Remarkable Morphologies in Precise Acid- and Ion-Containing Polymers

Professor Stein1

FUNDAMENTALS----- Meeting Notes (8/20/15 21:27) -----Do Yoon reminded me that I ended up working with Prof. Stein for a similar reason. Namely Ed Kramer told me to work with Prof. Stein2

COLLABORATE----- Meeting Notes (8/20/15 21:27) -----Collaborate: On this particular day we had seminars by Dr. Hasegawa, Dr. Noda and Guy Berry on one day!!! 3

TRAVEL----- Meeting Notes (8/20/15 21:27) -----Develop Good Collaborations4

EDUCATEEDUCATION: Dr. Stein was ahead of the curve in using technology for education starting in mid 1980's. In addition to remotely teaching his scattering course, he was early innovator in using videos to enhance the educational experience.

SHOW the VHS tape.5August 21st , 2015Stein SymposiumUniversity of Massachusetts

Russell CompostoDepartment of Materials Science and Engineering, and Laboratory on the Structure of Matter, University of Pennsylvania

PennSheffield (UK)NSF/EPSRC Materials World Network DMR-1210379, EPSRC/5065373/1 Dynamics in polymer nanocomposites

6Why should you listen ?

Chatsworth UKPolymer diffusion in PNCs with spherical NPs is captured by a simple (empirical) parameterMechanism is not understood (ripe for theory)Diffusion in PNCs: Its more than an obstacle course7Is diffusion just an obstacle course 7

Huynh et. al. Science 2002

Rockey,Albertaphotovoltaics

Medalia et al. Science 2002.automotive

antistatic componentsprocessing

Scratch Resistant CoatingsFotoliaFIBRILTM CNT/PNCs eukaryotic cellseparations

Song et. al. Energy Enviro. Sci 2012Diffusion is important for fundamental understanding and applications8Automotive CoatingsScratch resistanceSmall particles therefore clear top coat.

Hybrid solar cell

cryo-EM of an intact eukaryotic cell that hasnt been fixed, dehydrated or stained.Actin filaments are orange; ribosomes and other macromolecule assemblies are green; membrane structures are blue.Cells are extremely crowded and macromolecules can occupy up to 40% of interier.Biological macromolecules have evolved to function in such crowded environments.How they fold, associate and diffuse through such crowded space is not well understood.

Rouse, Zimm, Ferry, deGennes, Doi, EdwardsIn the Rouse model the diffusing chain consists of monomer beads connected by entropic springs and therefore the friction coefficient of a Rouse chain as a whole is given by the monomer friction coefficient times the number of beads (M/Mo).

9

Entanglement density near particle: less entangled or more entangled? TUBEChain size perturbed? PRIMITIVE PATHFriction coefficient? Relaxation near NP slower or faster?

Nigels MD simulations show increase in contour length and primitive path

10diffusion couple consists ofPS:NP(fNP)dPS(M)T ~ Tg + 70CsiliconM = tracer molecular weightfNP= volume fraction of NPsSpherical NPs are 13 to 50nmImmobile 11Polymer diffusion tracks the position evolution of the mass center of a single molecule (probe) (at a relative long time and distance) moving in a matrix.To depth profile the tracer polymer we use ERD

Elastic recoil detection (ERD)

2D2D4He++4He++12Preview: Polymer diffusion in PNCs with Spherical Nanoparticles Is tracer diffusion faster/slower/unchanged ?Does diffusion follow a unifying theme? If so, what are dominant materials parameters?Rod-like Nanoparticles (not today)Does the shape of the nanoparticle influence the behavior of polymer diffusion ?1313NP Surface Chemistry and Shape

OHOH

SiO2dPS / PS:SiO2

dPMMA / PMMA:SiO2

dPS / PS:PS-SiO2

dPS / PS:NRdPS /PS:PS-NRNot today14

Lets start with a model system: dPS / PS:SiO2-Phenyl

Gam et. al., Macromolecules (2011)RutherfordBackscatteringPS : NP(2 vol%)PS : NP(50 vol%)

Tg = 100.7 C15Phenyl chemically similar to PS matrix (athermal)Glass transition temperature independent of NP RBS depth profile NPs (no surface segregation)TEM shows NS dispersion in bulk and near surface (inset)15

Diffusion slows down as fNP increasesGam et. al., Macromolecules (2011) T = 170 C dP = 29 nmSimultaneously account for penetrant size (i.e., 2Rg) and NP confinement.10 nm2Rg44 nm24 nm16Adding NPs slows down diffusion.The more rapid decrease at low fNP becomes stronger as M increasesMaxwell model shows weaker and linear dependence. Therefore effect is much stronger

Maxwell Model1) Molecular transport in heterogeneous media has been explained by extending the Maxwell model for conductivity to diffusion.2) In this framework, D in composites with a sparse distribution of discrete particles decreases monotonically with filler concentration. 3) However, the Maxwell model and refinements do not account for the impact of changes to chain conformation or packing near obstacles as a consequence of either entropic confinement or enthalpic interactions.

