Application of COSMO-RS in the Design of Ionic Liquid Systems

Transcription

Application of COSMO-RS
in the Design of
Ionic Liquid Systems
Steve Lustig
Andreas Klamt
E.I. du Pont de Nemours & Co.
Central Research & Development
Wilmington, Delaware, USA
[email protected]
COSMOlogic GmbH & Co. KG
Leverkusen, Germany
[email protected]
2
Complexity of Chemical & Materials Engineering Design
Element
Atom
Molecule
Cluster
Properties, e.g.
Thermodynamic
Transport
Reaction
Structure
Functions, e.g.
State
Solution
Material
System
4/5/2012
Technological (useful)
1. Interconversions
2. Performance materials
3. Energy storage & utilization
Biological
1. “Life”
2. Disease
3. Foods, medicines
3
Complexity of Chemical & Materials Engineering Design
Element
Atom
Molecule
Cluster
Properties, e.g.
Thermodynamic
Transport
Reaction
Structure
Functions, e.g.
State
Solution
Material
System
4/5/2012
Technological (useful)
1. Interconversions
2. Performance materials
3. Energy storage & utilization
Biological
1. “Life”
2. Disease
3. Foods, medicines
4
Outline
• Introduction to COSMO Theory
• What is COSMO-RS?
• Benchmarks
• Ionic Liquids for Absorption Cooling
• What are Ionic Liquids?
• What is Absorption Cooling?
• Can you design an optimal absorbent?
• Li Ion Battery Electrolytes
• How stable? How much salt will dissolve?
• How many charge carriers?
• Practical Aspects of COSMO-RS
4/5/2012
5
Outline
• Benchmarks
4/5/2012
6
Introduction to COSMO Theory
COnductor-like Screening MOdel (COSMO) of solvation with exact statistical
mechanics to predict pure-component and mixture thermodynamic properties (e.g.
vapor pressure, solubility, activity coefficients, VLE, LLE, pKa, binding) of given
compounds.
Developed by:
• Andreas Klamt (COSMOlogic) COSMO-RS
A. Klamt and G. Schuurmann, J. Chem. Soc. Perkin Trans., 2, 799 (1993).
A. Klamt, J. Phys. Chem., 99, 2224 (1995)
A. Klamt, et al., J. Phys. Chem. A., 102, 5074 (1998)
F. Eckert and A. Klamt, AIChE J. 48, 369 (2002).
Later investigators include:
• S. Sandler COSMO-SAC
S.-T. Lin and S. Sandler, Ind. Eng. Chem. Res., 41, 899 (2002)
S.-T. Lin, J. Chang, S. Wang, W. Goddard and S. Sandler, J. Phys. Chem. A., 108 7429 (2004)
• C. Panayiotou
C. Panayiotou, Ind. Eng. Chem. Res., 42, 1495 (2003)
• J. Gmehling COSMO-RS(O1)
H. Grensemann, J. Gmehling, Ind. Eng. Chem. Res., 44, 1610 (2005)
4/5/2012
7
COnductor-like Screening MOdel (COSMO) of solvation with exact statistical
mechanics to predict pure-component and mixture thermodynamic properties (e.g.
vapor pressure, solubility, activity coefficients, VLE, LLE, pKa, binding) of given
compounds.
Developed by:
• Andreas Klamt (COSMOlogic) COSMO-RS
A. Klamt and G. Schuurmann, J. Chem. Soc. Perkin Trans., 2, 799 (1993).
A. Klamt, J. Phys. Chem., 99, 2224 (1995)
A. Klamt, et al., J. Phys. Chem. A., 102, 5074 (1998)
F. Eckert and A. Klamt, AIChE J. 48, 369 (2002).
COSMO-RS in three, easy steps…
4/5/2012
8
(http://www.cosmologic.de/ChemicalEngineering/theory_background.html)
1. Solve an exact electrostatic problem using ab initio electronic structure chemistry
Solvation Shell comprising
ideal-conductor charge tiles
Polarization charges, , on conductors
Probability, p(σ)
The result is a
distribution, p(), of 
charge tiles; a
fingerprint of the
molecule.
4/5/2012
Charge Density, σ
9
2. Use experimental data to ‘fit constants’ in an energy expression for real solutions
using only , p() and atomic element descriptors
The result is a Hamiltonian
involving only the charge tiles
which are independent of
chemical group or molecular
structure
E  EMisfit  EHydrogenBond  E LD  ERing
EMisfit  ,   
aeff 
   2
2
E HydrogenBond  ,    cHB f HB (T ) min 0; min( ,  )   HB  max0; max( ,  )   HB 
4/5/2012
10
3. Use exact (and fast) statistical mechanics to compute thermodynamic properties.
The results are expressions for chemical potential, activity coefficient, vapor
pressure, solubility, LLE, VLE, etc. for pure components and mixtures.
Sigma Profile of Mixtures:
pS ( )   xi pi ( )
iS
  S ( )  E ( ,  ) 
 S ( )  kT ln  d  pS ( ) exp 

