1. Computing the photophysics and 1O2
sensitization characteristics of BODIPY
dyes
Keenan Komoto
Research Advisor: Tim Kowalczyk
1

2. What is a BODIPY Dye?
Core Structure of a BODIPY dye
 Class of fluorescent dyes used in a variety of
applications
 Photodynamic Therapy (PDT)1,2
 Biological Imaging3
 Organic Electronics (solar cells)4
1. Awuah, S., Polreis, J., Biradar, V., You, Y. Org. Lett., 2011, 13, 3884-3887
2. Lovell, J., Liu, T., Chen, J., Zheng, G. Chem. Rev. 2010, 110, 2839–2857
3. Ono, M., Watanabe, H., Kimura, H., Saji, H. ACS Chem. Neurosci., 2012, 3, 319–324
4. Dou, L., Liu, Y., Hong, Z., Li, G., Yang, Y. Chem. Rev. 2015, 115, 12633−12665
5. Lincoln, R., Greene, L., Krumova, K., Ding, Z., Cosa, G. J. Phys. Chem. A 2014, 118, 10622−10630
2

3. What is a BODIPY Dye?
Core Structure of a BODIPY dye
 Class of fluorescent dyes used in a variety of
applications
 Photodynamic Therapy (PDT)1,2
 Biological Imaging3
 Organic Electronics (solar cells)4
 Many derivatives and easily customizable
 Semi-recent article (2014) reported experimental
properties for 26 BODIPY derivatives.5
 Commercially available derivatives online
 Thermo Fisher Scientific
 Sigma Aldrich
 Eurogentec
1. Awuah, S., Polreis, J., Biradar, V., You, Y. Org. Lett., 2011, 13, 3884-3887
2. Lovell, J., Liu, T., Chen, J., Zheng, G. Chem. Rev. 2010, 110, 2839–2857
3. Ono, M., Watanabe, H., Kimura, H., Saji, H. ACS Chem. Neurosci., 2012, 3, 319–324
4. Dou, L., Liu, Y., Hong, Z., Li, G., Yang, Y. Chem. Rev. 2015, 115, 12633−12665
5. Lincoln, R., Greene, L., Krumova, K., Ding, Z., Cosa, G. J. Phys. Chem. A 2014, 118, 10622−10630
3

4. Desired properties
 Each application requires unique properties of the dyes for optimal performance
 PDT
 High absorption of specific light
 Appropriate triplet state energy
 High quantum yield of triplet state
 Long triplet state lifetimes
 High photostability.
 Biological Imaging
 Photostability
 Photons per switching cycle
 Number of switching cycles
 Organic Electronics
 % Efficiency
 HOMO/LUMO energies
 Reorganization energies 4

5. Desired properties
 Each application requires unique properties of the dyes for optimal performance
 PDT
 High absorption of specific light
 Appropriate triplet state energy
 High quantum yield of triplet state
 Long triplet state lifetimes
 High photostability.
 Biological Imaging
 Photostability
 Photons per switching cycle
 Number of switching cycles
 Organic Electronics
 % Efficiency
 HOMO/LUMO energies
 Reorganization energies 5

6. Photodynamic therapy
 PDT is a type of treatment which uses a photosensitizer
(molecule which produces a chemical change initiated by light),
light, and oxygen to destroy nearby cells
+ 3O2
(Light)
1O2
Cells Destroyed!!!
(Photosensitizer)
6

7. What is the problem?
So many dyes……
http://mariamascia.blogspot.com/2015_11_01_archive.html (accessed May 12th, 2016)
7

8. How do we solve this problem?
We need to:
 Find a cheaper and more efficient way to develop
appropriate photosensitizers
 Gather data about the dyes (photophysical properties)
 Find an approach which allows us to replicate that data
 Compare our approach to others
 If our approach is viable, then expand on the research already done
8

9. Where to start
The Computer
9

10. Where to start
The Computer
For a single Helium atom:
ΗΨ = 𝐸Ψ
10

11. Where to start
The Computer
For a single Helium atom:
ΗΨ = 𝐸Ψ
𝐻 = −
ħ2
2𝑚 𝑒
ࢺ 1
2
−
𝑍𝑒2
4𝜋𝜀0 𝑟1
−
ħ2
2𝑚 𝑒
ࢺ 2
2
−
𝑍𝑒2
4𝜋𝜀0 𝑟2
+
𝑒2
4𝜋𝜀0 𝑟12
 Computer makes solving these equations a lot easier
11

12. Computational chemistry
 Computational methods allow us to “easily” predict:
 Photophysical properties
 Photostability
 Excitation and Emission energies
 Excited state properties
12

13. Computational chemistry
 Computational methods allow us to “easily” predict:
 Photophysical properties
 Photostability
 Excitation and Emission energies
 Excited state properties
 Many different computational approaches to this problem
 Some more established than others
13

