Digitised permanent-thinning data and activation-parameter analysis code for viscosity-modifier polymers

Description

Digitised coordinates, tabulated regression output, and Python analysis scripts supporting the stress-activated re-analysis of permanent viscosity-loss data reported in Marx et al. (2017, Tribology Letters, doi:10.1007/s11249-017-0888-7). No new experimental measurements were generated; only numerical coordinates were extracted from previously published figures using automated colour-segmentation digitisation. Scripts reproduce Table 6, Table 7, and Figure 4(c) of the accompanying manuscript, "Polymeric Viscosity Modifiers in Lubricating Oils: A Mechanism-Led Reassessment and a Proposed Stress-Activated, Relaxation-Time Design Framework," submitted to Tribology International. Files: S1_digitised_data_Marx2017.csv — 34 digitised coordinate pairs (shear stress vs. apparent viscosity-loss rate) recovered from Figures 8, 10 and 11 of the source publication. S2_activation_parameters.csv — regression output (slope, activation parameter, standard error, coefficient of determination) for nine fitted series. S3_individual_fits.py — fits the stress-activated rate equation to each series; reads S1, writes S2. S4_pooling_and_statistics.py — pools results across seven unique formulations, performs slope-comparison t-tests and censoring-sensitivity analysis. S5_relaxation_time_test.py — evaluates a Rouse-based relaxation-time expression and tests its correlation with the extracted activation parameter. README.md — full documentation, provenance of the digitisation method, and known limitations. Requires Python 3.9+ with numpy and scipy. No plotting libraries are needed; all scripts print results to standard output. Run S3 first to regenerate S2 from the raw coordinates, then S4 and S5.

Steps to reproduce

Requires Python 3.9 or later with numpy and scipy installed. No plotting libraries are needed. 1. Place S1_digitised_data_Marx2017.csv, S2_activation_parameters.csv, S3_individual_fits.py, S4_pooling_and_statistics.py, and S5_relaxation_time_test.py in the same directory. 2. Run: python S3_individual_fits.py This reads S1 and regenerates S2 from the raw coordinates, reproducing the individual-fit rows of Table 6 in the manuscript. 3. Run: python S4_pooling_and_statistics.py This pools the nine fits onto seven unique formulations and reproduces Table 7. 4. Run: python S5_relaxation_time_test.py This evaluates the Rouse-based relaxation-time expression and reproduces Figure 4(c). Each script prints its results to standard output. See README.md for full documentation and known limitations.

Institutions

• University of Basrah

  Basra, Basra

Categories

Licence