[PPT] - Obtaining Knowledge and Data for Separations from Molecular PowerPoint Presentation

SLIDE 1

Obtaining Knowledge and Data for Separations from Molecular Simulation

J. Ilja Siepmann

Nanoporous Materials Genome Center, Chemical Theory Center,

Depts. of Chemistry and of Chemical Engineering & Materials Science

University of Minnesota, Twin-Cities, MN

Vapor–Liquid Equilibria / Distillation Liquid–Liquid Equilibria / Extraction Sorption and Transport in Porous Materials / Adsorption & Membranes Analyte Distribution / Chromatography A Research Agenda for a New Era in Separations Science Keck Center, Washington, DC August 22, 2018

SLIDE 2

Goals and Challenges: Molecular Simulation

provide atomic-level understanding of complex

chemical systems and processes

investigate how changes in molecular architecture

and composition influence macroscopic observables

predict accurate thermophysical properties

(Chemical Industry, Vision 2020)

design improved separation processes and

functional materials

precision of particle-based simulations depends solely on sampling the important regions

in phase space

develop efficient Monte Carlo and molecular dynamics algorithms to sample

events/processes occurring at multiple physical timescales and length scales, e.g., local displacements versus self-assembly in fluid mixtures, transfer in multi-phase systems, and chemical reactions

develop software and workflows that utilize high-performance computers with

> 105 cores, novel memory distribution, and co-processor acceleration

accuracy depends solely on the force field or electronic structure theory used to describe

inter- and intramolecular interactions

develop transferable force fields (different molecules and solid sorbents,

state points, compositions, and properties)

develop Kohn-Sham density functional theory for chemisorption and reactive systems

SLIDE 3

all electron pseudopotentjal for core electrons frozen electron density

ff-atom sites

AA with multjples and/or higher-order dispersion AA with partjal charges and C6 dispersion UA with multjpoles and/or higher-order dispersion UA with partjal charges and C6 dispersion beads representjng 3 or 4 bonded atoms blobs representjng many atoms blobs representjng multjple molecules e x c i t e d s t a t e s g r

u

n d s t a t e a t

m

t r a n s f e r / r e a c tj v e c h a r g e t r a n s f e r p

l

a r i z a b l e m a n y

b
d

y t e r m s p a i r w i s e a d d i tj v e s h

r

t

r

a n g e r e p u l s i v e nuclear quantum effects classical mechanics in contjnuum space classical mechanics on lattjce

Potentials & Forces – Goarse-Graining

Strans Srot Sdihedral

particle position number of sites response

SLIDE 4

Transferability, Accuracy & Efficiency

Ø Transferable force field (FF) implies

l parameters for a given interaction site should be transferable to different molecules

(e.g., identical parameters should be used for the methyl group in, say, n-hexane, 1- hexene, or 1-hexanol)

l a specific (set of) combining rule(s) is used consistently l parameters can be used over a wide range of temperatures and pressures;

(low-temperature physics, planetary science)

l parameters can be used to predict different types of properties (e.g., thermodynamic,

structural, or transport)

Ø Accuracy can only be assessed by comparison to reliable experimental data and

ne requires data beyond the fitting set to truly assess transferability (true

versus effective potentials)

Ø “Efficiency” is a quest for simplicity while maintaining “accuracy” by adjusting

l functional form of interaction potential (square root, spherically symmetric) l number and types of interaction sites (e.g., meso-bead, united atom, all atom, nuclei &

valence electron centers)

l number of adjustable parameters

SLIDE 5

Parameterization and Transferability

Vapor-liquid coexistence curves

Bead-by-bead parameterization using experimental VLE data

Transferability of CHx united atoms Early TraPPE-UA models:

Ø Fitted to reproduce critical temperature and low-T liquid density; i.e., the

temperature-dependent Gibbs free energy of transfer

Ø Using relatively short simulations over limited temperature range

SLIDE 6

VLE – Prediction of Critical Points

Nature 1993, 365, 330

J. Chem. Eng. Data 2014, 59, 3301

SLIDE 7

Performance for VLE Prediction

200 400 600 800

Tc

exp [K]

200 400 600 800

Tc

sim [K]

Gibbs99 NERD 200 400 600 800

Tc

exp [K]

Mie 200 400 600 800

Tc

exp [K]

OPPE 200 400 600 800

Tc

exp [K]

TraPPE-EH TraPPE-UA

R

2 = 0.999

MUPE = 0.2% R

2 = 0.997

MUPE = 0.9% R

2 = 0.967

MUPE = 1.2% R

2 = 0.984

MUPE = 1.5% R

2 = 0.993

MUPE = 1.0% R

2 = 0.995

MUPE = 1.2%

200 400 600

Tb

exp [K]

200 400 600

Tb

sim [K]

Mie 200 400 600

Tb

exp [K]