14. Computational chemistry
 Computational methods allow us to “easily” predict:
 Photophysical properties
 Photostability
 Excitation and Emission energies
 Excited state properties
 Many different computational approaches to this problem
 Some more established than others
 Question:
 Are we able to use our own less established method for studying these
dyes? And then can we use this method to make further
developments?
14

15. Long term goal
 Develop a protocol (utilizing our own
computational method) which selects a
photosensitizer based on desired
properties
15

16. Long term goal
 Develop a protocol (utilizing our own
computational method) which selects a
photosensitizer based on desired
properties
 Aid experimentalists designing these
compounds
 Save time and money
 Streamline process of discovery
 Not just good for PDT, but for any
situation involving organic chromophores
in their excited states
16

17. Excited states
Energy
Nuclear Coordinates (q)
λabsorbance
λemission S0
S1
1
2
3
4
17

18. Excited states
 1: Optimized ground state energy
Energy
Nuclear Coordinates (q)
1
2
3
4
λabsorbance
λemission S0
S1
18

19. Excited states
 1: Optimized ground state energy
 1 → 2: Excitation to first excited state
(absorbance)
Energy
Nuclear Coordinates (q)
λabsorbance
λemission S0
S1
1
2
3
4
19

20. Excited states
 1: Optimized ground state energy
 1 → 2: Excitation to first excited state
(absorbance)
 2 → 3: Energy minimization in excited state
Energy
Nuclear Coordinates (q)
λabsorbance
λemission S0
S1
1
2
3
4
20

21. Excited states
 1: Optimized ground state energy
 1 → 2: Excitation to first excited state
(absorbance)
 2 → 3: Energy minimization in excited state
 3 → 4: Relaxation back to ground state
(fluorescence)
Energy
Nuclear Coordinates (q)
λabsorbance
λemission S0
S1
1
2
3
4
21

22. Excited states
 1: Optimized ground state energy
 1 → 2: Excitation to first excited state
(absorbance)
 2 → 3: Energy minimization in excited state
 3 → 4: Relaxation back to ground state
(fluorescence)
“Why should I care?”
Energy
Nuclear Coordinates (q)
λabsorbance
λemission S0
S1
1
2
3
4
22

23. Excited states
 1: Optimized ground state energy
 1 → 2: Excitation to first excited state
(absorbance)
 2 → 3: Energy minimization in excited state
 3 → 4: Relaxation back to ground state
(fluorescence)
“Why should I care?”
This is the fundamentals of how our computational
methods work &…
Energy
Nuclear Coordinates (q)
λabsorbance
λemission S0
S1
1
2
3
4
23

24. Excited states
 1: Optimized ground state energy
 1 → 2: Excitation to first excited state
(absorbance)
 2 → 3: Energy minimization in excited state
 3 → 4: Relaxation back to ground state
(fluorescence)
“Why should I care?”
This is the fundamentals of how our computational
methods work &…
All the chemistry we care about: fluorescence,
photoinduced charge/electron transfer, intersystem
crossing…
All occur in the EXCITED STATE
Energy
Nuclear Coordinates (q)
λabsorbance
λemission S0
S1
1
2
3
4
24

25. Reaction pathways (Jablonski diagram)
Energy
1PS+3O2
1PS*+3O2
2PS+•+2O-•
2
3PS+3O2 1PS+1O2
Electron Transfer
Intersystem
Crossing
Excitation
Energy Transfer
Excitation
Fluorescence
Type I Reactions
Type II
Reactions
PS=Photosensitizer (BODIPY)
O2= Oxygen molecule
*=Excited state
•=Radical
25

26. Singlet oxygen generation
 Type I reactions
 Interaction between excited
photosensitizer and oxygenation
substrates
 Major products are free radicals
which lead to peroxy radical and
peroxide accumulation
 Type II reactions
 Interaction between excited
photosensitizer and oxygen
 Major product is singlet oxygen
6. Krasnovsky, A. Biochemistry (Moscow) 2007, 72 (10), 1065-1080
26

27. Singlet oxygen generation
 Type I reactions
 Interaction between excited
photosensitizer and oxygenation
substrates
 Major products are free radicals
which lead to peroxy radical and
peroxide accumulation
 Type II reactions
 Interaction between excited
photosensitizer and oxygen
 Major product is singlet oxygen
6. Krasnovsky, A. Biochemistry (Moscow) 2007, 72 (10), 1065-1080
27

28. How do we model this computationally?
Energy
Nuclear Coordinates (q)
λabsorbance
λemission
S0
S1
1
2
3
4
28

29. How do we model this computationally?
Energy
Nuclear Coordinates (q)
λabsorbance
λemission
S0
S1
1
2
3
4Density Functional
Theory (DFT)
29

