DOI: 10.5061/dryad.sbcc2frcv
title: Data from: Designing superlubricious hydrogels from spontaneous peroxidation gradients

description: Hydrogels are hydrated three-dimensional networks of hydrophilic polymers
 that are commonly used in the biomedical industry due to their mechanical
 and structural tunability, biocompatibility, and similar water content to
 biological tissues. The surface structure of hydrogels polymerized through
 free-radical polymerization can be modified by controlling environmental
 oxygen concentrations, leading to the formation of a polymer concentration
 gradient. In this work, 17.5 wt % polyacrylamide hydrogels are polymerized
 in low (0.01 mol % O2) and high (20 mol % O2) oxygen environments, and
 their mechanical and tribological properties are characterized through
 microindentation, nanoindentation, and tribological sliding experiments.
 Without significantly reducing the elastic modulus of the hydrogel (E* ≈
 200 kPa), we demonstrate an order of magnitude reduction in friction
 coefficient (from μ = 0.021 ± 0.006 to μ = 0.002 ± 0.001) by adjusting
 polymerization conditions (e.g., oxygen concentration). A quantitative
 analytical model based on polyacrylamide chemistry and kinetics was
 developed to estimate the thickness and structure of the monomer
 conversion gradient, termed the “surface gel layer”. We find that
 polymerizing hydrogels at high oxygen concentrations leads to the
 formation of a preswollen surface gel layer that is approximately five
 times thicker (t ≈ 50 μm) and four times less concentrated (≈ 6% monomer
 conversion) at the surface prior to swelling compared to low oxygen
 environments (t ≈ 10 μm, ≈ 20% monomer conversion). Our model could be
 readily modified to predict the preswollen concentration profile of the
 polyacrylamide gel surface layer for any reaction conditions—monomer and
 initiator concentration, oxygen concentration, reaction time, and reaction
 media depth—or used to select conditions that correspond to a certain
 desired surface gel layer profile.
description: Microindentations and friction measurements were conducted using
 a custom-built linear reciprocating microtribometer. 
 Nanoindentations were conducted using an atomic force microscope
 (Asylum MFP-3D Bio). Data was analyzed using custom
 MATLAB codes.
description: All files are in open-source formats (.csv, .txt).  
description: Data from peer-reviewed article: Title: Designing Superlubricious
 Hydrogels from Spontaneous Peroxidation Gradients Article: ACS Applied
 Materials and Interfaces Authors: Allison L. Chau\*, Chelsea E. R.
 Edwards\*, Matthew E. Helgeson, Angela A. Pitenis \*co-first author
 Corresponding author: Angela Pitenis, apitenis@ucsb.edu *** File List A)
 Fig03b.csv B) Fig03c.csv C) Fig03d.csv D) Fig04a.csv E) Fig04b.csv F)
 Fig05a.csv G) Fig05b\_FigS14a.csv H) Fig05c\_FigS14b.csv I)
 Fig06a\_FigS11.csv J) Fig06b\_FigS12.csv K) FigS01.csv L) FigS04.csv M)
 FigS05a.csv N) FigS05b.csv O) FigS06.csv P) FigS07a.csv Q) FigS07b.csv R)
 FigS08a.csv S) FigS08b.csv T) FigS09.csv U) FigS10a.csv V) FigS10b.csv W)
 FigS13.csv X) FigS15.csv Y) FigS16.csv Z) FigS17.csv AA)
 TableS07\_E\_mu\_lit\_value\_compilation AB)
 Friction\_Data\_Compilation.csv AC) Modulus\_Data\_Compilation.csv FIGURE
 3: microtribometer --- A) Fig03b.csv: representative microtribometer
 indentation curves * row 1: x and y axis, corresponding to indentation
 depth (x) and normal force (y) * row 2: units for corresponding axes * row
 3: casting conditions (100 ppm = 0.01% O2 or 200,000 ppm = 20% O2) *
 dataset 1: 100 ppm - columns A + B * dataset 2: 200,000 ppm - columns D +
 E * row 4: sample identifier (internal use) * row 5 and beyond: data B)
 Fig03c.csv: representative friction force loops * row 1: x and y axis,
 corresponding to x position (x) and friction force (y) * row 2: units for
 corresponding axes * row 3: casting conditions (100 ppm = 0.01% O2 or