kT


Chemical potential of a charge tile:
Chemical potential of solute X in solvent S:
 SX ( )   d p X ( )  S ( ) -  kT lnAS
COSMO-RS is implemented in COSMOthermX Version 2.1_0106 by F. Eckert and A. Klamt, COSMOlogic GmbH & Co. KG
F. Eckert and A. Klamt, “Fast Solvent Screening via Quantum Chemistry: COSMO-RS approach,” AIChE J. 48, 369 (2002).
4/5/2012
11
Vapor Pressure:
PiVap
i
IdealGas





IdealGas
i
i
 Pi
exp 

RT


Liquid Phase Activity Coefficient:
  

ln  iS   iS   ii RT
Chemical potential continuity between vapor and liquid:
yi  iG P  xi iS PiVap
Chemical potential continuity between liquid 1 and liquid 2:
Pi ( 2,1)
4/5/2012
 (  i(1)   i( 2 ) ) V1 
 exp 

RT
V2 

"COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design",
Andreas Klamt, Elsevier Science Ltd., Amsterdam, The Netherlands (2005)
12
Advantages over traditional methods, e.g. Monte Carlo and Molecular Dynamics
•
Once a molecule’s electronic structure is solved using quantum mechanics, the
sigma distribution is presumed applicable to any mixture without recomputation.
(we create an electronic structure database for all important compounds)
•
Prediction of thermodynamic properties is very fast
(about 1 minute on a laptop for pure compounds and mixtures)
(MC and MD calculations take days/weeks for comparable accuracy)
Applicable to:
 Multicomponent solutions
 Salts
 Polymers and polypeptides
 Surfaces
 Interfaces
4/5/2012
13
Benchmark Study of HFC Henry’s Law Constants
Henry’s Law expresses Vapor-Liquid Equilibrium
COSMO-RS Bound [bmim][PF6] Pairs
10
3
10
2
10
1
10
0
HFC Gas Phase
Prediction (MPa)
HFC + IL Liquid Phase
GasPhase
LiquidPhase
 HFC
  HFC
GasPhase
HFC
P
GasPhase
HFC
H
P
P
H
Vapor
HFC

LiquidPhase LiquidPhase
HFC
HFC
x
LiquidPhase
HFC HFC
LiquidPhase
is
constant
as
HFC
HFC
4/5/2012
10
x
x
-1
10
0
CF4
CHF2Cl
CHF3
CH2F2
CH3F
CH4
CO2
CHF2CF3
CF3CH2F
CHF2CHF2
CH3CF3
CH3CHF2
CH3CH2F
CH3CH3
-1
10
0
10
1
10
2
10
3
Experiment (MPa)
Data by Mark Shiflett (CR&D-CS&E)
at 283, 298, 323 and 348K
14
Benchmark Studies
HFC Boiling Point Predictions
 HFC isomer boiling points are
predicted qualitatively correctly,
quantitatively  < 8oC
 Additional study with over 1,000
compounds shows boiling points
predicted  < 8oC
4/5/2012
15
Outline
• Benchmarks
4/5/2012
16
Ionic liquids - Variety Through Combination
common cations
[Cation][Anion]
CH3
F
N
C8H17
C8H17
N
N
C8H17
CH3
C4H9
C6H13
P
F
F
F
P
F
F
F
F
methy(trioctyl)ammonium
C6H13
N
C4H9
C14H25
trihexy(tetradecyl)phosphonium
O
F3C S O
O
triflate
F
O
F
O
N S
F3C S
CF3
O
O
triflimide
N-butlypyridinium
Estimated that >109 ionic liquids can be synthesized
4/5/2012
B
tetrafluoroborate
hexafluorophosphate
1-butyl-3-methylimidazolium
C6H13
common anions
17
Typical Ionic Liquid Properties
o
- liquid cation and anion, RTIL defined with Tm < 100 C
- negligible vapor pressure
- liquid over a wide temperature range
- high thermal, chemical and electrochemical stability
- form stable hydrophilic or hydrophobic solutions
- dissolve many organic and inorganic compounds
- variable solubility in gases and liquids
4/5/2012
18
What Is Absorption Cooling?
• Uses a heat source to provide the energy needed
to drive the cooling system.
• Popular alternative to regular compressor
systems where:
 electricity is unreliable, costly, or unavailable,
 noise from a compressor is problematic,
 surplus heat is available (e.g., from
engines/motors, turbine exhausts or industrial
processes, or from solar plants), or
 large cooling capacity required (large buildings)
First commercialized by Electrolux, no moving parts
http://en.wikipedia.org/wiki/Einstein_refrigerator
4/5/2012
http://en.wikipedia.org/wiki/Absorption_heat_pump
19
Ionic Liquid Design for Aqueous Absorption Cooling
Cooling
Tower
Coefficient of Performance, COP
Chemical
Compressor
H2O
COP 
Refrigerant
Condenser
Generator
5
2
6
Heat Exchanger
IL
Absorbent
H2O
3
1