16Define a confinement parameter

ID < 2RgID > 2Rg

dConfinement ParameterGam et. al., Macromolecules (2011)ID = interparticle distance

ID

17Accounts for size of diffusing particle relative to the mesh size. Applicable for particles smaller than the mesh size.

17

Normalized D collapses when plotted versus ID/2RgGam et. al., Macromolecules (2011)Confinement Parameter19SLOW. Use silence here

Main points1) Two regimes separated at ID = 2Rg2) Slowing down even at very dilute concentrations (ID ~ 3 Rg)

Exp DetailsT = 170 C; PS matric (265 kg/mol)49k Rg = 5nm532k Rg = 22nm19Lets hammer away at confinement parameter concept

http://greaterancestors.com/who-could-have-weilded-a-64lb-hammer/

OHOH

SiO2dPS / PS:SiO2dPMMA / PMMA:SiO2

dPS / PS:PS-SiO2North Wales20There is an ancient copper mine near the coastal town of Llandudno in North Wales. This area rises 220 meters above the Irish Sea, and it is known as the location of the Great Orme Copper Mine. It dates to the Bronze Age, about 3500 years ago. More than 2500 hammers at the mine have been recovered.20

For attractive interactions, D/Do for dPMMA collapses onto master curveLin, et. al., Macromolecules, 2013PS : phenyl-NP21Stop here and do a so what. If you only did studies at dilute concentrations of NPS, you could overestimate D by a factor of 2x at a confinement parameter of 0.5. This would make a significant difference you were trying to weld two composites together or trying to use this composite as a membrane.

ATHERMAL and attractive systems behave similarly (although confinement parameter does not account for polymer-particle interactions).3 particle sizes evaluated for PMMA systemEven out to ID = 8 x polymer size we see slowing down.

How far can we hammer this confinement parameter concept? Immobile yet penetrable particles21Hard Spheres grafted with polymer brush

Jihoon Choi, et. al., ACS Macro Letters (2013).Effective Diameter: deff = d + 2 heffTracer 23k deff = 51 nm Tracer 532k deff = 70 nm

51nmEffective Diameter (SANS, SCFT) [M. Hore]22Silica NP with PS brush (87 kg/mol); PS matrix 160 kg/mol

Note: When you change M you change BOTH 2Rg and ID in the soft brush case.

22For soft NPs, D/Do collapses using confinement parameter defined by effective interparticle distance (ID).

Jihoon Choi, et. al., ACS Macro Letters (2013).23d = 51 nm silica with PS brush (87 kg/mol); PS matrix 160 kg/mol23D/Do collapses on master curve versus confinement parameterconfined and highly confined regionsslowing down long range (Meth et. al. JChemPhys B 2013)

Choi et al Macromolecules 201424Conclusions24Diffusion is more than an obstacle course(NPs can change entanglements, friction coefficient)

2525AcknowledgementsFundingNSF/Materials World NetworkNSF/DMR Polymers ProgramNSF/MRSECDupont

AlsoJeff Meth (Dupont)Mike Hore (CWR)Ken Schweizer (UICU)Michael Rubinstein (UNC)Rob Riggleman (Penn)

Neutrons: ILL, NIST, ORNLComposto GroupDr. Sangah Gam (Samsung)Prof. Jihoon Choi (Chungnam Nat.U)Chia-Chun (Jim) Lin (PhD)

Winey Group (Penn)Dr. Minfang Mu (DuPont Shanghai)Dr. Wei-Shao (Walter) Tung

Clarke Group (Sheffield)Dr. Argyrios KaratrantosDr. Mike Weir

2627Final thoughtsMechanism of slowing down theory, simulations, EBM (Muthu), etclocal and COM dynamics (longest relax. time)Cylindrical confinement versus discrete NPsLocal seg. friction does not capture discrete NP results

