太阳能电池设计可参照进化论

The sun's energy is virtually limitless, but harnessing its electricity with today's single-crystal silicon¹ solar cells is extremely expensive -- 10 times pricier than coal, according to some estimates. Organic solar cells -- polymer solar cells that use organic materials to absorb light and convert it into electricity -- could be a solution, but current designs suffer because polymers（聚合物） have less-than-optimal² electrical properties. Researchers at Northwestern University have now developed a new design for organic solar cells that could lead to more efficient, less expensive solar power. Instead of attempting to increase efficiency by altering the thickness of the solar cell's polymer layer -- a tactic³ that has preciously garnered⁴ mixed results -- the researchers sought to design the geometric pattern of the scattering⁵ layer to maximize the amount of time light remained trapped within the cell.

 

Using a mathematical search algorithm based on natural evolution, the researchers pinpointed⁶ a specific geometrical pattern that is optimal for capturing and holding light in thin-cell organic solar cells.

 

The resulting design exhibited a three-fold increase over the Yablonovitch Limit, a thermodynamic limit developed in the 1980s that statistically⁷ describes how long a photon（光子） can be trapped in a semiconductor⁸.

 

In the newly designed organic solar cell, light first enters a 100-nanometer-thick "scattering layer," a geometrically-patterned dielectric layer designed to maximize the amount of light transmitted into the cell. The light is then transmitted to the active layer, where it is converted into electricity.

 

"We wanted to determine the geometry for the scattering layer that would give us optimal performance," said Cheng Sun, assistant professor of mechanical engineering in Northwestern's McCormick School of Engineering and Applied⁹ Science and co-author of the paper. "But with so many possibilities, it's difficult to know where to start, so we looked to laws of natural selection to guide us."

 

The researchers employed a genetic¹⁰ algorithm, a search process that mimics¹¹ the process of natural evolution, explained Wei Chen, Wilson-Cook Professor in Engineering Design and professor of mechanical engineering at McCormick and co-investigator of the research.

 

"Due to the highly nonlinear and irregular behavior of the system, you must use an intelligent approach to find the optimal solution," Chen said. "Our approach is based on the biologically evolutionary¹² process of survival of the fittest."

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