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Updated: Apr 21, 2026

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Published on: April 9, 2021
Open-source, high-throughput ultrasound treatment chamber
Researchers developed an open-source, 3D-printed device for reproducible cell sonication. This low-cost, easy-to-fabricate tool enhances biomedical ultrasound research by enabling parallel sample processing with high acoustic output consistency.
Area of Science:
- Biomedical Engineering
- Cellular Biology
- Acoustics
Background:
- Biomedical ultrasound research demands expertise in both physics and cell biology, posing challenges in knowledge translation.
- Variability in ultrasonic equipment across labs complicates experimental replication and increases costs.
- Existing methods for cell sonication often lack reproducibility and accessibility.
Purpose of the Study:
- To design and fabricate an open-source, low-cost, and reproducible device for parallel sample sonication in biomedical research.
- To provide a template for consistent and accessible cell sonication experiments.
- To address the limitations of current ultrasonic equipment in research settings.
Main Methods:
- Utilized three-dimensional (3D) printing and affordable consumables for device fabrication.
- Developed a device compatible with standard 24-well plates and OptiCell™.
- Implemented electrical impedance matching with transmission line transformers to enhance transducer pressure output.
- Performed complete acoustic calibration to ensure high reproducibility.
Main Results:
- Achieved a 3.15-fold increase in pressure output through electrical impedance matching.
- Fabricated devices cost approximately €220 in consumables and take ~8 working hours.
- Demonstrated high reproducibility with <1% variance in mean acoustic output between devices.
- Successfully sonicated 24-well plates and OptiCell™ samples.
Conclusions:
- The developed open-source device offers a cost-effective and reproducible solution for cell sonication.
- The design facilitates parallel sample processing, saving time and resources.
- Complete documentation (3D files, software) enables widespread adoption in research labs without extensive prior expertise.
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