Diffusion in presence of mobile NPsFaster / slower / same as immobile case ?2828Part two: Polymer diffusion in PNCs with Spherical Nanoparticles Is tracer diffusion faster/slower/unchanged ?Does diffusion follow a unifying theme? If so, what are dominant materials parameters?Rod-like NanoparticlesDoes the shape of the nanoparticle influence the behavior of polymer diffusion ?2929Gordon et. al., J. Am. Chem. Soc. (2012)NR-short (43 x 5 nm) NR-long (371 x 43 nm)

Gao et. al., Langmuir (2011)Nano-rods (NR) functionalized with phenyl TiO2Similar aspect ratio ~ 9

SiO2-Ni(N2H4)Not today30

Reduced D decreases monotonically as fNR-long increases

Jihoon Choi et al ACS Macro Letters 2014dPS(168k)dPS(532k)Rg < RNRNR-long How do you define the interparticle distance for rods ?Monotonic decrease in D

Qualitatively similar to sphere

Initial decrease is stronger at higher M

Why not plot as confinement parameter? hard to define ID for randomly packed nanorods (must be in 3D)31Reduced D exhibits a minimum value and then recoversJihoon Choi et al ACS Macro Letters 2014

c = 0.04 @ L / d = 9

Dmin Rg > RNRdPS(1866k)dPS(3400k)Note: Minimum previously observed for dPS / PS:SWCNT (L/d=35), c = 0.005(Mu et. al. Macromolecules 2009)

NR-long Location of minimum depends on aspect ratio32Dmin observed if2RNR < 2Rg < L

If > c, L > 2Rg and 2RNR < 2Rg then diffusion along the rod MAY be responsible for recovery of diffusion. (trap model ?)MonotonicMinimum

NR-long NR-short33

168k532kFor NR-short, minimum observed for dPS168k and 532k

L < 2Rg1866k3400k

Jihoon Choi et al ACS Macro Letters 2014.L > 2RgRg > RNRNote D/Do for dPS (3400k) greater than dPS(1866k) !So if you prepared membranes to select out macromolecules, you would need to account for this upturn in diffusion coefficient above percolation.

Note that dPS (3400k) diffuses relatively FASTER than dPS(1866k)34

Jim Lin et al ACS Macro Letters submitted35Diffusion is faster when NPs are mobile (at low NP concentration)For nanospheres, D/Do collapses on master curve versus confinement parameterconfined and highly confined regionsslowing down long range (Meth et. al. JChemPhys B 2013)

Choi et al Macromolecules 2014

For nanorods, D shows a minimumD exhibits a minimum near percolation. anisotropic diffusion possible mechanism (simulations needed).Similar behavior for CNTs and fractal NPs. 3636How might NPs influence Rg, Ne and o ?

NPs may influence :Polymer size Rg increase: PS:CNTs (Winey); PS:xPS(McKay)decrease: PEP:silica (Richter); PDMS:silica (Nakatani)no change: PS:silica (Crawford; Schweitzer)

Entanglements NeNe decreases (Richter; Liu; Riggleman; Clarke)

Monomer Friction Coefficient o interface layer: PDMS (Saalwachter; Richter),P2VP (Sokolov), PS (Gin)no change in segmental dynamics: PEO:AAO (Richter et al)Fundamental Studies as function of length scale (Rg vs dNP), interactions, confinement (spheres versus pores), particle geometry, time scale, Entanglement density near particle: less entangled or more entangled? TUBEChain size perturbed? PRIMITIVE PATHFriction coefficient? Relaxation near NP slower or faster?

Nigels MD simulations show increase in contour length and primitive path

37What about chained spherical NPs with L / D = 5 ?

100nmFe3O4-PS: PS (270k)Lin et al Macromolecules 2014Fe3O4 (5nm) grafted with PS, fcore= 0.024

49k168k532k1866kdPS (M) fminfcore= 0.01Pure Fe3O4-PS38About 5 particles per chainPS brush: M = 132kGrafting density: 0.19 chains/nm238ID decreases rapidly as fNP increases

Does tracer diffusion behave differently in confined and highly confined regimes ?

Random dense packing(Wu ; Torquato)highly confined

Main pointsEffect of phi NP1) ID decreases most rapidly at low fNP.2) Confinement Parameter separates highly confined and confined regionse.g., For NP29, at 10% loading ID = 2Rg = 25nm

Effect of NP size1) As NP size decreases, fNP at high confinement decreasesJeff Meth: Excluded Volume Model for Reduced Diffusion CoefficientMeth et al, J. Phys. Chem. B 2013, 117, 15675-15683

2r2Rs

2r2Rg

f1) Nanocomposite topology is treated as an ensemble of confining pores (cylinders).