Water
4/5/2012
Absorber
7
Chilled Water
to Building
4
H2O /IL
Solution
QGenerator
H 7  H 6 
H 2O
 1  wGenerator
H 5  H 3   H 3  H 4  H 2O
H 2O
 wAbsorber  wGenerator
Efficiency 
H 2O
H 2O
with  wAbsorber
and  wGenerator
Refrigerant
Evaporator
QEvaporator
or
dw H 2O
dT
 0,
dw H 2 O
dP
 0



20
•
Coefficient of Performance, COP
COP 
•
QEvaporator
QGenerator

H 7  H 6 
H2 0
 1  wGenerator
H 5  H 3   H 3  H 4  H 2 0
H2 0
w
w

Generator
 Absorber



Design Statement
H 2O
H 2O
 wGenerator
wAbsorber
objF {[ A][B ] / H 2 O} 
H 2O
1  wGenerator
Maximize objF {[ A][B ] / H 2 O} through selection of [ A][B ]
•
4/5/2012
Process Conditions
PAbsorber  8mbar
PGenerator  66 mbar
TAbsorber  38 o C
TGenerator  180 o C
21
Design [A][B]/H20 to: maximize ObjF &
minimize thermal decomposition
IL Database contains
> 10,000 pairs
Given Cation
Anion Pair
Calculate water
solubility in absorber
Solubility > 5%
?
no
Next Pair
yes
T=100oC
Calculate water solubility
in generator @ T
T=T+5oC
Next Pair
4/5/2012
Save
Candidate
yes
Solubility
Drop > 5%
?
no
22
Design [A][B]/H20 to maximize ObjF
1 ,5 0 0
6,080 cation/anion pairs designed+screened
1 ,0 0 0
Count
Best balance of
• hydrogen bonding
• hydrophobicity
500
0
0
0 .1
0 .2
0 .3
0 .4
o b jF
4/5/2012
0 .5
0 .6
0 .7
23
Complete Process Data Measurement
Home-built multisample
vapor-salt analysis system
Heated Transfer Lines
Jacketed Water
Bubble Column
Oven
• Simultaneous measurement
of many compositions
• Attains all Absorber and
Generator conditions
Oil Bath/Circulator
Mass Flow Controlled Nitrogen
4/5/2012
Sealed Desicator
& Open Samples
Wwater,absorber
24
0.07
0.06
• While pure LiBr absorbs the most water in
absorber conditions, it also retains the most water
in both generators
0.05
C
Complete Process Data Measurement
0.20
0.25
0.30
0.35
0.40
0.04
0.03
• Additives limit the water retained in the absorbent
under generator conditions and suppress
crystallization at low water contents
0.02
0.01
• Data validate prior COSMO-RS predictions
qualitatively
0.00
0.80
0.85
0.90
0.95
1.00
PHI
Wwater,generator,high-T
Wwater,generator,low-T
0.07
0.07
0.06
0.06
0.03
C
C
0.04
0.04
0.03
0.02
0.02
0.01
0.01
0.00
4/5/2012
0.80
0.85
0.90
PHI
0.95
1.00
0.20
0.25
0.30
0.35
0.40
0.05
0.20
0.25
0.30
0.35
0.40
0.05
Pure
LiBr
0.00
0.80
0.85
0.90
PHI
0.95
1.00
25
Energy Efficiency and Lifetime Operating Costs
• Lower crystallization temperature allows higher energy efficiency
• Modeling predicts higher COP (10%) with lower lifetime costs (7%)
4/5/2012
10% increase
Lifetime Cost
0.07
1.52
1.50
1.48
1.46
1.44
1.42 LiBr
1.40
1.38
1.36
0.01
0.02
0.03
C 0.04
0.05
0.06
0.07
7% increase
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
0.90
0.95
1.00
I
PH
0.85
0.80
0.06
$MM
0.05
C
C OP
Parallel Double-Effect Process Model
4.70
4.75
4.80
4.85
4.90
4.95
5.00
0.04
0.03
0.02
0.01
0.00
0.80
0.85
0.90
PHI
0.95
1.00
26
Concluding Remarks
• The chemists had no experience or intuition how to select
absorbents which would optimize the design criteria
• The COSMO-RS screening provided a valid short list of
candidates with qualitatively-correct ranking
• COSMO-RS & Chemical principles gave qualitative
