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Our Informatics Approach

Our Informatics Approach. Key Experimental Validation Needed. R 2 = 0.78. R 2 = 0.52. PREDICTED. PREDICTED. dispersive. polar. ACTUAL. ACTUAL. Step 1 - Predict Surface Energies - MQSPR Breneman Group. Lone Pair Info Electrostatic Potential Electron Density. Morphology descriptors.

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Our Informatics Approach

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  1. Our Informatics Approach Key Experimental Validation Needed

  2. R2 = 0.78 R2 = 0.52 PREDICTED PREDICTED dispersive polar ACTUAL ACTUAL Step 1 - Predict Surface Energies - MQSPR Breneman Group Lone Pair Info Electrostatic Potential Electron Density

  3. Morphology descriptors Generate statistical 2-point correlation function System being studied: 3% ChloroSiilica PMMA Binary image Statistically equivalent 3D/2D reconstruction 2p correlation data Rc - Agglomerate Size Rd - Agglomerate Spacing Brinson Advanced Materials Lab

  4. 5.6μm 16.7μm 5.6μm Micrograph 3%CPMMA 8%OPS 8%APS 2p-correlation 2D-reconstruction Brinson Advanced Materials Lab

  5. Dispersion and Mobility Predictions - Short Molecule • If γliquid < γsolid, the liquid wets the solid surface • If we think of the particles as the liquid, if their energy is low enough, they are more likely to want to “wet” the polymer • (or they have less incentive to agglomerate initially) So perhaps we can use that to predict propensity to aggregate Owens, D.K.; Wendt, R. C. JOURNAL OF APPLIED POLYMER SCIENCE. 1969, 13, 1741-1747.

  6. Systems Studied - 1,2,3,8 wt% Loading Aminopropyldimethylethoxysilane (APDMES: NH2-C3H6-Si(CH3)2-O-C2H5) Octyldimethylmethoxysilane (ODMMS: CH3-(CH2)7-Si(CH3)2-O-C2H5), Chloropropyldimethylmethoxysilane (CPDMES: Cl-C3H6-Si(CH3)2-O-C2H5)

  7. Correlation Between Cosθ, ΔWa and NNI / Skewness Increasing ΔWa CPDMES-PS CPDMES-PMMA θ=0ο Rc: 42nm Rd: 422 Rc: 21 Rd: 190 ODMMS-PS ODMMS-PMMA Increasing cosϴ θ=0ο Rc: 41nm Rd: 418 Rc: 41nm Rd: 418 Rc: 48nm Rd: 462 APDMES-PMMA APDMES-PS Rd: 224 Rd: 2463 Rc: 175 Rd: 2036

  8. 50 3% Loading 8% Loading Decreasing Degree of dispersion 40 30 20 10 0 γs Complete Wetting γsd Dispersion Related to Contact Angle Total Surface Energy Polar Component of the Surface Energy Stöckelhuber, K. W., Das, A., Jurk, R., & Heinrich, G. (2010). Polymer, 51(9), 1954-1963

  9. F P F F P P F P 3% Loading 8% Loading Dispersion Related to Relative Work of Adhesion • The propensity to flocculate / aggregate is related to the relative work of adhesion (the difference in affinity of FF, PP, FP) 70 60 50 40 30 20 10 0 Iso ΔWa lines Considers Filler / Filler affinity as well as Polymer / Polymer and Polymer / Filler affinity Total Surface Energy (mJ/m2) • 0 5 10 15 20 25 Polar Component - Surface Energy *Wang, M. J. Rubber Chemistry and Technology. 1998, 71, 520–589.

  10. 70 60 50 40 30 20 10 0 Iso ΔWa lines 10 5 Tg UP 5 2 2 Tg DOWN 0 1 -2 Amino .2 -5 Chloro Total Surface Energy mJ/m2 Iso Ws lines Butyl(~Octyl) Trimethyl Dimethylchloro Fluoro Tolyl • 0 5 10 15 20 25 Polar Component - Surface Energy Work of Spreading Related to Tg? • The change in MOBILITY, however, is due to the work of spreading - the difference in the cohesive energy of polymer and the work of adhesion between polymer and filler. • Negative - mobility UP • - Tg DOWN • Positive - mobility DOWN • - Tg UP Considers only Polymer / Polymer & Polymer / Filler affinity *Wu, S. Polymer Interface and Adhesion; Marcel Dekker: New York, 1982

  11. Our Informatics Approach

  12. Calculate properties of polymer and interphase at each T Extract viscoelastic response of the composites (Tanδ) Adding interphase layer according to energy parameters Tg of the composites are found Brinson Advanced Materials Lab

  13. Comparison between experiment and FEA Brinson Advanced Materials Lab

  14. Intermittent Summary We have developed and informatics based approach that 1. Uses physics based heuristics (MQSPR) to predict the particle and polymer surface energies 2. Correlates those surface energies to dispersion and interface properties 3. Recreates a 3D morphology in FEA and predicts thermomechanical behavior We have identified key areas for further improvement 1. Image analysis to capture all the small particles 2. Better 2D correlation 3. Improved MQSPR of surface energies 4. Perhaps a heterogeneous matrix in the FEA model? NEW CHALLENGE - SHORT MOLECULES DON”T ENTANGLE WITH THE MATRIX MINIMAL CONTROL

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