Nanostructures raise solar cell efficiency

researchers are working to develop new devices that could lead to big gains in thin-film solar cell efficiency by increasing both the number of photons thin-film solar cells absorb and the number of excited electrons the same devices collect.

Past approach

In the past, engineers have tried to add quantum wells to thin-film solar cell devices by stacking several quantum-well layers to achieve a high probability of absorption of low-energy photons.

This approach, however, can be counter productive because electron-hole pairs get stuck in the quantum wells, making it impossible for them to generate current for the device.

From the outside, the new optimized devices behave just like traditional thin-film solar cells. But inside, nanostructures enable the solar cells to circumvent an important trade-off that has stymied past attempts to incorporate quantum wells into thin-film solar cells in order to boost device efficiency.

Quantum wells can increase solar cell efficiency by raising photon absorption by lowering the energy band gap.

Thanks to nanostructures that scatter and channel light, University of California, San Diego, electrical engineers are working toward thin-film “single junction” solar cells with the potential for nearly 45 per cent sunlight-to-electricity conversion efficiencies.

“The most recent estimate of the maximum power conversion efficiency — under normal illumination conditions — that one can expect with our new thin-film solar cell approach is approximately 45 per cent.

This is a very large improvement over the 31 per cent maximum theoretical efficiency for today’s solar cells with classic p-n junctions,” said Edward Yu, the Principal Investigator.

The UC San Diego engineers are using nanoparticles to scatter incoming light into paths within the quantum well region — paths that run parallel to the p-n junction. This gives photons more time to be absorbed without having to stack the quantum wells to a thickness that makes it hard for electrons and holes to escape, according to a University of California, San Diego

ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) is both the intelligence of machines and the branch of computer science which aims to create it.

Major AI textbooks define artificial intelligence as “the study and design of intelligent agents,” where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. AI can be seen as a realization of an abstract intelligent agent (AIA) which exhibits the functional essence of intelligence. John McCarthy, who coined the term in 1956, defines it as “the science and engineering of making intelligent machines.”

Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or “strong AI”) has not yet been achieved and is a long-term goal of AI research.

AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic. AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others. Other names for the field have been proposed, such as computational intelligence, synthetic intelligence, intelligent systems, or computational rationality