understanding on why optimum candidates ranked well-
Small & Low molecular weight for low heat capacity
Balance hydrophobicity & hydrogen bonding
• Experimental measurements and Aspen® process modelling
validated the best final candidates
4/5/2012
27
Outline
• Benchmarks
4/5/2012
28
Lithium Ion Battery Electrolytes
• A solvent is needed to dissolve LiPF6 salt which shuffles ions
between the anode and cathode
• The solvent must be electrochemically stable at high potentials
near the electrodes
• The solvent must facilitate high conductivity and remain liquid and
chemically stable between -30oC and 60oC (other needs too…)
• So many questions, so little time:
 How can we design
electrochemical stability?
 How can we design high ionic
conductivity?
 How can we predict solubility and
ion speciation?
4/5/2012
http://www.nbpowersupply.com
29
Temperature-Dependent Solubility of LiPF6 in Propylene Carbonate
 Data points from V.M. Plakhotnik and I.V. Goncharova, Ukr. Khim. Zhurn. 66, 31 (2000)
 Refractive Index method to determine concentration (suspicious accuracy)
0.24
Mole Fraction LiPF
6
0.22
0.20
0.18
0.16
0.14
0.12
0.10
-40
-20
0
20
Temperature, C
4/5/2012
40
60
30
31P
NMR Measurement of LiPF6 Concentration
Capillary insert NMR technique with sample and concentration reference
Sample of unknown LiPF6
concentration
Standard 85wt% D3PO4
Sample Concentration is computed from tube geometrical factor & spectral integrals,
C LiPF 6  1 .59
4/5/2012
ILiPF 6
ID 3PO 4
, molar
31
31P
NMR Determination of LiPF6 Solubility
• The experimental method is validated to +4% precision
• Experimental LiPF6 Solubilities
0.7
Solubility, Mole Fraction
0.6
0.5
XC12
0.4
PC
0.3
0.2
0.1
0.0
0
10
20
30
Temperature, C
4/5/2012
40
50
60
32
“COSMOsalt” Theory Overview
Gfus

LiPF
6

Equilibrium G 

Solid
LiPF6
Species
x
i
iSolution is at a minimum
i
subject to constraints :
Solution
Solid
 LiPF
LiPF
6
6
Species
x
i
1
i
Species
4/5/2012
Solution
LiPF6
x
i
i
qi  0
33
COSMO-RS Theory for LiPF6-Solvent Solubility
Temperature-Dependent Solubility of LiPF6 in Propylene Carbonate & XC-12
•
Data from Lustig 31P-NMR measurements
•
Simplest system equilibrium hypothesis:
LiPF6 (solid)  Solvent  Li   PF6-  Solvent
LiPF6 Solubility
0.70
Simple Hypothesis Fails!
Unpaired Propylene Carbonate
H fus  10.2 kcal / mol
Solubility Limit, x 
0.60
S fus  0.030 kcal / mol  K
0.50
Propylene Carbonate
0.40
XC-12
Unpaired XC-12
0.30
H fus  4.3 kcal / mol
S fus  0.003 kcal / mol  K
0.20
0.10
10
4/5/2012
20
30
40
Temperature, C
50
60
34
Species in Solution in Equilibrium with LiPF6(s)
Propylene Carbonate + LiPF6
XC12+ LiPF6
PC
XC12
LiPF6
LiPF6
LiPF6 (PC)
LiPF6 (XC12)
LiPF6 (PC)2
Li+
Li+
Li+ (PC)
Li+ (XC12)
Li+ (PC)2
Li+ (XC12)2
Li+ (PC)3
Others unfinished
Li+ (PC)4
PF64/5/2012
PF6-
35
Gfus  - 3.326 kcal/mol
PC Measured
PC Predicted, Constant G
0.8
fus
XC12 Measured
LiPF6 Solubility, mole fraction
0.7
XC12 Predicted, Constant G
fus
0.6
0.5
0.4
0.3
0.2
0.1
0.0
4/5/2012
10
20
30
40
Temperature, C
50
60
36
Species in PC Solution in Equilibrium with LiPF6(s)
x[PC]=
0.201635
x[LiPF6]=
0.017383
x[LiPF6PC]= 0.010213
x[LiPF6PC2]= 0.075880




