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

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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

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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) -> columns A + B - indentation depth (x) and normal force (y) -> columns C + D
  • dataset 2: 200,000 ppm - indentation depth (x) and Hertz fit (y) -> columns E + F - indentation depth (x) and normal force (y) -> columns G + H
• row 4 and beyond: data

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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

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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)

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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) -> columns A + B - indentation depth (x) and Hertz fit (y) -> columns C + D
  • dataset 2: 200,000 ppm - indentation depth (x) and normal force (y) -> columns F + G - indentation depth (x) and Hertz fit (y) -> columns H + I
• row 4: sample identifier (internal use)
• row 5 and beyond: data

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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) -> columns B + C + D
    • dataset 2: Sample 2 - sliding distance (x) and friction coefficient average (y) and friction coefficient standard deviation (y) -> columns B + E + F
  • dataset 3: Sample 3 - sliding distance (x) and friction coefficient average (y) and friction coefficient standard deviation (y) -> 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) -> columns B + C + D
    • dataset 2: Sample 2 - sliding distance (x) and friction coefficient average (y) and friction coefficient standard deviation (y) -> columns B + E + F
  • dataset 3: Sample 3 - sliding distance (x) and friction coefficient average (y) and friction coefficient standard deviation (y) -> columns B + G + H
• row 5 and beyond: data

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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

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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) -> columns A + B
  • dataset 2: 100 ppm and v = 500 um/s - x position (x) and friction force (y) -> 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) -> columns A + B
  • dataset 2: 200,000 ppm and v = 500 um/s - x position (x) and friction force (y) -> columns C + D
• row 4: sample identifier (internal use) for each sample
• row 5 and beyond: data

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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) -> columns A + B
  • dataset 2: 100 ppm and v = 10 um/s - indentation depth (x) and normal force (y) -> 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) -> columns A + B
  • dataset 2: 200,000 ppm and v = 10 um/s - indentation depth (x) and normal force (y) -> columns C + D
• row 5 and beyond: data

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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) -> columns A + B
  • dataset 2: Sample 2 - 100 ppm - indentation depth (x) and normal force (y) -> columns C + D
  • dataset 3: Sample 3 - 100 ppm - indentation depth (x) and normal force (y) -> columns E + F
  • dataset 4: Sample 1 - 200,000 ppm - indentation depth (x) and normal force (y) -> columns G + H
  • dataset 5: Sample 2 - 200,000 ppm - indentation depth (x) and normal force (y) -> columns I + J
  • dataset 6: Sample 3 - 200,000 ppm - indentation depth (x) and normal force (y) -> columns K + L
• row 4 and beyond: data

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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

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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) -> columns A + B
  • dataset 2: Sample 2 - 100 ppm - F (x) and F' (y) -> columns C + D
  • dataset 3: Sample 3 - 100 ppm - F (x) and F' (y) -> 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) -> columns A + B
  • dataset 2: Sample 2 - 200,000 ppm - F (x) and F' (y) -> columns C + D
  • dataset 3: Sample 3 - 200,000 ppm - F (x) and F' (y) -> columns E + F
• row 4 and beyond: data

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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) -> columns A + B
  • dataset 2: 200,000 ppm - reaction time (x) and root mean square error of fit (y) -> columns C + D
• row 4 and beyond: data

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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

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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

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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) -> columns A + B
  • dataset 2: O2 concentration (x) and surface layer thickness (y) -> columns A + C
  • dataset 3: O2 concentration (x) and surface layer gradient (y) -> columns A + D
  • dataset 4: O2 concentration (x) and liquid layer thickness (y) -> columns A + E
  • dataset 5: O2 concentration (x) and bulk monomer conversion (y) -> columns A + F
  • dataset 6: O2 concentration (x) and root mean square error of fit (y) -> columns A + G

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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

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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

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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)

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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