30. How do we model this computationally?
Energy
Nuclear Coordinates (q)
λabsorbance
λemission
S0
S1
1
2
3
4Density Functional
Theory (DFT)
Time Dependent DFT
(TD-DFT)
30

31. How do we model this computationally?
Energy
Nuclear Coordinates (q)
λabsorbance
λemission
S0
S1
1
2
3
4Density Functional
Theory (DFT)
Time Dependent DFT
(TD-DFT)
Restricted Open-
Shell Kohn-Sham
(ROKS) method
31

32. How do we model this computationally?
Energy
Nuclear Coordinates (q)
λabsorbance
λemission
S0
S1
1
2
3
4Density Functional
Theory (DFT)
Time Dependent DFT
(TD-DFT)
Restricted Open-
Shell Kohn-Sham
(ROKS) method
1PS+3O2
1PS+1O2 Constrained
DFT (CDFT)
32

33. Preliminary Work
33

34. Gathering data
 Based on Cosa and group study5
 Optimized subset of 26 BODIPY dyes
(table 1)
5. Lincoln, R., Greene, L., Krumova, K., Ding, Z., Cosa, G. J. Phys. Chem. A 2014, 118, 10622−10630
34

35. Validating our methods
 Based on Cosa and group study5
 Optimized subset of 26 BODIPY dyes (table 1)
 Performed ROKS and TDDFT calculations and
compared to experimental data5
5. Lincoln, R., Greene, L., Krumova, K., Ding, Z., Cosa, G. J. Phys. Chem. A 2014, 118, 10622−10630
35

36. Validating our methods
 Based on Cosa and group study5
 Optimized subset of 26 BODIPY dyes (table 1)
 Performed ROKS and TDDFT calculations and
compared to each other
5. Lincoln, R., Greene, L., Krumova, K., Ding, Z., Cosa, G. J. Phys. Chem. A 2014, 118, 10622−10630
36

37. Molecular dynamics sampling
 Molecular Dynamics (MD) simulations
sample a potential energy surface (PES)
giving a conformation and it’s energy
Matplotlib.org/mpl_toolkit/mplot3d/tutorial.html
37

38. Molecular dynamics sampling
 Molecular Dynamics (MD) simulations
sample a potential energy surface (PES)
giving a conformation and it’s energy
 Performed excited state MD simulations
using ROKS theory and TD-DFT
 ~4,500 steps
 Time step of 40 a.u. (0.968 fs)
 Extracted “snapshot” geometries and
performed opposing excited state
calculations on snapshots to check non-
parallelity
Matplotlib.org/mpl_toolkit/mplot3d/tutorial.html
38

39. Non-parallelity from MD simulations
ROKS potential energy surface
TD-DFT potential energy surface
39

40. Non-parallelity from MD simulations
ΔE
ROKS potential energy surface
TD-DFT potential energy surface
40

41. MD simulations
41

42. MD simulations
42
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
-0.6 -0.54 -0.48 -0.42 -0.36 -0.3 -0.24 -0.18 -0.12 -0.06 0 0.06 0.12 0.18 0.24 0.3
%Occurrence
Energy Difference (eV)
Distribution of Energy Differences for MD Simulations
TDDFT
ROKS

43. BODIPY + O2 Interaction
 Generated a multiple systems in the form of a grid of O2
molecules surrounding a BODIPY dye
43

44. BODIPY + O2 Interaction
 Optimized each system
44

45. BODIPY + O2 Interaction
 Calculated energy of each system
45

46. BODIPY + O2 Interaction
 Calculated energy of each system
46

47. Constrained density functional theory
Energy
1PS+3O2
1PS*+3O2
2PS+•+2O-•
2
3PS+3O2 1PS+1O2
Electron Transfer
Intersystem
Crossing
Excitation
Energy Transfer
Excitation
Fluorescence
Type I Reactions
Type II
Reactions
PS=Photosensitizer (BODIPY)
O2= Oxygen molecule
*=Excited state
•=Radical
47

48. Constrained density functional theory
48

49. Constrained density functional theory
49

50. Projected thesis goals
 Compute the coupling between electronic states
 Solve for rates between electronic states (lifetimes)
 Using classical transition state theory and/or Marcus theory
 𝑅𝑎𝑡𝑒 𝑟𝑥𝑛 ∝ 𝑒−
∆𝐺‡
𝑅𝑇
 Investigate singlet oxygen generation
 Compare to experimental methods and other photosensitizers
 Study how different BODIPY derivatives compare
 How do different substituents affect the properties of the dyes?
 How do we achieve the highest singlet oxygen quantum yield?
50

