Novel Next-Generation Multijunction Quantum Dot Solar Panel Designs Using Monte Carlo-Based Modeling

1. Novel Next-Generation Multijunction Quantum Dot Solar Panel DesignsUsing Monte Carlo-Based Modeling Valerie Ding

2. Introduction • Problem • The Idea • Model • Results • Conclusion Overview • Introduction • The Problem • The Idea • The Model • Data and Analysis • Conclusion

3. Introduction • Our Sun is incredibly powerful • We can harness its energy Just 45 minutes of sunlight on Earthprovides complete global energy needs for 1 year Sun to Earth: 1.74 X 1017 Watts

4. The Problem • 30+ billion tons of CO2 emissions EACH YEAR • 100 times the weight of the entire human race • Solar energy is a perfect solution: clean and infinite • Yet, solar is < 1% of world energy supply; current solar is not competitive

5. The Culprit: Cost Solar energy cost, in dollars, per watt

6. The Key Limiter of Cost: Efficiency • Conventional solar cells have average 15% and demonstrated maximum 21% efficiency. • Conventional solar cell efficiency is limited by Shockley-Queisser: theoretical maximum efficiency is 33.7%. • Spectral losses are the biggest mode of loss. More than half of solar irradiance (at right, in blue) is currently unused in electricity generation.

7. High-Efficiency Multijunction AND Low-Cost Quantum Dots High efficiency: Multijunction solar cells boost efficiency by tuning each layer for a specific spectral portion. e- e- e- e- e- Low cost:Quantum dots enable bandgap tuning without changing material. e- e- e- e-

8. Challenges Facing Multijunction Quantum Dot Solar Cells • Multijunction quantum dot solar cells (MJQDSCs) offer huge potential for solar energy. • Very little experimental data is available, as it is difficult to conduct experiments without a good understanding of complex photon-quantum dot interactions. • This work focuses on modeling and investigating interactions in order to address the most important issue: designing multijunction quantum dot solar cells to minimize intrinsic spectral losses.

9. My Integrated Methodology • NanoHUB • Cloud computing • Bulk PbS properties • NREL: • Solar spectrum on earth • Monte Carlo simulation for each MJQD SC stack • Compare Spectral Loss and Efficiency • QD Absorption Spectrum for each • Best • MJ • QDSC • Schrodinger equation solutions • Gaussian • distribution to account for QD diameter spread • Colloidal PbS quantum dot diameters • JAVA program, 10 million photons

10. Schrodinger’s equation (3-D time-independent) • h is Planck’s constant • mis particle’s mass • Ψ is particle’s wavefunction • Laplace operator (2nd-order differential eq.) Photon Absorptionby QDs • Absorption coefficient (can be quantum mechanically computed) • frepresents the Fermi-Dirac distribution • δ function is a step function = 1 when EC– EV– E = 0, otherwise 0 where

11. A sample of states: 5nm QD diameter, states 1, 5, 17, 30, 85, and 100

13. Flux and 10 Million Photons Intensity I at photon energy E is fluxF (# photons striking surface) times E Flux will change as photons travel through solar cells. For one photon in one layer, change in flux can be expressed as a ceiling function: r = random # generator p = absorption probability For a multijunction solar cell with 10 million incoming photons assumed, change in flux can be expressed as: at energy E The distribution of 10 million photons across the solar spectrum is described at right, which assumes a Gaussian distribution of photon energies due to quantum dot diameter distribution.

14. Intrinsic and Carnot Efficiency The total incoming solar power is P0: Usable power by N stacks total: Intrinsic efficiency, absorbed/incoming Thermodynamic loss is common to all solar cells. The intrinsic efficiency is multiplied by the Carnot thermal efficiency η(right) to find the theoretical maximum.

22. The Designs Using this grid size, the best order and size choices identified with Monte Carlo modeling

23. Analysis • As predicted, increasing the number of stacks leads to higher intrinsic efficiency. • The model accounted for normalization by holding the total thickness of QD layers at 18 microns. • Because the simulation involved 10 million photons, variation from run to run was small. Results were stable to three significant figures. • The model accounts for fundamental spectral loss. Additional losses such as inefficient capture and transport need to be minimized to truly reap full benefits and achieve maximum efficiency.

24. Model Advantages

25. Feedback from Experts “Very impressive” Univ of Toronto Prof. Ted Sargent: world record holder, quantum dot solar efficiency “Big success” Stanford Prof. Stacey Bent: Director, TomKat Center on Sustainable Energy “Key to future success” Univ of Notre Dame Prof. Prashant Kamat: leading expert on nanoparticles and energy

26. Conclusion • Using Monte Carlo simulation from theoretical calculations and factoring in the Carnot principle, this model predicts 2,3,5, 9 junction PbS quantum dot solar cells to have maximum 50.0%, 57.5%, 66.1% and 75.0% efficiency respectively. • A model using photon-electron interaction has been demonstrated and can be used to rapidly design and optimize MJQDSC. • This model can be calibrated using future experimental data to achieve accuracy and be used to improve MJQDSC for various materials.

27. Acknowledgements • Dr. Veronica Ledoux • Dr. Bjoern Seipel, SolarWorld USA • Mr. Andrew Merrill • Mr. Bob Sauer • Prof. Stacey Bent & Group, Stanford • Prof. Ted Sargent & Group, Toronto • Prof. Prashant Kamat & Group, Notre Dame • Dr. Christophe Ballif& Team, EPFL • Ms. Bonnie Raskin & Caroline D. Bradley Scholarship • Oregon JSHS and Judges

28. Thank You! Questions?

31. Ivanpah, Mojave Desert: World’s largest solar plant @ 392 MW, opened Feb 13, 2014 Efficiency: 18%, Cost: $2.2B, Technology: Boiler from focused sunlight