 200,000 ppm = 20% O2) * dataset 1: 100 ppm - columns A + B * dataset 2:
 200,000 ppm - columns C + D * row 4: sample identifier (internal use) *
 row 5 and beyond: data C) Fig03d.csv: representative friction force loops
 (same data as columns C and D in Fig03c.csv) * row 1: x and y axis,
 corresponding to x position (x) and friction force (y) * row 2: units for
 corresponding axes * row 3: casting conditions (200,000 ppm = 20% O2) *
 dataset 1: 200,000 ppm - columns A + B * row 4: sample identifier
 (internal use) * row 5 and beyond: data *** FIGURE 4: AFM --- D)
 Fig04a.csv: representative AFM indentation data with Hertz fits * row 1: x
 and y axis, corresponding to indentation depth (x) and normal force (y)
 and Hertz fit (y) * row 2: units for corresponding axes * row 3: sample
 identifier (internal use) and casting conditions (100 ppm = 0.01% O2 or
 200,000 ppm = 20% O2) * dataset 1: 100 ppm - columns A + B + C * dataset
 2: 200,000 ppm - columns D + E + F * row 4 and beyond: data E) Fig04b.csv:
 full representative AFM indentation data with Hertz fits * row 1: x and y
 axis, corresponding to indentation depth (x) and Hertz fit (y) and
 indentation depth (x) and normal force (y) * row 2: units for
 corresponding axes * row 3: sample identifier (internal use) and casting
 conditions (100 ppm = 0.01% O2 or 200,000 ppm = 20% O2) * dataset 1: 100
 ppm - indentation depth (x) and Hertz fit (y) -&gt; columns A + B \-
 indentation depth \(x\) and normal force \(y\) \-\&gt; columns C \+ D
 * dataset 2: 200,000 ppm - indentation depth (x) and Hertz fit (y)
 -&gt; columns E + F \- indentation depth \(x\) and normal force \(y\)
 \-\&gt; columns G \+ H * row 4 and beyond: data *** FIGURE 5 and
 FIGURE S14: data produced with slmcurvefit\_dotm MATLAB code --- F)
 Fig05a.csv: kinetic model - gel thickness vs. reaction time data * row 1:
 x and y axis, corresponding to reaction time (x) and gel thickness (y) *
 row 2: units for corresponding axes * row 3: casting conditions (100 ppm =
 0.01% O2 or 200,000 ppm = 20% O2) * dataset 1: 0.01% O2 - columns A + B *
 dataset 2: 20% O2 - columns C + D * row 4 and beyond: data G)
 Fig05b\_FigS14a.csv: kinetic model - surface gel layer thickness vs.
 reaction time data * row 1: x and y axis, corresponding to reaction time
 (x) and surface gel layer thickness (y) * row 2: units for corresponding
 axes * row 3: casting conditions (100 ppm = 0.01% O2 or 200,000 ppm = 20%
 O2) * dataset 1: 0.01% O2 - columns A + B * dataset 2: 20% O2 - columns C
 + D * row 4 and beyond: data H) Fig05c\_FigS14b.csv: kinetic model -
 surface gel layer gradient vs. reaction time data * row 1: x and y axis,
 corresponding to reaction time (x) and surface gel layer gradient (y) *
 row 2: units for corresponding axes * row 3: casting conditions (100 ppm =
 0.01% O2 or 200,000 ppm = 20% O2) * dataset 1: 0.01% O2 - columns A + B *
 dataset 2: 20% O2 - columns C + D * row 4 and beyond: data *** FIGURE 6:
 data produced with rxndiff\_Pitenis\_015wt100ppm and
 rxndiff\_Pitenis\_015wt200000ppm MATLAB codes --- I) Fig06a\_FigS11.csv:
 monomer conversion vs. depth into gel for 100 ppm O2 * row 1: x and y
 axis, corresponding to depth into gel (x) and monomer conversion (y) * row
 2: units for corresponding axes * row 3: polymerization time * dataset 1:
 3 min - columns A + B * dataset 2: 6 min - columns C + D * dataset 3: 9
 min - columns E + F * dataset 4: 12 min - columns G + H * dataset 5: 15
 min - columns I + J * row 4: casting conditions (100 ppm = 0.01% O2) * row
 5 and beyond: data * Fig06a: columns A - J * FigS11: columns A - J and
 columns L - U (line segment fits for each dataset) J) Fig06b\_FigS12.csv:
 monomer conversion vs. depth into gel for 200,000 ppm O2 * row 1: x and y
 axis, corresponding to depth into gel (x) and monomer conversion (y) * row
 2: units for corresponding axes * row 3: polymerization time * dataset 1:
 3 min - columns A + B * dataset 2: 6 min - columns C + D * dataset 3: 9
 min - columns E + F * dataset 4: 12 min - columns G + H * dataset 5: 15
 min - columns I + J * row 4: casting conditions (200,000 ppm = 20% O2) *
 row 5 and beyond: data * Fig06b: columns A - J * FigS12: columns A - J and
 columns L - U (line segment fits for each dataset) *** FIGURE S1:
 representative microtribometer indentations with Hertz fits --- K)
 FigS01.csv * row 1: x and y axis, corresponding to indentation depth (x)
 and normal force (y) and indentation depth (x) and Hertz fits (y) * row 2:
 units for corresponding axes * row 3: casting conditions (100 ppm = 0.01%
 O2 or 200,000 ppm = 20% O2) * dataset 1: 100 ppm - indentation depth (x)
 and normal force (y) -&gt; columns A + B \- indentation depth \(x\)
 and Hertz fit \(y\) \-\&gt; columns C \+ D * dataset 2: 200,000 ppm -
 indentation depth (x) and normal force (y) -&gt; columns F + G \-
 indentation depth \(x\) and Hertz fit \(y\) \-\&gt; columns H \+ I *
 row 4: sample identifier (internal use) * row 5 and beyond: data ***
 FIGURE S4: bar chart of literature friction coefficient values --- L)
 FigS04.csv: all friction data was taken at v = 500 um/s with a contact
 pressure \~ 6 -11 kPaData taken from the following papers- J.M. Uruena et
 al, Biotribology, 1, 2015- Y. Gombert et al, Advanced Materials and
 Interfaces, 1901320, 2019- Y.A. Meier et al, Langmuir, 35, 2019- S.
 Bonyadi et al, Soft Matter, 15, 2019- R. Simic and N. Spencer, Tribology
 Letters, 69, 2021- R. Simic et al, Soft Matter, 17, 2021- R. Simic et al,
 Tribology Letters, 68, 2020 --- FIGURE S5: friction coefficient vs.
 sliding distance --- M) FigS05a.csv: friction coefficient vs. sliding
 distance for 100 ppm O2 * row 1: x and y axis, corresponding to cycle
 number (x), sliding distance (x), friction coefficient average (y), and
 friction coefficient standard deviation (y) * row 2: units for
 corresponding axes * row 3: casting conditions (100 ppm = 0.01% O2) * row
 4: sample identifier (internal use) for each sample * dataset 1: Sample 1
 - sliding distance (x) and friction coefficient average (y) and friction
 coefficient standard deviation (y) -&gt; columns B + C + D * dataset
 2: Sample 2 - sliding distance (x) and friction coefficient average (y)
 and friction coefficient standard deviation (y) -&gt; columns B + E +
 F * dataset 3: Sample 3 - sliding distance (x) and friction coefficient
 average (y) and friction coefficient standard deviation (y) -&gt;
 columns B + G + H * row 5 and beyond: data N) FigS05b.csv: friction
 coefficient vs. sliding distance for 200,000 ppm O2 * row 1: x and y axis,
 corresponding to cycle number (x), sliding distance (x), friction
 coefficient average (y), and friction coefficient standard deviation (y) *
 row 2: units for corresponding axes * row 3: casting conditions (200,000
 ppm = 20% O2) * row 4: sample identifier (internal use) for each sample *
 dataset 1: Sample 1 - sliding distance (x) and friction coefficient
 average (y) and friction coefficient standard deviation (y) -&gt;
 columns B + C + D * dataset 2: Sample 2 - sliding distance (x) and
 friction coefficient average (y) and friction coefficient standard
 deviation (y) -&gt; columns B + E + F * dataset 3: Sample 3 - sliding
 distance (x) and friction coefficient average (y) and friction coefficient
 standard deviation (y) -&gt; columns B + G + H * row 5 and beyond:
 data *** FIGURE S6: friction coefficient vs. normal force --- O)
 FigS06.csv * Table 1: raw data (A2 - A28, L2 - L28) * Column A: sample
 identifer (internal use) * Column B: casting conditions (100 ppm = 0.01%
 O2 or 200,000 ppm = 20% O2) * Column C: sample identifier (internal use) *
 Column D + E: normal force average and standard deviation * Column F + G:
 friction force average and standard deviation * Column H + I: kinetic