51. Brief recap
 Motivation: Use computational screenings to find the best chromophore to use in PDT
 Problem: Tough to filter the vast amount of chromophores available
 Solution: Use computational modeling to gather information to help develop a protocol to
choose a chromophore
 Preliminary Work:
 Optimized BODIPY dye geometries, calculated their excitation and emission energies and
compared to experimental and to the well established TD-DFT
 Performed MD simulation on dyes to compare the ROKS method to TD-DFT (ROKS is a
viable method for these dyes)
 Studied the interaction between a BODIPY dye and an O2 molecule
 Currently using CDFT to study the conformational dependence of different electronic
states involved in singlet oxygen generation
 Project Goals:
 Find out how to tailor the dyes in order to achieve the highest 1O2 quantum yield
51

52. Acknowledgements
Faculty
Dr. Timothy Kowalczyk
Dr. Robert Berger
Dr. Steven Emory
Dr. Antos
All of my chemistry professors for
inspiring me
Research Group
Natalya, Khoa, Zoe, Linda, Adam (Viktor)
Nicole, Innes
Special Thanks
WWU Department of Chemistry
Advanced Materials Science & Engineering
Center (AMSEC)
Institute for Energy Studies
Amy Cully
Stacey Maxwell
52

53. Supplemental
Supplementary slides provided for more detailed information
53

54. Supplemental “What is a BODIPY dye?”
 Relatively insensitive to polarity and pH of their environments
 Some undesirable characteristics
• Most emit less than 600nm
• Only a handful of derivatives are water soluble
(((BODIPY Dyes and Their Derivatives: Syntheses and Spectroscopic Properties)))
Chemical Reviews 2007 107 (11), 4891-4932
Core Structure of a BODIPY dye (Proper IUPAC numbering)
1
2 4
3 5
7
6
8
54

55. Reaction pathways (Jablonski diagram)
Energy
1PS+3O2
1PS*+3O2
2PS+•+2O-•
2
3PS+3O2 1PS+1O2
Electron Transfer
Intersystem
Crossing
Excitation
Energy Transfer
Excitation
Fluorescence
Type I Reactions
Type II
Reactions
PS=Photosensitizer (BODIPY)
O2= Oxygen molecule
*=Excited state
•=Radical
55

56. Supplemental difference between ROKS
and TD-DFT
 ROKS
 The way EROKS is calculated allows for a straightforward evaluation of
gradients
𝐸𝑠
𝑅𝑂𝐾𝑆
= 2𝐸 𝑚 𝜙𝑖 − 𝐸𝑡 𝜙𝑖
 TD-DFT
 Assumes potentials can be expanded in a Taylor series around t0
56

57. How do we model this computationally?
 Density Functional Theory (DFT)7
 Built on the Hohenberg-Kohn theorems, relies on electron density and the use
of “exchange-correlation functionals”
 Gives info about the electronic ground state
 Time Dependent DFT (TD-DFT)7
 Widely used approach to calculate excited state properties
 Restricted open-shell Kohn-Sham (ROKS) theory8
 A different approach to calculate excited state properties
 Constrained DFT (CDFT)9
 Similar to DFT but allows “constraining” charge and spins on molecular
fragments
 Can constrain charges and spin states on molecules
7. Head-Gordon, M., Dreuw, A. Chem. Rev. 2005, 105, 4009-4037
8. Kowalczyk, T., Tsuchimochi, T., Chen, P., Top, L., Van Voorhis, T. The Journal of Chemical Physics, 2013, 138, 164101
9. Kaduk, B., Kowalczyk, T., Van Voorhis, T., Chem. Rev. 2012, 112, 321-370
57

58. Supplemental Lincoln Study
 Predicting redox potentials for the electronic excited states of
BODIPY dyes
 Used both computational and experimental analysis on a library
of 100 BODIPY derivatives
 Reduction potentials for a subset of 26 dyes were
experimentally measured and ranged form -1.84 to -0.52V
 Absorbance and emission Lambda(max)
 Measured in acetonitrile
58

59. Supplemental excited
and optimized
59

60. Vibrational Frequencies
Ground State ROKS excited state TD-DFT excited state
60

61. Supplemental Molecular dynamics
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116
121
126
131
136
141
146
151
156
161
166
171
176
181
186
191
196
201
206
211
216
221
226
231
236
241
246
251
256
261
266
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Snapshot
Weight
Vibrational Mode 75 (High Weight)
61

62. Supplemental Molecular dynamics
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116
121
126
131
136
141
146
151
156
161
166
171
176
181
186
191
196
201
206
211
216
221
226
231
236
241
246
251
256
261
266
-0.016
-0.014
-0.012
-0.01
-0.008
-0.006
-0.004
-0.002
0
0.002
0.004
Snapshot
Weight
Vibrational Mode 79 (Low weight)
62

63. Marcus Theory
𝑘 = 𝐴 exp
−∆𝐺∗
𝑘 𝑏 𝑇
Δ𝐺∗
=
𝜆
4
1 +
Δ𝐺0
𝜆
2
63