4/5/2012
x[Li+]=
x[LiPC+]=
x[LiPC2+]=
x[LiPC3+]=
x[LiPC4+]=
0.317741
0.015346
0.001059
0.000247
0.013051
x[PF6-]=
0.347445
Much solvent is paired with ions
More Ion-Solvent species drags more Li+ into solution
Larger solvated species will transport more slowly
Anion-Solvent species in progress
37
Lithium Ion Battery Electrolytes
Concluding Remarks
• Always validate data before you trust it!
• Always validate theory before you trust it!
• “COSMOsalt” speciation provides critical and
unique insight into both solubility & charge
carrier concentrations+speciation
4/5/2012
38
Outline
• Benchmarks
4/5/2012
39
Practical Aspects of COSMO-RS
Create results in vacuum & COSMO states
Quantum chemistry software with or without GUI
User needs to know about:
• Successful QM calculation criteria
• Rotational Isomerism & Ground States
Apply statistical thermodynamics
• Property prediction software with or without GUI
• Predicted properties: Activitie coefficients, Henry’s law
coeff., Vapor pressure of pure compounds and mixtures,
Heat of vaporization, Separation coefficients, Heat of
mixing, Phase diagrams, VLE, LLE, SLE, Solubility in
polymers, partition coefficients, probably lots more…
• My most-valued result is chemical potential!
4/5/2012
40
4/5/2012
41
Experimental Station
Wilmington, Delaware
Chemical design= relevant solutions
4/5/2012
42
Try to solve big, important problems
4/5/2012
43
Chemists’
Skill+Intuition
Theory+
Computation
4/5/2012
Use theory+computation especially
when chemists admit they need help
44
4/5/2012
A. Klamt, COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design, Elsevier, New York, 2005.
45
4/5/2012
A. Klamt, COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design, Elsevier, New York, 2005.
46
0.20
0.60
44414P+
BMIM+
PMPY+
DMPIM+
Probability
Probability
0.15
0.70
0.10
BEIEtOSO3HFPSPF6TPES-
0.50
0.40
0.30
0.20
0.05
0.10
0.00
-2.0
-1.5
-1.0
0.0
2
 charge (e/A )
4,4,4,14P+
PMPY+
4/5/2012
-0.5
0.5
1.0
BMIM+
0.00
-1.5
-1.0
-0.5
0.0
0.5
 charge (e/A )
EtOSO3-
BEI-
DMPIM+
HFPS-
1.0
2
TPES-
1.5
PF6-
2.0
47
What Is Absorption Cooling?
• Absorption cooling refrigeration was invented by the French scientist
Edmond Carré in 1858. The original design used water and sulfuric
acid.
• Possible solution for more efficient energy usage
70% of U.S. Electricity is used in: commercial buildings, schools,
hospitals, institutional workplaces
Combined Cooling/Heating/Power (CCHP) Units use waste heat,
reduce energy usage, reduce CO2 emissions (20 – 40%)
• Possible solution to reduce greenhouse & ozone-depleting emissions
Can use water as a refrigerant
• Most efficient cooling system for large buildings, becoming more
economical for single family residences
4/5/2012

Application of COSMO-RS in the Design of Ionic Liquid Systems

Transcription

Similar documents

June 2016

“After” - Mexel USA

spraycool™ technology replaces fans and heat

Acson Moveo Catalogue 2015 (Online).compressed

Boiling water ready to drink in 80 seconds

Know More - Reliance Infrastructure

mexel 432® mexel 432

Surprising Ways That Your Building Wastes Energy

Wine Coolers

Product catalogue teco cs 230