 friction coefficient average and standard deviation * Column J: cycle #
 where analysis begins (internal use) * Column K: number of cycles analyzed
 * Column L: range of x position analyzed on the friction force loop
 (internal use) * Table 2: averaged data for normal force = 1 mN (N2 - N10,
 S2 - S10) * Column N: casting conditions (100 ppm = 0.01% O2 or 200,000
 ppm = 20% O2) * Column O: sample identifier (internal use) * Column P + Q:
 kinetic friction coefficient sample average and standard deviation *
 Column R + S: kinetic friction coefficient casting condition average and
 standard deviation * R5 + S5: kinetic friction coefficient average and
 standard deviation for 100 ppm O2 samples * R8 + S8: kinetic friction
 coefficient average and standard deviation for 200,000 ppm O2 samples *
 Table 3: averaged data for all normal forces (1, 2, 3, 4 mN) (A31 - A41,
 E31 - E41) * Column A: casting conditions (100 ppm = 0.01% O2 or 200,000
 ppm = 20% O2) * Column B + C: normal force average and standard deviation
 * Column D + E: kinetic friction coefficient average and standard
 deviation *** FIGURE S7: friction force vs. x position (comparing sliding
 velocity) --- P) FigS07a.csv: friction force loop for 100 ppm O2 * row 1:
 x and y axis, corresponding to x position (x) and friction force (y) * row
 2: units for corresponding axes * row 3: casting conditions (100 ppm =
 0.01% O2) and sliding velocity (v = 100 um/s or 500 um/s) * dataset 1: 100
 ppm and v = 100 um/s - x position (x) and friction force (y) -&gt;
 columns A + B * dataset 2: 100 ppm and v = 500 um/s - x position (x) and
 friction force (y) -&gt; columns C + D * row 4: sample identifier
 (internal use) for each sample * row 5 and beyond: data Q) FigS07b.csv:
 friction force loop for 200,000 ppm O2 * row 1: x and y axis,
 corresponding to x position (x) and friction force (y) * row 2: units for
 corresponding axes * row 3: casting conditions (200,000 ppm = 20% O2) and
 sliding velocity (v = 100 um/s or 500 um/s) * dataset 1: 200,000 ppm and v
 = 100 um/s - x position (x) and friction force (y) -&gt; columns A + B
 * dataset 2: 200,000 ppm and v = 500 um/s - x position (x) and friction
 force (y) -&gt; columns C + D * row 4: sample identifier (internal
 use) for each sample * row 5 and beyond: data *** FIGURE S8:
 microtribometer indentation curves (comparing indentation velocity) --- R)
 FigS08a.csv: indentation curves for 100 ppm O2 * row 1: x and y axis,
 corresponding to indentation depth (x) and normal force (y) * row 2: units
 for corresponding axes * row 3: sample identifier (internal use) for each
 sample * row 4: casting conditions (100 ppm = 0.01% O2) and indentation
 velocity (v = 1 um/s or 10 um/s) * dataset 1: 100 ppm and v = 1 um/s -
 indentation depth (x) and normal force (y) -&gt; columns A + B *
 dataset 2: 100 ppm and v = 10 um/s - indentation depth (x) and normal
 force (y) -&gt; columns C + D * row 5 and beyond: data S) FigS08b.csv:
 indentation curves for 200,000 ppm O2 * row 1: x and y axis, corresponding
 to indentation depth (x) and normal force (y) * row 2: units for
 corresponding axes * row 3: sample identifier (internal use) for each
 sample * row 4: casting conditions (200,000 ppm = 20% O2) and indentation
 velocity (v = 1 um/s or 10 um/s) * dataset 1: 200,000 ppm and v = 1 um/s -
 indentation depth (x) and normal force (y) -&gt; columns A + B *
 dataset 2: 200,000 ppm and v = 10 um/s - indentation depth (x) and normal
 force (y) -&gt; columns C + D * row 5 and beyond: data *** FIGURE S9:
 AFM nanoindentation curves --- T) FigS09.csv * row 1: x and y axis,
 corresponding to indentation depth (x) and normal force (y) * row 2: units
 for corresponding axes * row 3: sample identifier (internal use) for each
 sample * dataset 1: Sample 1 - 100 ppm - indentation depth (x) and normal
 force (y) -&gt; columns A + B * dataset 2: Sample 2 - 100 ppm -
