Design of soft material surfaces with rationally tuned water diffusivity
Data files
May 22, 2023 version files 3.83 MB
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Figures.zip
3.81 MB
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README.txt
15.96 KB
May 22, 2023 version files 3.84 MB
-
Figures.zip
3.81 MB
-
README.md
16.30 KB
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README.txt
15.96 KB
Abstract
This dataset accompanies the article "Design of Soft Material Surfaces with Rationally Tuned Water Diffusivity" by Audra J. DeStefano, My Nguyen, Glenn H. Fredrickson, Songi Han, and Rachel A. Segalman published in ACS Central Science. This article demonstrates a local water diffusivity gradient within polymer micelles using Overhauser Dynamic Nuclear Polarization spectroscopy. The dataset contains the necessary simulated and experimental data to reproduce all main text and supporting figures. Simulated monomer positions and density profiles, local hydration parameters, electron paramagnetic spectra and corresponding rotational correlation times, matrix assisted laser desorption/ionization spectra, and liquid chromatography traces are included.
https://doi.org/10.25349/D9N327
GENERAL INFORMATION
1. Title of Dataset: Design of Soft Material Surfaces with Rationally Tuned Water Diffusivity
2. Author Information
A. Principal Investigator Contact Information
Name: Rachel A. Segalman
Institution: University of California Santa Barbara
Address: Department of Chemical Engineering, University of California, Santa Barbara, 93106
Email: segalman@ucsb.edu
B. Associate or Co-investigator Contact Information
Name: Audra J. DeStefano
Institution: University of California Santa Barbara
Address: Department of Chemical Engineering, University of California, Santa Barbara, 93106
Email: adestefano@ucsb.edu
C. Associate or Co-investigator Contact Information
Name: My Nguyen
Institution: University of California Santa Barbara
Address: Department of Chemical Engineering, University of California, Santa Barbara, 93106
Email: my@ucsb.edu
D. Associate or Co-investigator Contact Information
Name: Glenn H. Fredrickson
Institution: University of California Santa Barbara
Address: Department of Chemical Engineering, University of California, Santa Barbara, 93106
Email: ghf@ucsb.edu
E. Associate or Co-investigator Contact Information
Name: Songi Han
Institution: University of California Santa Barbara
Address: Department of Chemical Engineering, University of California, Santa Barbara, 93106
Email: songi@chem.ucsb.edu
3. Date of data collection (single date, range, approximate date): 2021-01-01 to 2023-01-30
4. Geographic location of data collection: Santa Barbara, CA
5. Information about funding sources that supported the collection of the data:
This work was supported by the Center for Materials for Water and Energy Systems (M-WET), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award #DE-SC0019272. A.J.D. acknowledges support from the National Defense Science & Engineering Graduate (NDSEG) Fellowship Program. M.N. was supported by BASF Corporation through the California Research Alliance. Support for the ODNP studies was provided by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy─EXC-2033─Project number 390677874. This work makes use of Materials Research Lab (MRL) Shared Experimental Facilities that are supported by the MRSEC Program of the NSF under Award No. DMR 1720256; a member of the NSF-funded Materials Research Facilities Network (www.mrfn.org). Use was made of computational facilities purchased with funds from the National Science Foundation (OAC-1925717) and administered by the Center for Scientific Computing (CSC). The CSC is supported by the California NanoSystems Institute and the Materials Research Science and Engineering Center (MRSEC; NSF DMR 1720256) at UC Santa Barbara.
SHARING/ACCESS INFORMATION
1. Licenses/restrictions placed on the data: N/A
2. Links to publications that cite or use the data: https://doi.org/10.1021/acscentsci.3c00208
3. Links to other publicly accessible locations of the data: N/A
4. Links/relationships to ancillary data sets: N/A
5. Was data derived from another source? no
6. Recommended citation for this dataset: DeStefano, Audra et al. (2023), Design of Soft Material Surfaces with Rationally Tuned Water Diffusivity, Dryad, Dataset,
DATA & FILE OVERVIEW
1. File List: Files are orgranized into folders based on figure number from the corresponding manuscript. Files are named as "Figure#_Technqiue_SampleID.csv".
A. Figure2: Average monomer position obtained by coarse-grained molecular dynamics simulation
a. Figure2_CGMD.csv
1. Variables: monomer position in polymer chain, average distance from micelle core (units=nanometers), variance of distance from micelle core (units=nanometers)
2. Column names: MonomerID, distance_chainavg, distance_var_chainavg
B. Figure3: Local water diffusivity throughout the corona of a micelle obtained by Overhause Dynamic Nuclear Polarization
a. Figure3_ODNP.csv
1. Variables: sample ID, average distance from micelle core (units=nanometers), water volume fraction (units=none), local water diffusivity (units=meters2/second), local water diffusivity standard deviation (units=meters2/second), local water diffusivity as normalized in Figure 3(units=none), normalized local water diffusivity standard deviation (units=none)
2. Column names: Sample ID, distance_chainavg, water_vol_frac, Dlocal, Dlocal_STDEV, Dlocal/Dcore, Dlocal/Dcore_STDEV)
C. Figure4 - Rotational correlations times throughout the corona of a micelle obtained by fitting the lineshape of continuous-wave electron paramagnetic resonance spectra. Raw and fit data spectra are included in Figure S3.
