(1) The document discusses a project to develop a thermal fluid with enhanced heat capacity through computational discovery of thermally activated covalent bond formation. (2) The approach involves using computational screening of potential reactions to identify systems where heat is absorbed or released during covalent bond breaking and formation on heating and cooling, respectively. (3) Preliminary experimental results on computationally predicted systems show heat capacity enhancements within organic solvents and water, demonstrating the potential of the approach.

1. Computational Discovery of Thermal Fluids
with Enhanced Heat Capacity
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA

2. 2
Project goal – combine the best of thermal storage and
thermal fluids into one technology

3. 3
Project goal – combine the best of thermal storage and
thermal fluids into one technology
Try to find a
thermal storage
technology that
goes here!
Gur, I., Sawyer, K. &
Prasher, R. Searching
for a Better Thermal
Battery. Science 335,
1454–1455 (2012).

4. • Heat capacity exceeding
current technologies
– Greater than water from 0-
100°C
– Greater than Dowtherm at
high T
– Greater than ethylene glycol
mix (~3.6 J/g*K) at low T
• Good kinetics
• Stable / reversible
• Low viscosity
• High thermal conductivity
• Low cost
4
Metrics for the proposed technology

5. 5
Our approach: thermally activated covalent bond formation
heat
cool
• On heating, enhanced heat capacity due to need
to break covalent bonds (more heat needed)
• On cooling, enhanced heat capacity from
covalent bond formation (more heat released)
Note: these represent fully dissolved species in a base solution
(better viscosity than nanoparticle suspensions)

6. 6
How do the properties of the underlying reaction affect the
macroscopic properties of the thermal fluid?
Concentrationof
dissolvedspecies
ΔHrxn
ΔSrxn
Yu, P., Jain, A. & Prasher, R. S.
Enhanced Thermochemical Heat
Capacity of Liquids: Molecular to
Macroscale Modeling.
Nanoscale and Microscale
Thermophysical Engineering 0,
1–12 (2019).

7. 7
Diels–Alder reactions might be a good concept
Diels–Alder reactions were first proposed for thermal
energy storage by Lenz1,2 and Sparks and Poling3 in the late
1970s and early 1980s.
Thermodynamics:
– High reaction enthalpy (∆"#$%) and entropy (∆&#$%)
• Turning temperature is often moderate, which is what we want
Additional benefits:
– These reactions have been widely studied
– Often low-cost
– Often quite stable
1
Terry G. Lenz, Louis S. Hegedus.
Sun II, 1979
2 Terry G. Lenz, Louis S. Hegedus,
John D. Vaughn. Int. J. Energy
Res., 6(4), 1982.
3
B. G. Sparks, B. E. Poling. AIChE
J., 29(4), 1983.

8. 8
Diels–Alder reactions might be a good concept
Diels–Alder reactions were first proposed for thermal
energy storage by Lenz1,2 and Sparks and Poling3 in the late
1970s and early 1980s.
Thermodynamics:
– High reaction enthalpy (∆"#$%) and entropy (∆&#$%)
• Turning temperature is often moderate, which is what we want
Additional benefits:
– These reactions have been widely studied
– Can tune based on functionalization
– Often low-cost
– Often quite stable
1
Terry G. Lenz, Louis S. Hegedus.
Sun II, 1979
2 Terry G. Lenz, Louis S. Hegedus,
John D. Vaughn. Int. J. Energy
Res., 6(4), 1982.
3
B. G. Sparks, B. E. Poling. AIChE
J., 29(4), 1983.
But, how can we figure out the
best functionalization that
gives us the metrics we
require?

9. 9
Computational screening provides a way to do this quickly
and efficiently
Automatic density
functional theory
workflows
Supercomputing
Power
High-throughput
materials screening
We computed the thermodynamic and thermal properties:
• ∆"#$%, ∆'#$% – density functional theory (DFT)
• ()*$, +,-./, +012., – quantitative structure-property
relations (QSPR)
• 345, 67, 89 – thermodynamic model
9
DFT reaction
parameters
Concentration
estimation
Melting/boiling
point estimation
Experiment
Large number of
candidates
Validated results

10. 10
We computationally screened reactions for use in both
aqueous and organic solvents
heat
cool
Aqueous solutions
heat
cool
Organic solutions
(e.g., diphenyl ether)
not discussed in this talk
Goal – observe highest
heat capacity in a non-
cryogenic liquid!
Goal – improve upon
performance characteristics
of existing thermal fluids

11. 11
We performed a computational screening for interesting
molecules starting with the Reaxys database

12. 12
Next, we ran calculations on a subset of 53 reactions

13. 13
We did functional replacements on promising results

14. 14
We obtain 4 possible test systems, including
2-hydroxymethylfuran + maleimide
O + NH
O
O
DMF solvent
O
NH
O
O
exo-DA-product
OH
HO
2 mol/L
2-(hydroxymethyl)furan maleimide
+3 other reactions

15. Experiments on computationally-predicted systems show heat
capacity enhancement within DMF AND water (preliminary)
15
Experiments are promising and demonstrate heat capacity
enhancements
Unpublished results, provisional patent application filed
O + NH
O
O
Water
O
NH
O
O
exo-DA-product
OH
HO
1 mol/L
2-(hydroxymethyl)furan maleimide
3.0 J/g·K at 107 °C
O + NH
O
O
DMF solvent
O
NH
O
O
exo-DA-product
OH
HO
2 mol/L
2-(hydroxymethyl)furan maleimide
PRELIMINARY RESULTS

16. Kinetic modeling allows us to get
time-dependent concentrations of
reactants/products
(verified by NMR experiment)
Modifies Cp predictions such that
they are more in-line with
experimental data
16
Kinetic (transition state) modeling can fully explain
observed shift in transition temperature
Experiment
Thermodynamic model
Kinetic model
O + O
O
O
O
O
O
O
2-methylfuran maleic anhydride exo-DA-product
DFT-computed transition state
(different, related reaction)

17. 17
Reversibility experiments also in progress
Unpublished results
O + O
O
O
DMF solvent
O
O
O
O
2-methylfuran maleic anhydride exo-DA-product
2 mol/L
Note: this is the enhancement in
base specific energy due to
Diels-Alder reaction portion
1st run started 12 h after mixing (well equilibrated)
2nd through 5th with 3h interval only (less
equilibrated, due to equipment constraints)
(different, related reaction)

18. • One can enhance heat capacity through
thermally reversible covalent reactions
• Computational screening can be used to
determine the best potential reactions for this
purpose
• Experimental work verifies the proposed heat
capacity enhancements
18
Conclusions

19. 19
Acknowledgements
Peiyuan Yu
Postdoc
(now professor,
SUSTech Shenzhen)
Evan Spotte-Smith
Undergraduate
(now grad student, UC Berkeley
Jason Ma
Postdoc
Drew Lilley
Graduate student
Ravi Prasher
ALD
Funding:
LBNL LDRD
HEATER
program