 indentation depth (x) and normal force (y) -&gt; columns C + D *
 dataset 3: Sample 3 - 100 ppm - indentation depth (x) and normal force (y)
 -&gt; columns E + F * dataset 4: Sample 1 - 200,000 ppm - indentation
 depth (x) and normal force (y) -&gt; columns G + H * dataset 5: Sample
 2 - 200,000 ppm - indentation depth (x) and normal force (y) -&gt;
 columns I + J * dataset 6: Sample 3 - 200,000 ppm - indentation depth (x)
 and normal force (y) -&gt; columns K + L * row 4 and beyond: data ***
 FIGURE S10: F' vs F data for AFM nanoindentations * For more details
 about data analysis, see Garcia et al, Tribology - Materials, Surface
 & Interfaces, 11, 2017 *** U) FigS10a.csv: F' vs F data for
 100 ppm O2 * row 1: x and y axis, corresponding to F (normal force) (x)
 and F' (derivative of normal force) (y) * row 2: units for
 corresponding axes * row 3: sample identifier (internal use) for each
 sample * dataset 1: Sample 1 - 100 ppm - F (x) and F' (y) -&gt;
 columns A + B * dataset 2: Sample 2 - 100 ppm - F (x) and F' (y)
 -&gt; columns C + D * dataset 3: Sample 3 - 100 ppm - F (x) and
 F' (y) -&gt; columns E + F * row 4 and beyond: data V)
 FigS10b.csv: F' vs F data for 200,000 ppm O2 * row 1: x and y axis,
 corresponding to F (normal force) (x) and F' (derivative of normal
 force) (y) * row 2: units for corresponding axes * row 3: sample
 identifier (internal use) for each sample * dataset 1: Sample 1 - 200,000
 ppm - F (x) and F' (y) -&gt; columns A + B * dataset 2: Sample 2
 - 200,000 ppm - F (x) and F' (y) -&gt; columns C + D * dataset 3:
 Sample 3 - 200,000 ppm - F (x) and F' (y) -&gt; columns E + F *
 row 4 and beyond: data *** FIGURE S13: data produced with
 slmcurvefit\_dotm MATLAB codes --- W) FigS13.csv: root mean square error
 of fits vs. reaction time * row 1: x and y axis, corresponding to reaction
 time (x) and root mean square error of fit (y) * row 2: units for
 corresponding axes * row 3: casting conditions (100 ppm = 0.01% O2 or
 200,000 ppm = 20% O2) * dataset 1: 100 ppm - reaction time (x) and root
 mean square error of fit (y) -&gt; columns A + B * dataset 2: 200,000
 ppm - reaction time (x) and root mean square error of fit (y) -&gt;
 columns C + D * row 4 and beyond: data *** FIGURE S15: depth from surface
 of reacting medium vs. monomer conversion --- X) FigS15.csv * row 1: x and
 y axis, corresponding to depth into gel(x) and monomer conversion (y) or
 depth from surface of reacting medium (x) and monomer conversion (y) * row
 2: units for corresponding axes * row 3: polymerization time * depth into
 gel (x) and monomer conversion (y) * dataset 1: 0 min - columns A + B *
 dataset 2: 3 min - columns A + C * dataset 3: 6 min - columns A + D *
 dataset 4: 9 min - columns A + E * dataset 5: 12 min - columns A + F *
 dataset 6: 15 min - columns A + G * depth from surface of reacting medium
 (x) and monomer conversion (y) * dataset 7: 0 min - columns I + J *
 dataset 8: 3 min - columns I + K * dataset 9: 6 min - columns I + L *
 dataset 10: 9 min - columns I + M * dataset 11: 12 min - columns I + N *
 dataset 12: 15 min - columns I + O * row 4: casting conditions (100 ppm =
 0.01% O2 or 200,000 ppm = 20% O2) * row 5 and beyond: data *** FIGURE S16:
 Damkholer study --- Y) FigS16.csv: monomer conversion (comparing oxygen
 concentrations) * row 1: x and y axis, corresponding to depth from surface
 of reacting medium (x) and monomer conversion (y) or depth into gel (x)
 and monomer conversion (y) * row 2: units for corresponding axes * row 3:
 casting conditions (0.004, 0.01, 0.02, 0.05, 0.13, 0.29, 0.68, 1.6, 3.7,
 8.6, 20, 46.5% O2) * depth from surface of reacting medium (x) and monomer
 conversion (y) * dataset 1: 0.004% O2 - columns A + B * dataset 2: 0.01%
 O2 - columns A + C * dataset 3: 0.02% O2 - columns A + D * dataset 4:
 0.05% O2 - columns A + E * dataset 5: 0.13% O2 - columns A + F * dataset
 6: 0.29% O2 - columns A + G * dataset 7: 0.68% O2 - columns A + H *