2) Diffusion through each pore is reduced by hydrodynamics, which is the same as configurational entropy loss.3) The distribution of pore sizes is modeled from the theory of a hard sphere fluid.4) The ensemble average is calculated and compared with data.cylinder radius / particle diameterNormalized Pore Size Probability

40Diffusion collapses for two NP diameters

29 nm13 nmGam et. al., Soft Matter 2012; Meth et. al. JPC 20134141Chart1105.74105.1799.65107.03106.26100.38106.94106.59100.62106.97106.26100.43106.66106.499.950000106.96106.56101.47107.19106.83101.1000107.41107.24101.57107.34107.25101.27107.27107.19101.54106.59106.5100.14106.56105.97100.94106.73106.59100.94105.74105.170

Phenyl 1st heatPhenyl 2nd heatPhenyl CoolingVolume % nanosilicaTg (C)

Sheet1286333PhTMS in PolystyreneV%W%Sample1st heat2nd heatCooling1st heat2nd heatCooling1st heat2nd heatCooling00834035E115259-270105.74105.1799.654.903.9716.510.056070.054180.0558111.98834036D101332-881107.03106.26100.385.454.4516.260.056640.055180.0510123.92834037D101332-892106.94106.59100.626.233.9416.120.057120.052830.0488359.52834038D101332-815106.97106.26100.435.744.7216.680.048260.051600.046951018.18834039D101332-9010106.66106.4099.955.844.9016.640.041810.047790.04009290866SampleDesignationDescription1st heat2nd heatcooling1st heat2nd heatcooling1st heat2nd heatcooling4057.14848304D101332-12840 V% PHTMS-SILICA in PS106.96106.56101.476.466.276.820.017100.024750.021593046.15848305D101332-12930 V% PHTMS-SILICA IN PS107.19106.83101.107.395.015.480.024870.031230.02546289658 & 288528SampleDesignationDescription1st heat2nd heat1st heat2nd heat1st heat2nd heat5066.67844119D101332-11150 V% PHTMS-SILICA IN PS107.41107.24101.577.887.5017.420.011490.018140.013352033.33844120D101332-11220 V% PHTMS-SILICA IN PS107.34107.25101.275.303.8316.310.031940.036130.034211018.18844121D101332-11410 V% PHTMS-SILICA IN PS107.27107.19101.544.443.5415.880.03860.041690.0405559.52840684D101332-1015 V% PHTMS-SILICA IN PS106.59106.50100.143.464.1117.330.057290.046290.0457923.92844122D101332-1152 V% PHTMS-SILICA IN PS106.56105.97100.944.694.2615.650.05260.053280.0500211.98844123D101332-1161 V% PHTMS-SILICA IN PS106.73106.59100.944.883.9215.470.053490.051930.0468900834035E115259-270 V% PHTMS-SILICA IN PS105.74105.174.903.970.056070.05418289042 & 288528SampleDesignationDescription1st heat2nd heat1st heat2nd heat1st heat2nd heat5066.67842338D101332-11050 V% No Mod Nanosilica/PS107.58107.08101.258.218.8120.350.013030.018160.0128159.52840642D101332-975 V% No Mod Nanosilica/PS106.61106.26101.084.624.4716.180.044630.052940.04749290198SampleDesignationDescription1st heat2nd heat1st heat2nd heat1st heat2nd heat00846190E115259-78-1Unfilled Polystyrene (Neat)106.88106.39100.284.783.9715.960.050300.050760.0482400846191E115259-78-2Unfilled Polystyrene (Neat)106.66106.50100.744.783.9215.110.053530.051470.0468300846192E115259-78-3Unfilled Polystyrene (Neat)106.64106.40100.464.623.7115.860.050770.051830.0501122846193D101332-124-12 w% PHTMS-Polystyrene106.50106.43100.844.793.8116.020.054520.053520.0526522846194D101332-124-22 w% PHTMS-Polystyrene106.49106.60100.314.493.7416.520.051850.053690.0514722846195D101332-124-32 w% PHTMS-Polystyrene106.64106.50101.334.793.7614.860.055580.051950.04784288765, 288278SampleDesignationDescription1st heat2nd heat1st heat2nd heat1st heat2nd heat5066.67839908D101332-105106.42105.6099.877.447.4415.290.014120.018790.014455066.67839906D101332-104-1104.93104.6599.638.669.5315.310.019770.025310.013845066.67839907D101332-104-2105.97105.36100.298.127.2715.820.019330.020480.014572033.33840685D101332-106106.51106.0299.813.894.1517.330.023680.034330.034081018.18840686D101332-107106.54106.07101.063.753.9715.980.047590.043570.0420459.52839905D101332-95-2106.35106.02100.574.414.0516.540.045900.048220.0472323.92841494D101332-108106.07106.15100.854.903.9215.160.051370.048670.0474011.98841495D101332-109106.46106.40101.574.773.6516.110.051800.049820.0507300.0551000.05500834035E115259-27105.74105.174.903.970.056070.05418100.0551000.0495200.0551000.044290224 & 288528300.0551000.0385SampleDesignationDescription1st heat2nd heat1st heat2nd heat1st heat2nd heat400.0551000.03311.98846318D101332-1171 V% DMAC-ST in PS106.03105.75100.344.873.9316.440.053470.052320.05266500.0551000.027523.92846319D101332-1202 V% DMAC-ST in PS107.02106.78101.524.503.7815.030.051390.050800.04744600.0551000.0221018.18846320D101332-12110 V% DMAC-ST in PS107.13106.50102.284.434.0215.730.038070.043390.03943700.0551000.01652033.33846321D101332-12220 V% DMAC-ST in PS107.94106.78101.176.735.0316.570.024170.033780.03101800.0551000.0115066.67846322D101332-12350 V% DMAC-ST in PS99.5099.1510.2117.440.012040.00801900.0551000.005559.52840683D101332-995 V% DMAC-ST in PS106.69106.81100.544.963.8416.630.044690.04450.046341000.0551000106.36100.71