a. Figure4_cwEPR.csv
1. Variables: sample ID, average distance from micelle core (units=nanometers), rotational correlation time (units=nanoseconds), rotational correlation time as normalized in Figure 4 (units=none)
2. Column names: Sample ID, distance_chainavg, RotationalCorrelationTime, NormalizedRotationalCorrelationTime
D. FigureS1 - Sample mass spectra obtained by matrix assisted laser desorption/ionization
a. FigureS1_MALDI_C6.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
b. FigureS1_MALDI_C8.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
c. FigureS1_MALDI_C10.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
d. FigureS1_MALDI_C12.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
e. FigureS1_MALDI_C18.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
f. FigureS1_MALDI_C24.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
g. FigureS1_MALDI_C26.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
c. FigureS1_MALDI_Unlabeled.csv
1. Variables: mass divided by charge number of ion (units=none), signal intensity (units=arbitrary)
2. Column names: m/z, intensity
E. FigureS2 - Liquid chromotography traces for each sample
a. FigureS2_HPLC_C6.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
b. FigureS2_HPLC_C8.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
c. FigureS2_HPLC_C10.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
d. FigureS2_HPLC_C12.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
e. FigureS2_HPLC_C18.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
f. FigureS2_HPLC_C24.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
g. FigureS2_HPLC_C26.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
c. FigureS2_HPLC_Unlabeled.csv
1. Variables: elution time (units=minutes), signal intensity (units=arbitrary)
2. Column names: Time_min, Intensity
F. FigureS3 - Raw and fit continuous-wave electron paramagnetic resonance spectra for each sample
a. FigureS3_cwEPR_C6.csv
1. Variables: data point index, magnetic field (units=gauss), signal intensity (units=arbitrary)
2. Column names: index, Field[G], Intensity[]
b. FigureS3_cwEPR_C8.csv
1. Variables: data point index, magnetic field (units=gauss), signal intensity (units=arbitrary)
2. Column names: index, Field[G], Intensity[]
c. FigureS3_cwEPR_C10.csv
1. Variables: data point index, magnetic field (units=gauss), signal intensity (units=arbitrary)
2. Column names: index, Field[G], Intensity[]
d. FigureS3_cwEPR_C12.csv
1. Variables: data point index, magnetic field (units=gauss), signal intensity (units=arbitrary)
2. Column names: index, Field[G], Intensity[]
e. FigureS3_cwEPR_C18.csv
1. Variables: data point index, magnetic field (units=gauss), signal intensity (units=arbitrary)
2. Column names: index, Field[G], Intensity[]
f. FigureS3_cwEPR_C24.csv
1. Variables: data point index, magnetic field (units=gauss), signal intensity (units=arbitrary)
2. Column names: index, Field[G], Intensity[]
g. FigureS3_cwEPR_C26.csv
1. Variables: data point index, magnetic field (units=gauss), signal intensity (units=arbitrary)
2. Column names: index, Field[G], Intensity[]
h. FigureS3_cwEPRFit_C6.csv
1. Variables: magentic field (units=gauss), raw signal intensity (units=arbitrary), fit signal intensity (units=arbitrary)
2. Column names: Field_G, Data_Intensity, Fit_Intensity
i. FigureS3_cwEPRFit_C8.csv
1. Variables: magentic field (units=gauss), raw signal intensity (units=arbitrary), fit signal intensity (units=arbitrary)
2. Column names: Field_G, Data_Intensity, Fit_Intensity
j. FigureS3_cwEPRFit_C10.csv
1. Variables: magentic field (units=gauss), raw signal intensity (units=arbitrary), fit signal intensity (units=arbitrary)
2. Column names: Field_G, Data_Intensity, Fit_Intensity
k. FigureS3_cwEPRFit_C12.csv
1. Variables: magentic field (units=gauss), raw signal intensity (units=arbitrary), fit signal intensity (units=arbitrary)
2. Column names: Field_G, Data_Intensity, Fit_Intensity
l. FigureS3_cwEPRFit_C18.csv
1. Variables: magentic field (units=gauss), raw signal intensity (units=arbitrary), fit signal intensity (units=arbitrary)
2. Column names: Field_G, Data_Intensity, Fit_Intensity
m. FigureS3_cwEPRFit_C24.csv
1. Variables: magentic field (units=gauss), raw signal intensity (units=arbitrary), fit signal intensity (units=arbitrary)
2. Column names: Field_G, Data_Intensity, Fit_Intensity
n. FigureS3_cwEPRFit_C26.csv
1. Variables: magentic field (units=gauss), raw signal intensity (units=arbitrary), fit signal intensity (units=arbitrary)