 dataset 8: 1.6% O2 - columns A + I * dataset 9: 3.7% O2 - columns A + J *
 dataset 10: 8.6% O2 - columns A + K * dataset 11: 20% O2 - columns A + L *
 dataset 12: 46.5% O2 - columns A + M * depth into gel (x) and monomer
 conversion (y) * dataset 1: 0.004% O2 - columns O + P * dataset 2: 0.01%
 O2 - columns O + Q * dataset 3: 0.02% O2 - columns O + R * dataset 4:
 0.05% O2 - columns O + S * dataset 5: 0.13% O2 - columns O + T * dataset
 6: 0.29% O2 - columns O + U * dataset 7: 0.68% O2 - columns O + V *
 dataset 8: 1.6% O2 - columns O + W * dataset 9: 3.7% O2 - columns O + X *
 dataset 10: 8.6% O2 - columns O + Y * dataset 11: 20% O2 - columns O + Z *
 dataset 12: 46.5% O2 - columns O + AA * row 4 and beyond: data *** FIGURE
 S17: Damkholer study --- Z) FigS17.csv: comparison of oxygen
 concentrations * row 1: x and y axis, corresponding to O2 concentration
 (x) and surface monomer conversion (y), surface layer thickness (y),
 surface layer gradient (y), liquid layer thickness (y), bulk monomer
 conversion (y), and root mean square error of fit [RMSE] of fit (y) * row
 2: units for corresponding axes * row 3 and beyond: data * dataset 1: O2
 concentration (x) and surface monomer conversion (y) -&gt; columns A +
 B * dataset 2: O2 concentration (x) and surface layer thickness (y)
 -&gt; columns A + C * dataset 3: O2 concentration (x) and surface
 layer gradient (y) -&gt; columns A + D * dataset 4: O2 concentration
 (x) and liquid layer thickness (y) -&gt; columns A + E * dataset 5: O2
 concentration (x) and bulk monomer conversion (y) -&gt; columns A + F
 * dataset 6: O2 concentration (x) and root mean square error of fit (y)
 -&gt; columns A + G *** TABLE S7: table of literature indentation and
 friction coefficient values --- AA)
 TableS07\_E\_mu\_lit\_value\_compilation * column A: acrylamide (AAm)
 concentration (wt%) * column B: bisacrylamide (MBAm) concentration (wt%) *
 column C: casting conditions (mold material and oxygen concentration in
 mol%) * column D: elastic modulus measured via micro-rheology (kPa) *
 column E: elastic modulus measured via AFM (kPa) * column F: elastic
 modulus measured via microtribometer (kPa) * column G: friction
 coefficient * column H: surface gel layer thickness (um) * column I:
 surface gel layer thickness characterization technique * column J:
 reference number (corresponds to the reference section in the main text) *
 column K: abbreviated reference *** Compilation of raw friction
 coefficient data --- AB) Friction\_Data\_Compilation.csv: raw compiled
 friction coefficient data from hydrogel samples * Table 1: raw data (A2 -
 A28, L2 - L28) * Column A: sample identifer (internal use) * Column B:
 casting conditions (100 ppm = 0.01% O2 or 200,000 ppm = 20% O2) * Column
 C: sample identifier (internal use) * Column D + E: normal force average
 and standard deviation * Column F + G: friction force average and standard
 deviation * Column H + I: kinetic friction coefficient average and
 standard deviation * Column J: cycle # where analysis begins (internal
 use) * Column K: number of cycles analyzed * Column L: range of x position
 analyzed on the friction force loop (internal use) * Table 2: averaged
 data for normal force = 1 mN (N2 - N10, S2 - S10) * Column N: casting
 conditions (100 ppm = 0.01% O2 or 200,000 ppm = 20% O2) * Column O: sample
 identifier (internal use) * Column P + Q: kinetic friction coefficient
 sample average and standard deviation * Column R + S: kinetic friction
 coefficient casting condition average and standard deviation * R5 + S5:
 kinetic friction coefficient average and standard deviation for 100 ppm O2
 samples * R8 + S8: kinetic friction coefficient average and standard
 deviation for 200,000 ppm O2 samples * Table 3: averaged data for all
 normal forces (1, 2, 3, 4 mN) (N12 - N22, R12 - R22) * Column N: casting
