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Data-driven optimization for controllable multi-scale aperture fabrication of nanopipettes
Runan Guo1, Zhi Chen1, Xue Han1,2
1Tianjin International Center for Nanoparticles and Nanosystems, Tianjin University, Tianjin, 300072, P. R. China. E-mail: mayanqing@tju.edu.cn, lei.ma@tju.edu.cn.
The Analyst
|May 14, 2026
Summary
This study introduces a new framework for controllable nanopipette fabrication, combining artificial neural networks (ANN) and physical models. This approach enables precise control over nanopipette aperture size, moving beyond trial-and-error methods.
Area of Science:
- Nanotechnology
- Materials Science
- Engineering
Background:
- Controllable fabrication of nanopipettes is crucial for various scientific applications.
- Traditional methods often rely on empirical trial-and-error, leading to variability and inefficiency.
Purpose of the Study:
- To develop a multi-parameter collaborative optimization framework for controllable nanopipette fabrication.
- To establish a predictable, model-driven paradigm for nanopipette manufacturing.
Main Methods:
- Utilized an artificial neural network (ANN) to map fabrication parameters to nanopipette aperture size.
- Employed feature weight analysis (Random Forest, SHAP, Garson) to rank parameter importance.
- Derived a physical model from mechanical theory to describe aperture evolution.
Main Results:
- Identified 'Heat' as the most influential fabrication parameter, followed by 'Pull', 'Delay', 'Filament', and 'Velocity'.
- Achieved controllable fabrication of nanopipettes with target apertures from 50 nm to 1000 nm in 100 nm increments.
- Demonstrated the framework's ability to transform fabrication into a predictable, model-driven process.
Conclusions:
- The proposed framework successfully enables precise control over nanopipette aperture size.
- This model-driven approach significantly enhances the efficiency and predictability of nanopipette fabrication.
- The findings pave the way for standardized and reliable nanopipette manufacturing.
