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Abstract Low-pressure nonthermal flowing plasmas are widely used for the gas-phase synthesis of nanoparticles and quantum dots of materials that are difficult or impractical to synthesize using other techniques. To date, the impact of temporary electrostatic particle trapping in these plasmas has not been recognized, a process that may be leveraged to control particle properties. Here, we present experimental and computational evidence that, during their growth in the plasma, sub-10 nm silicon particles become temporarily confined in an electrostatic trap in radio-frequency excited plasmas until they grow to a size at which the increasing drag force imparted by the flowing gas entrains the particles, carrying them out of the trap. We demonstrate that this trapping enables the size filtering of the synthesized particles, leading to highly monodisperse particle sizes, as well as the electrostatic focusing of the particles onto the reactor centerline. Understanding of the mechanisms and utilization of such particle trapping will enable the design of plasma processes with improved size control and the ability to grow heterostructured nanoparticles.
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This content will become publicly available on July 1, 2026
An accessible planar charged particle trap for experiential learning in quantum technologies
We describe an inexpensive and accessible instructional setup that explores particle trapping with a planar linear ion trap. The planar trap is constructed using standard printed circuit board manufacturing and is designed to trap macroscopic charged particles in air. Trapping, shuttling, and splitting are demonstrated to students using these particles, which are visible to the naked eye. Students control trap voltages and can compare properties of particle motion with an analytic model of the trap using a computer vision program for particle tracking. Learning outcomes include understanding the design considerations for planar AC traps, mechanisms underpinning particle ejection, the physics of micromotion, and methods of data analysis using standard computer vision libraries.
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- Award ID(s):
- 2308999
- PAR ID:
- 10625111
- Publisher / Repository:
- AIP
- Date Published:
- Journal Name:
- American Journal of Physics
- Volume:
- 93
- Issue:
- 7
- ISSN:
- 0002-9505
- Page Range / eLocation ID:
- 581 to 588
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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