 conditions (100 ppm = 0.01% O2 or 200,000 ppm = 20% O2) * Column O + P:
 normal force average and standard deviation * Column Q + R: kinetic
 friction coefficient average and standard deviation *** Compilation of raw
 elastic modulus data --- AC) Modulus\_Data\_Compilation.csv: raw compiled
 modulus data from hydrogel samples * 100 ppm O2 * Table 1: elastic modulus
 for Sample 1 (A2 - A24, J2 - J24) * Column A: casting condition (100 ppm =
 0.01% O2) * Column B: position * Column C: cycle number * Column D:
 maximum force reached during analysis * Column E: maximum contact pressure
 reached during analysis * Column F: maximum contact radius reached during
 analysis * Column G: maximum contact area reached during analysis * Column
 H: estimated reduced elastic modulus using the Hertz model * Column I + J:
 averages and standard deviation of reduced elastic modulus per position *
 Table 2: elastic modulus for Sample 2 (L2 - L24, U2 - U24) * Column L:
 casting condition (100 ppm = 0.01% O2) * Column M: position * Column N:
 cycle number * Column O: maximum force reached during analysis * Column P:
 maximum contact pressure reached during analysis * Column Q: maximum
 contact radius reached during analysis * Column R: maximum contact area
 reached during analysis * Column S: estimated reduced elastic modulus
 using the Hertz model * Column T + U: averages and standard deviation of
 reduced elastic modulus per position * Table 3: elastic modulus for Sample
 3 (W2 - W24, AF2 - AF24) * Column W: casting condition (100 ppm = 0.01%
 O2) * Column X: position * Column Y: cycle number * Column Z: maximum
 force reached during analysis * Column AA: maximum contact pressure
 reached during analysis * Column AB: maximum contact radius reached during
 analysis * Column AC: maximum contact area reached during analysis *
 Column AD: estimated reduced elastic modulus using the Hertz model *
 Column AE + AF: averages and standard deviation of reduced elastic modulus
 per position * AG5 + AH5: average and standard deviation of reduced
 elastic modulus for all the 100 ppm hydrogel samples * 200,000 ppm O2 *
 Table 4: elastic modulus for Sample 1 (A27 - A48, J27 - J48) * Column A:
 casting condition (100 ppm = 0.01% O2) * Column B: position * Column C:
 cycle number * Column D: maximum force reached during analysis * Column E:
 maximum contact pressure reached during analysis * Column F: maximum
 contact radius reached during analysis * Column G: maximum contact area
 reached during analysis * Column H: estimated reduced elastic modulus
 using the Hertz model * Column I + J: averages and standard deviation of
 reduced elastic modulus per position * Table 5: elastic modulus for Sample
 2 (L27 - L48, U27 - U48) * Column L: casting condition (100 ppm = 0.01%
 O2) * Column M: position * Column N: cycle number * Column O: maximum
 force reached during analysis * Column P: maximum contact pressure reached
 during analysis * Column Q: maximum contact radius reached during analysis
 * Column R: maximum contact area reached during analysis * Column S:
 estimated reduced elastic modulus using the Hertz model * Column T + U:
 averages and standard deviation of reduced elastic modulus per position *
 Table 6: elastic modulus for Sample 3 (W27 - W48, AF27 - AF48) * Column W:
 casting condition (100 ppm = 0.01% O2) * Column X: position * Column Y:
 cycle number * Column Z: maximum force reached during analysis * Column
 AA: maximum contact pressure reached during analysis * Column AB: maximum
 contact radius reached during analysis * Column AC: maximum contact area
 reached during analysis * Column AD: estimated reduced elastic modulus
 using the Hertz model * Column AE + AF: averages and standard deviation of
 reduced elastic modulus per position * AG29 + AH29: average and standard
 deviation of reduced elastic modulus for all the 200,0000 ppm hydrogel
 samples * Table 7: average and standard deviation of the contact radius,
