README.md

Charge mosaics on contact-electrified dielectrics result from polarity-inverting discharges

This is a repository of code accompanying the research article

Yaroslav I. Sobolev, Witold Adamkiewicz, Marta Siek, Bartosz A. Grzybowski, "Charge mosaics on contact-electrified dielectrics result from polarity-inverting discharges", Nature Physics (2022).

Experimental data and results of numerical calculations are too large (~16 Gb) for Github and are available from separate repository in Harvard Dataverse:

Installation

This code is compatible with Python 3.7

The primary Python dependencies are:

• Matplotlib version 3.1.0
• Matplotlib-scalebar version 0.6.0
• Numpy version 1.21.6
• Scipy version 1.5.2

Other Python dependencies are standard -- come pre-installed with Anaconda distribution.

Running custom code controlling the experimental setups (Scanning Kelvin probe and setup for controlled delamination, see custom_code_for_experimental_setups folder) additionally requires:

• 32-bit Python 3
• PyAutoGui version 0.9.47
• thorlabs_apt module for Python
• Thorlabs APT drivers (32-bit).

Running the numerical model (see macro-scale-model folder) requires:

• PyAutoGui version 0.9.47
• COMSOL Multiphysics version 5.4 or later, with AC/DC package

Reproducing methods and figures from the paper

For constructing Paschen's law interpolator from literature data (Supplementary Figure S26-S27) see paschen_curve_approximating/paschen_curve_processing.py and paschen_curve_approximating/paschen_curve_processing_for_argon.py

For reproducing Scanning Kelvin Probe (SKP) (Figure 2c,f, Figure 3b, Supplementary Figure 5) see folder data_processing/kelvinprobe_viewer

For plotting sections of SKP maps see data_processing/kelvinprobe_sections folder

For reproducing Supplementary Figures S14-S18 see data_processing/kelvinprobe_viewer/moving_averages.py

For processing electrometer data (Figure 4d, Figure 3b, Supplementary Figures S10d, S11d, S12c, S6d) see scripts named data_processing/process_electrometer*.py

For evaluating net charge from electrometer data (Figure 3b, Supplementary Figure S6d) see data_processing/net_charge_extraction.py

For processing direct optical detection os sparks see (Figure 4b,c), see scripts data_processing/sparks_camera_processing*.py

For detecting motion of delamination front (Supplementary Figures S10b, S11b, S12c) see scripts data_processing/delamination_front_finder*.py and data_processing/delamination_front_plotter*.py

For XPS data plotting (Figure S8, S9) see data_processing/plot-all-XPS.py

For hygrometer calibration (Supplementary Figure S33) see data_processing/hygrometer_calibration

For evaluation of adhesion work (Figure 3c, Supplementary Figures S6c,e) see data_processing/adhesion_work_extraction.py