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Sheet1

Phenyl 1st heatPhenyl 2nd heatPhenyl CoolingUnmodified 1st heatUnmodified 2nd heatUnmodified CoolingPhTMS 1st heatPhTMS 2nd heatPhTMS coolingPhenylethyl 1st heatPhenylethyl 2nd heatPhenylethyl coolingDMAC-ST 1st heatDMAC-ST 2nd heatDMAC-ST CoolingVolume % nanosilicaTg (C)

Sheet2

Phenyl 1st heatPhenyl 2nd heatPhenyl CoolingUnmodified 1st heatUnmodified 2nd heatUnmodified CoolingPhTMS 1st heatPhTMS 2nd heatPhTMS coolingPhenylethyl 1st heatPhenylethyl 2nd heatPhenylethyl coolingDMAC-ST 1st heatDMAC-ST 2nd heatDMAC-ST CoolingVolume % nanosilicaTg Width (C)

Phenyl 1st heatPhenyl 2nd heatPhenyl CoolingUnmodified 1st heatUnmodified 2nd heatUnmodified CoolingPhTMS 1st heatPhTMS 2nd heatPhTMS coolingPhenylethyl 1st heatPhenylethyl 2nd heatPhenylethyl coolingDMAC-ST 1st heatDMAC-ST 2nd heatDMAC-ST CoolingLinear guideWeight % NanosilicaHeat Flow (W/g)

Phenyl 1st heatPhenyl 2nd heatPhenyl CoolingVolume % nanosilicaTg (C)

00.056071.980.056643.920.057129.520.0482618.180.0418157.140.0171046.150.0248766.670.0114933.330.0319418.180.03869.520.057293.920.05261.980.0534900.0560766.670.013039.520.0446300.0503000.0535300.0507720.0545220.0518520.0555866.670.0141266.670.0197766.670.0193333.330.0236818.180.047599.520.045903.920.051371.980.0518000.056071.980.053473.920.0513918.180.0380733.330.0241766.679.520.0446900.054181.980.055183.920.052839.520.0516018.180.0477957.140.0247546.150.0312366.670.0181433.330.0361318.180.041699.520.046293.920.053281.980.0519300.0541866.670.018169.520.0529400.0507600.0514700.0518320.0535220.0536920.0519566.670.0187966.670.0253166.670.0204833.330.0343318.180.043579.520.048223.920.048671.980.0498200.054181.980.052323.920.0508018.180.0433933.330.0337866.670.012049.520.044500.055811.980.051013.920.048839.520.0469518.180.0400957.140.0215946.150.0254666.670.0133533.330.0342118.180.040559.520.045793.920.050021.980.04689066.670.012819.520.0474900.0482400.0468300.0501120.0526520.0514720.0478466.670.0144566.670.0138466.670.0145733.330.0340818.180.042049.520.047233.920.047401.980.0507301.980.052663.920.0474418.180.0394333.330.0310166.670.008019.520.04634

2

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