OPPE 200 400 600

Tb

exp [K]

TraPPE-EH TraPPE-UA

R

2 = 0.999

MUPE = 0.5% R

2 = 0.989

MUPE = 2.1% R

2 = 0.993

MUPE = 2.0% R

2 = 0.995

MUPE = 1.0%

0.5 1 1.5

rsim [g/cm

3]

4
2

2 4

rsim/rexp - 1 [%]

Mie OPPE 0.5 1 1.5

rsim [g/cm

3]

OPLS 0.5 1 1.5

rsim [g/cm

3]

TraPPE-UA

MUPE = 0.8% MUPE = 1.0% MUPE = 1.5% MUPE = 1.1%

critical temperature normal boiling point ambient density

Siepmann, unpublished data

SLIDE 8

Fitting to Binary VLE Data

Vapor-liquid coexistence curves

EPM2 TraPPE-CO2

AIChE J. 2001, 47, 1676 JPCB 2001, 105, 9840

SLIDE 9

VLE – Azeotropes & Complex Mixtures

FPE 2004, 220, 211 JPCB 2001, 105, 3093 ACR 2007, 40, 1200

SLIDE 10

VLE - Distillation

Difference in separation factor for ethane-rich mixtures leads to 30% differences in number of stages in stripping section of distillation column

Model with distributed partial charges is significantly more accurate than point- quadrupole model

AIChE J. 2017, 63, 5098

SLIDE 11

VLE – First Principles Simulations

J. Phys. Chem. A 2006, 110, 640

0.0 0.2 0.4 0.6 0.8 1.0 r [g/ml] 400 500 600 700 T [K] rVV10 B97M-rV revPBE-D3 BLYP-D3 M06-L-D3 PBE0-D3 1.50 1.75 2.00 2.25 1000/T 2 3 4 5 6 7

lnrvap.

PCCP 2013, 15, 13578 Siepmann, unpublished data

SLIDE 12

(V)LLE – Miscibility Gaps

J. Phys. Chem. B 2005, 109, 2911

FPE 2016, 407, 269

SLIDE 13

LLE – Extraction

AIChE J. 2013, 59, 3065 1-decanol 4-decanol

SLIDE 14

LLE – Polymer Phase Behavior & Aggrgation

0.2 0.4 0.6 0.8 Weight fraction of PEP*-423 300 350 400 450 500 T [K] 222-MD-1.0 280-SZ-1.05 500-SZ-1.1 0.2 0.4 0.6 0.8 1 Weight fraction of PEP*-423 350 400 450 500 550 T [K] 217-MD-1.01 280-SZ-1.05 500-SZ-1.10 Expt Sim

n

PP

n

hhPP

n

PEP

O O CH3 H3C n−1

PEO

1
0.5

0.5 1

(δ1-δ2)SANS [MPa

1/2]

1
0.5

0.5 1

(δ1-δ2)sim [MPa

1/2]

PP+PEP hhPP+PP hhPP+PEP

Chen et al., Macromolecules 49, 3975 (2016) Chen et al., Macromolecules 51, 3774 (2018)

Low-χ Mixtures: Polyolefins High-χ Mixtures: PEO/PEP

HO OH y-1 (x-3)/2 (x-3)/2

5 10 15 20 Domain period d [nm] 0.1 1 10 χMF (Tsim = 400 K or Texpt = 300 K) mono-oligomers di-oligomers Expt mean-field

mono-oligomers di-oligomers

HO OH y-1 x-2

Extreme-χ Block Oligomers

Chen et al., ACS Nano 12, 4351 (2018)

SLIDE 15

Developing the TraPPE-zeo Force Field

heptane ethanol CO2

J. Phys. Chem. C 2013, 117, 24375
Challenge: Large discrepancies of current zeolite

force fields for adsorption of polar guest molecules

Target: non-polar, polar, and H-bonding guest

molecules in all-silica zeolites

LJ sites on both Si & O to achieve better balance

between dispersive and H-bonding interactions

A three-step, grid-based search in 5-dimensional

parameter space requiring ≈50,000 simulations

SLIDE 16

Adsorptive Separation: Gas Phase

W

Angew. Chem. Int. Ed. 2016, 55, 5938

SLIDE 17

Adsorption – Alcohol/Water in Silicalite-1

IAST: Myers & Prausnitz, AIChE J., 1965, 11, 121

10

1

10

2

10

3

10

4

5 10 15 20 N [molec/uc] alcohol(+water), sim water(+alcohol), sim alcohol(+water), IAST water(+alcohol), IAST alcohol(pure), sim 10

1

10

2

10

3

10

4

palcohol [Pa] 4 8 12 N [molec/uc]

methanol ethanol

0.0 0.2 0.4 0.6 0.8 1.0 xalcohol, ads 0.00 0.02 0.04 0.06 0.08 xalcohol, sol 0.6 0.7 0.8 0.9 1.0 xalcohol, ads 0.2 0.4 0.6 0.8 1 xalcohol, sol 10

