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Solvent effects in RMG (Java + )PowerPoint Presentation

Solvent effects in RMG (Java + )

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Presentation Transcript

Solvent effects are important in many chemical systems of practical interest

Engines: gumming and clogging in diesel injectors

Biological oxidation

Catalysis, Fuel cells

Thermal stability under storage conditions

Detailed kinetic modeling of solution phase systems is still fairly fictional

Objective: to boldly swim where no one has swum before

Requires modifications to handle different solvents

Automatic estimation of solvent effects on:

Species thermochemistry

Reaction rate parameters

Our application demands both accuracy and high-throughput

Actual number of reactions and species considered can be ~106

Structure-activity relations are already used in gas-phase RMG for high-throughput parameter estimation

e.g. Group additivity & Evans Polanyi relations

We are looking for similar methods for solution phase thermochemistry and kinetics

Outline

We want quick estimates of and k in different solvents

- Solution phase thermochemistry
- Solution Phase kinetics

Metrics: Accuracy and high-throughput

Solvation thermodynamics involves cavity formation and solute-solvent interactions

- Dipolarity/polarizability
- Hydrogen bonding

Digging a hole in the solvent

Free energy change of entire process =

Existing theoretical models for solute-solvent interactions are fairly accurate but computationally expensive

- Implicit solvation models
- QM description of solute, solvent as bulk continuum
- Model both kinetics and thermochemistry

- Explicit solvent molecular mechanics
- Use force fields to model solute solvent interactions

- Parameterized using experimental data

- On-the-fly quantum calculations: computationally expensive
- Gas phase approach:
- Perform QM calculations and fit groups for each solvent

Empirical models of solvation use solute/solvent descriptors to model

The Abraham model

- Linear Solvation Energy Relationships (LSER)
- Use molecular descriptors to quantify different interactions

= c + eE + sS + aA + bB + lL

Solute descriptor

Solvent dependent coefficients

Cavity Formation

Electrostatic(dipolarity, polarizability)

Hydrogen bonding

A,B,E,S,L: available for over 5000 solutes

c,a,b,e,s,l : available for >50 solvents

LSER approach is attractive if we can estimate solute descriptors for an arbitrary molecule

Plattset al. developed a group additivity approach

Benzene carbon

Non- fused aromatic

Methyl

Non- cyclic ester

-CH2-

2D molecular structure

Solute descriptors

Negligible cost per computation => high-throughput

LSER reproduce experimental descriptors for an arbitrary moleculefor a variety of solute-solvent pairs

- Minnesota Solvation Database: 935 experimental data points (130 solutes, 35 solvents)
- RMS error (RMSE) = 0.47 kcal/mol
- Outliers: solute-solvent pairs with strong H-bonding

Temperature dependence: decomposition of descriptors for an arbitrary moleculeinto&

- Method 1: Analytical expressions from hard-sphere models
- Input parameters:

- Method 2: Empirical correlations for developed by Mintz et al.*
- No new input parameters, may not be available for all solvent of interest

Testing the accuracy of hard sphere models: alkane solvents descriptors for an arbitrary molecule

- Use of correction factors drastically improves agreement with experimental data:
- Heptane: ,
- High sensitivity to molecular radii

Testing the accuracy of hard sphere models: alkane solvents descriptors for an arbitrary molecule

Testing the accuracy of hard sphere models: protic solvents descriptors for an arbitrary molecule

- Correction factors improve agreement: Octanol: ,
- Species with strong H-bonding are main outliers

Testing the accuracy of hard sphere models: protic solvents descriptors for an arbitrary molecule

Empirical correlations work for both alkane and protic solvents

Mintz estimates for alkane solvents with RMG descriptors

Mintz estimates for protic solvents with RMG descriptors

Simple approximations are used to estimate solvation of free radical intermediates

- Corrections using Platts’ group values are being used
- correct for H-bond donating ability (A) of saturated species
- all other descriptors assumed to be the same

These radical corrections are implemented in a manner similar to gas phase thermo.

*values in kcal/mol

Outline radical intermediates

We want quick estimates of and k in different solvents

- Solution phase thermochemistry
- Solution Phase kinetics

Abraham/Platts/Mintz + some QM

Solvent dependent structure activity relationships

Metrics: Accuracy and high-throughput

Outline radical intermediates

We want quick estimates of and k in different solvents

- Solution phase thermochemistry
- Solution Phase kinetics

Solvent dependent structure activity relationships

?

Metrics: Accuracy and high-throughput

Prevailing view: radical intermediatesSolvents do not influence radical reaction rates

Solvent can affect elementary reaction rates primarily through two routes

Differential solvation of transition state vs. reactants

Reactantµ = 1.97 Debye

Transition state

µ = 2.64 Debye

β-scission rates electrostatic descriptors

Solvents can affect elementary reaction rates primarily through two routes

2. Formation of reactant-solvent complexes

H-Abstraction H-bonding descriptors

Complexation with solvent can reduce availability of free reactants

It is also possible that both effects operate simultaneously through two routes

Differential solvation and reactant-solvent complexes

El-Sheshtawyet al., 2011

Snelgrove et al., 2001

To what extent can computational modeling help us quantify these effects?

Outline through two routes

We want quick estimates of and k in different solvents

- Solution phase thermochemistry
- Solution Phase kinetics

Solvent dependent structure activity relationships

?

Metrics: Accuracy and high-throughput

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