Neil Upreti Tony Jun Huang |
| author_sort | Haodong Zhu |
| collection | DOAJ |
| description | Abstract Contactless manipulation of samples, particularly the ability to dynamically handle multiple fragile specimens while maintaining their integrity and viability, is crucial for various applications in biology, medicine, engineering, and physics. While hydrodynamic tweezers have emerged as a promising approach for gentle, label-free manipulation of a wide range of sample types and sizes, they typically have limited flexibility in terms of dynamic control, making it challenging to realize high-resolution and programmable manipulation of multiple samples. Here, we introduce the Streaming-based Tweezers for Routing, Engineering, And Manipulation of multiparticles (STREAM) with sub-wavelength resolution. The platform employs an array of piezoelectric plates arranged in a space-reciprocal pattern to generate acoustic streaming, creating localized trapping points. The mechanism of particle trapping and the improvement of routing resolution via multiunit activation were investigated. Subsequently, a convolutional neural network (CNN) with arbitrary voltage combination as the input and predicted trapping position as the output was integrated into the system. The CNN calibration is vital to the system as it enhances the platform’s performance, enabling precise control of the trapping positions beyond traditional physical unit size limitations. We demonstrated the STREAM platform’s capabilities through single particle routing with sub-wavelength precision, simultaneous manipulation of multiple particles, and on-demand assembly of samples. The STREAM platform opens new possibilities for applications requiring precise and dynamic control of particles and samples, with the potential to advance fields including biology, chemistry, and materials science. |
| format | Article |
| id | doaj-art-2ae5d08a3cc7451983c24cdcd6946cf8 |
| institution | OA Journals |
| issn | 2055-7434 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Microsystems & Nanoengineering |
| spelling | doaj-art-2ae5d08a3cc7451983c24cdcd6946cf82025-08-20T02:15:05ZengNature Publishing GroupMicrosystems & Nanoengineering2055-74342025-05-0111111210.1038/s41378-025-00907-5Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAMHaodong Zhu0Wenjun Yu1Neil Upreti2Tony Jun Huang3Department of Mechanical Engineering and Material Science, Duke UniversityDepartment of Mechanical Engineering and Material Science, Duke UniversityDepartment of Biomedical Engineering, Duke UniversityDepartment of Mechanical Engineering and Material Science, Duke UniversityAbstract Contactless manipulation of samples, particularly the ability to dynamically handle multiple fragile specimens while maintaining their integrity and viability, is crucial for various applications in biology, medicine, engineering, and physics. While hydrodynamic tweezers have emerged as a promising approach for gentle, label-free manipulation of a wide range of sample types and sizes, they typically have limited flexibility in terms of dynamic control, making it challenging to realize high-resolution and programmable manipulation of multiple samples. Here, we introduce the Streaming-based Tweezers for Routing, Engineering, And Manipulation of multiparticles (STREAM) with sub-wavelength resolution. The platform employs an array of piezoelectric plates arranged in a space-reciprocal pattern to generate acoustic streaming, creating localized trapping points. The mechanism of particle trapping and the improvement of routing resolution via multiunit activation were investigated. Subsequently, a convolutional neural network (CNN) with arbitrary voltage combination as the input and predicted trapping position as the output was integrated into the system. The CNN calibration is vital to the system as it enhances the platform’s performance, enabling precise control of the trapping positions beyond traditional physical unit size limitations. We demonstrated the STREAM platform’s capabilities through single particle routing with sub-wavelength precision, simultaneous manipulation of multiple particles, and on-demand assembly of samples. The STREAM platform opens new possibilities for applications requiring precise and dynamic control of particles and samples, with the potential to advance fields including biology, chemistry, and materials science.https://doi.org/10.1038/s41378-025-00907-5 |
| spellingShingle | Haodong Zhu Wenjun Yu Neil Upreti Tony Jun Huang Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM Microsystems & Nanoengineering |
| title | Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM |
| title_full | Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM |
| title_fullStr | Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM |
| title_full_unstemmed | Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM |
| title_short | Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM |
| title_sort | streaming based tweezers for routing engineering and manipulation of multiparticles stream |
| url | https://doi.org/10.1038/s41378-025-00907-5 |
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