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Data-driven optimization for controllable multi-scale aperture fabrication of nanopipettes.

Runan Guo¹, Zhi Chen¹, Xue Han^1,2

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.

|May 14, 2026

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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.

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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.