 contact area (um^2), contact area (mm^2), and contact pressure (kPa) for
 the 100 ppm O2 hydrogel samples (AI4 - AI6, AM4 - AM6) * Table 8: average
 and standard deviation of the contact radius, contact area (um^2), contact
 area (mm^2), and contact pressure (kPa) for the 200,000 ppm O2 hydrogel
 samples (AI28 - AI30, AM28 - AM30) ***
 ############################################################################################# MATLAB Codes: all codes are under MIT license- please cite the code if a significant portion has been used A) AFM\_Data\_v11\_ibw 1. natsortfiles 2. IBWread 3. AFM\_crop 4. AFM\_approach\_fits 5. AFM\_Sneddon B) AFM\_Data\_v10\_force\_maps C) F\_prime\_vs\_F\_AFM\_v1 D) Tribometer\_Indent\_Analysis\_v5 6. natsortfiles E) F\_prime\_vs\_F\_v6 7. natsortfiles 8. sgolayfilt F) Compiling\_Friction\_Data\_v7 9. natsortfiles G) rxndiff\_Pitenisetal\_015wt100ppm H) rxndiff\_Pitenisetal\_015wt200000ppm I) slmcurvefit ANALYZING AFM NANOINDENTATION DATA --- A) AFM\_Data\_v11\_ibw: opens individual AFM nanoindentation files (.ibw) and fits the data with the Hertz contact mechanics model using the lsqcurvefit functionB) AFM\_Data\_v10\_force\_maps: opens AFM nanoindentation data from force maps (already imported into an Excel file) and fits the data with the Hertz contact mechanics model using the lsqcurvefit functionC) F\_prime\_vs\_F\_AFM\_v1: opens AFM nanoindentation data from an Excel file, bins the data, and takes the first derivative following the analysis demonstrated in Garcia et al, Tribology - Materials, Surface & Interfaces, 11, 2017 --- ANALYZING TRIBOMETER INDENTATION DATA --- D) Tribometer\_Indent\_Analysis\_v5: opens microtribometer indentation data from Excel files and fits the data with the Hertz contact mechanics model using the lsqcurvefit functionE) F\_prime\_vs\_F\_v6: opens microtribometer indentation data from Excel files, smooths the data using a Savitzky-Golay filter (sgolayfilt function), bins the data, and takes the first derivative following the analysis demonstrated in Garcia et al, Tribology - Materials, Surface & Interfaces, 11, 2017 --- ANALYZING TRIBOMETER FRICTION DATA --- F) Compiling\_Friction\_Data\_v7: opens microtribometer friction data from Excel files and calculates the friction coefficient from the specified x-range in the friction force loop over a specified number of cycles --- POLYMERIZATION KINETICS MODELS --- G) rxndiff\_Pitenisetal\_015wt100ppm: reaction-diffusion model for the hydrogels cast at 100 ppm O2H) rxndiff\_Pitenisetal\_015wt200000ppm: reaction-diffusion model for the hydrogels cast at 200,000 ppm O2I) slmcurvefit: uses the MATLAB shape language modeling (SLM) engine (slmengine) to fit the depth into gel vs. monomer conversion data to estimate the surface gel layer thickness and gradient --- FUNCTIONS TO DOWNLOAD --- * Author: Stephen Cobeldick * Download from: https://www.mathworks.com/matlabcentral/fileexchange/47434-natural-order-filename-sort * Citation: Stephen23 (2023). Natural-Order Filename Sort (https://www.mathworks.com/matlabcentral/fileexchange/47434-natural-order-filename-sort), MATLAB Central File Exchange. Retrieved 2021. * Function of codes: reads and sorts through the files to analyze * Functions: A) natsortfiles B) natsort * Notes: natsortfiles calls natsort * Author: Jakub Bialek * Download from: https://www.mathworks.com/matlabcentral/fileexchange/42679-igor-pro-file-format-ibw-to-matlab-variable * Citation: Jakub Bialek (2023). Igor Pro file format (ibw) to matlab variable (https://www.mathworks.com/matlabcentral/fileexchange/42679-igor-pro-file-format-ibw-to-matlab-variable), MATLAB Central File Exchange. Retrieved 2023. * Function of codes: reads atomic force microscope (AFM) ibw files into Matlab * Functions A) IBWread B) readIBWheader C) readIBWbinheader * Notes: IBWread calls readIBWheaders which calls readIBWbinheader