Data and Code for "Positive Feedback Drives Sharp Swelling of Polymer Brushes near Saturation"

This archive contains the complete dataset and Python analysis scripts required to reproduce the figures in the publication: "Positive Feedback Drives Sharp Swelling of Polymer Brushes near Saturation". The analysis is performed within the environment of the Electronic Laboratory Notebook (ELN) software, which is detailed in the Journal of Open Research Software: https://openresearchsoftware.metajnl.com/articles/10.5334/jors.391 ------------------------------------------------- **Contents:** * **`db.sqlite3`**: The complete experimental database for the Labbook project.* **Jupyter Notebooks (`.ipynb`)**: A collection of notebooks used for data analysis and figure generation.* **`README.md`**: Detailed, step-by-step instructions for setting up the environment and reproducing the figures. ------------------------------------------------- **Quick Start Guide:** 1. Set up the ELN environment as described in the `README.md` file.2. Replace the default database in the new ELN project with the `db.sqlite3` file from this archive.3. Launch Jupyter from the ELN interface and run the notebooks. **For detailed, step-by-step instructions on how to set up the environment and reproduce the figures, please see the `README.md` file included in this archive.** ------------------------------------------------- **Figure Reproduction Guide:** The following list maps each Jupyter Notebook to the figure(s) it generates: * `fit_chi_dgl_smoothen.ipynb` -> `slov_vs_class_chi_grid`* `Eval_degree_high_compare.ipynb` -> `thickness_brush_temps_corr`, `Swelling_Ratio_comp`* `fit_chi_dgl_pincus.ipynb` -> `data_phi_vs_chi`, `phi_vs_chi_fixed_RH`, `data_phi_vs_diff_chi`* `Eval_12_degree_high.ipynb` -> `thickness_brush_12_no_corr`, `thickness_brush_12_corr`* `Var_graft_dens.ipynb` -> `comparison_plot_12C`* `Basic_prp.ipynb` -> `example_high_hum_start_0`, `example_high_tmp`* `Eval_elli.ipynb` -> `example_high_mse`, `example_high_n`* `LMP_EtOH_vs_none.ipynb` -> `Sim_with_fit`* `Adaptive_wetting_theory_least_squares_short_clean_data.ipynb` -> `RSD_CA_CLS_rel_hum_fit`, `delta_gamma_sl`