2. Column names: Field_G, Data_Intensity, Fit_Intensity
G. FigureS4 - Hydration parameters obtained by Overhauser Dynamic Nuclear Polarization spectroscopy
a. FigureS4_ODNP.csv
1. Variables: sample ID, water proton spin-lattice relaxtion time (units=seconds), coupling factor (units=none)
2. Column names: Sample, T10, CouplingFactor
H. FigureS5 - Denisty profiles of three monomer types obtained by coarse-grained molecular dynamics simulation.
a. FigureS5_CGMD.csv
1. Variables: distance from micelle center (units=nanometers), density of monomer type 1 (units=nanometers-3), density of monomer type 2 (units=nanometers-3), density of monomer type 3 (units=nanometers^-3)
2. Column names: DistanceFromMicelleCenter_nm, density1_nm-3, density2_nm-3, denisty3_nm^-3
2. Relationship between files, if important: N/A
3. Additional related data collected that was not included in the current data package: N/A
4. Are there multiple versions of the dataset? no
METHODOLOGICAL INFORMATION
1. Description of methods used for collection/generation of data: https://pubs.acs.org/doi/pdf/10.1021/acscentsci.3c00208
Coarse-grained molecular dynamics simulations: The micelle simulation consists of 13 polymer chains and 134,325 solvent molecules in a cubic box and is conducted with the OpenMM simulation package (https://openmm.org/). A 2.22 nm cutoff for the non-bonded Gaussian interactions and a time step of dt = 0.02 τ are used. The initial configuration is relaxed for 20 τ and the trajectory is collected for analysis in the last 20,000 τ. The temperature is set to T = 298.15 K using the Langevin thermostat with a relaxation time of 100 dt, while the pressure is set to P using the Monte Carlo isotropic barostat with the update frequency of 25 dt. The average box side length from the production run is about 16 nm. The average distance of each monomer from the micelle core and its variance are included in this dataset (Figure 2) along with a density profile (Figure S5).
Overhauser dynamic nuclear polarization spectroscopy: ODNP experiments utilize the samples prepared for cw-EPR. Sample temperature is maintained at 18°C in a ER4123D dielectric resonator using a stream of compressed air. ODNP is performed at 0.35 T at a 14.8 MHz 1H Larmor frequency and at 9.8 GHz electron spin Larmor frequency using a home-built U-shaped NMR coil. An inversion-recovery pulse sequence acquires proton spin-lattice relaxation times (T1).
Continuous-wave electron paramagnetic resonance spectroscopy: Spin label concentrations and spin label mobilities are measured via cw-EPR on micelle solutions prepared at a concentration of 5 mg/mL in water with spin concentrations of 100-200 μM. A quartz round capillary tube of 0.60 mm inner diameter and 0.84 mm outer diameter is loaded with 3.5 μL of solution and sealed at one end with beeswax and at the other with Critoseal. The dispersive electron paramagnetic resonance (EPR) spectrum is obtained with a fixed frequency (9.8 GHz) at 20 dB, while the magnetic field is swept with a modulation frequency of 140.0 kHz and a modulation amplitude of 0.70 G.
To confirm the presence of the target compounds, polypeptoid samples are characterized with matrix-assisted laser desorption/ionization (MALDI) spectrometry and high-pressure liquid chromatography (HPLC). MALDI is done on a Bruker Microflex LRF MALDI TOF mass spectrometer. Alpha-cyano matrix is prepared in tetrahydrofuran. Matrix-sample mixtures are spotted onto a polished steel MALDI target plate. Mass spectra are collected in positive reflectron mode. HPLC is done on a Waters Acquity H-class Ultra High Pressure Liquid Chromatography system. All samples are dissolved in acetonitrile and water (1 : 1, v/v) with 0.1% formic acid and separated using a 50-100% acetonitrile gradient. The detected wavelength is 214 nm.
2. Methods for processing the data: https://pubs.acs.org/doi/pdf/10.1021/acscentsci.3c00208
Hydration parameters are calculated from ODNP experiments using previously established methods implemented through a Python-based software package called dnpLab (http://dnplab.net/, https://thcasey3.github.io/hanlab/install.html). Obtained local water diffusivities are included in this dataset (Figure 3, Figure S4).
Spin concentrations were obtained by double integration of the spectrum. Lineshape analysis is performed using the Multicomponent software (https://sites.google.com/site/altenbach/labview-programs/epr-programs/multicomponent) to obtain rotational correlation times for each sample. Raw data and calculated rotational correlation times are included in this dataset (Figure 4, Figure S3).
HPLC traces are background corrected using the Waters software. This dataset includes MALDI (Figure S1) and HPLC (Figure S2) spectra for each sample.
3. Instrument- or software-specific information needed to interpret the data: N/A
4. Standards and calibration information, if appropriate: N/A
5. Environmental/experimental conditions: N/A
6. Describe any quality-assurance procedures performed on the data: N/A
7. People involved with sample collection, processing, analysis and/or submission: Audra DeStefano, My Nguyen
- DeStefano, Audra J. et al. (2023), Design of Soft Material Surfaces with Rationally Tuned Water Diffusivity, ACS Central Science, Journal-article, https://doi.org/10.1021/acscentsci.3c00208