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IMAGE: Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field.. view more
Credit: Department of Mechanical Engineering and Mechanics/Lehigh University
Today, solar energy provides 2% of U.S. power. However, by 2050, renewables are predicted to be the most used energy source (surpassing petroleum and other liquids, natural gas, and coal) and solar will overtake wind as the leading source of renewable power. To reach that point, and to make solar power more affordable, solar technologies still require a number of breakthroughs. One is the ability to more efficiently transform photons of light from the Sun into useable energy.
2,400 participants expected to take part in over 70 sessions
The sixth international conference on nanoscience and nanotechnology was inaugurated virtually at SRM Institute of Science and Technology, Kattankulathur, on Monday.
The institute’s Department of Physics and Nanotechnology organised the biennial conference in association with Shizuoka University-Japan, National Chiao Tung University (NCTU)-Taiwan, GNS Geological and Nuclear Sciences (GNS) Science-New Zealand, University of Rome Tor Vergata-Italy, RMIT University-Australia, Tata Institute of Fundamental Research (TIFR)-India, Asian Consortium on Computational Materials Science (ACCMS), Indian Physics Association (IPA), Materials Research Society of India (MRSI), Indian Carbon Society (ICS)-India and Springer Nature.
The three-day meet will have 2,400 participants and over 70 sessions with speakers from several countries.
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IMAGE: In an example shown with Old Main Penn State s main administration building on the University Park campus the researchers algorithm takes a simple image of the material microstructure. view more
Credit: Pranav Milind Khanolkar, Penn State
Various software packages can be used to evaluate products and predict failure; however, these packages are extremely computationally intensive and take a significant amount of time to produce a solution. Quicker solutions mean less accurate results.
To combat this issue, a team of Penn State researchers studied the use of machine learning and image colorization algorithms to ease computational load, maintain accuracy, reduce time and predict strain fields for porous materials. They published their work in the