1

10

2

10

3

10

4

Salcohol methanol, 298 K ethanol, 298 K methanol, 323 K ethanol, 323 K

Adsorption of H2O in MFI IAST overestimates separation factors for methanol and ethanol by about 5000

Bai et al., Langmuir 2012, 28, 15566; J. Phys. Chem. C 2013, 117, 24375

SLIDE 18

Adsorptive Separation – Adsorbent Screening

Ø Screening of 402 IZA-SC structures at w = 0.12% and for top-64 structures at 5 higher concs Ø Screening yields large number of frameworks that outperform MFI (ranked 15 – 26) Ø Processes may exploit FER’s high selectivity to extract 90% of EtOH contained in a 5 wt%

fermentation broth by adsorptive separation reaching down to raffinate conc of w ≈ 0.5 wt%

Ø Processes using ATN* may exploit its higher EtOH loading and reduce feed mixture from

initial w ≈ 15% down to 5 wt% and recycle the raffinate back to the fermentation broth Bai, Jeon, Ren, Knight, Deem, Tsapatsis & Siepmann, Nature Commun. 2015, 6, 5912

SLIDE 19

Ø Adsorption of n-C18, n-C24, n-C30,

2-methyl-C17, 4-methyl- C17, and 2,2-dimethyl-C16

Ø Infinite-dilution limit for all 300,000 structures, followed by

equimolar liquid-phase (p = 3 MPa) for top-3000 structures

Adsorptive Separation – Adsorbent Screening

Bai, Jeon, Ren, Knight, Deem, Tsapatsis & Siepmann, Nature Commun. 2015, 6, 5912

C18 C24 C30 mC17 dC16

ATO MTW PCOD-8113534

SLIDE 20

FPMC Studies of Adsorption in Open-Metal-Site MOFs

Dzubak, …, Smit, Gagliardi, Nature Chem. 4, 810 (2012)

Adsorption – First Principles Monte Carlo Studies

f Adsorption in Open-Metal-Site MOFs

SLIDE 21

Adsorption – First Principles Monte Carlo Studies

Chem. Comm. 2018, DOI: 10.1039/c8cc06178e

SLIDE 22

Transport – Hierarchical Structures and Nanosheets

ACS Nano 2016, 10, 7612 Nature 2017, 543, 690

SLIDE 23

Reversed-Phase Liquid Chromatography

ODS = Dimethyl octadecyl-silane with coverage of 2.9 µmol/m2, no endcapping C16 = Liquid n-hexadecane M = MeOH/water mixture A = ACN/water mixture

Anal. Chem. 2007, 79, 6551
J. Chromatogr. A 2011, 1218, 2203

LCGC North Am. 2002, 20, 516

SLIDE 24

Reversed-Phase Liquid Chromatography – Slot Model

1.56 1.14 1.16 1.02 sim 1.70 1.13 1.13 1.08 expt

Shape Selectivity

relative to triphenylene ODS @ 4.2 µmol/m2 67% ACN

> > ≈ ≈

NAP BcP TriP BaA Chrys

α = k'x k'TriP

Rafferty et al., J. Chromatogr. A 2011, 1218, 9183 Wise & Sander,

J. High. Res. Chromatogr.

1985, 8, 248

SLIDE 25

RPLC – 2D Column Selection

Virtual screening of 2-D chromatograms for 100 sets of 1,000 analytes computed for each of 319,225 column pairs using the Hydrophobic Subtraction Model

Lindsey, Eggimann, Stoll, Carr, Schure & Siepmann,

J. Chromatogr. A 2018, submitted

SLIDE 26

Size Exclusion Chromatography – Packings

Ø

Simple pore models fail to capture retention behavior (and pore size distribution)

Ø

Gel packings show linear regime for largest size range

Chen, Schure & Siepmann, J. Chromatogr. A 2018, in press

SLIDE 27

Conclusions & Acknowledgments

Ø Molecular simulation provides molecular-level knowledge on complex

separation systems

Ø Prediction of azeotropes and liquid–liquid and solid-fluid equilibria need to be

improved

Ø Hierarchical screening workflows allow for discovery of nanoporous materials Ø FPMC allows for calculation of unary and multi-component adsorption equilibria Ø Simulated systems are often too idealized and complexity needs to be increased

SLIDE 28

Conclusions & Acknowledgments

Ø Molecular simulation provides molecular-level knowledge on complex

separation systems

Ø Prediction of azeotropes and liquid–liquid and solid-fluid equilibria need to be

improved

Ø Hierarchical screening workflows allow for discovery of nanoporous materials Ø FPMC allows for calculation of unary and multi-component adsorption equilibria Ø Simulated systems are often too idealized and complexity needs to be increased