MIT and Stanford University researchers have developed a groundbreaking machine learning-based system that accelerates the development of optimized production methods for perovskite-based solar cells. Perovskite, a material with the potential to replace traditional silicon-based solar cells, presents numerous challenges due to its complex manufacturing requirements. With over a dozen technical variables affecting performance, achieving optimal results has been a daunting task.
This innovative system integrates data from prior experiments with insights from experienced workers, significantly improving accuracy. As a result, the perovskite batteries produced through this method achieved an impressive energy conversion efficiency of 18.5%, setting a new benchmark in the industry. The study, published in the journal Joule, was co-authored by Tonio Buonassisi from MIT, Reinhold Dauskardt from Stanford University, Zhe Liu from MIT, Nicholas Rolston from Stanford University, and others.
The team focused on a promising technique known as Rapid Spray Plasma Treatment (RSPP), which offers a scalable solution for transitioning from laboratory settings to industrial production. Unlike spin coating, a common but impractical method for large-scale manufacturing, RSPP employs a roll-to-roll process that sprays or inkjets the precursor solution onto moving sheets, enabling faster and more efficient production. Rolston noted that this method surpasses other photovoltaic technologies in terms of throughput.
Despite its promise, the process involves numerous variables—such as material composition, temperature, humidity, and nozzle distance—that interact in complex ways, making exhaustive experimentation unfeasible. Here, machine learning plays a crucial role by guiding the experimental process. The team also incorporated human expertise into the system using Bayesian optimization techniques, allowing for more nuanced predictions and adjustments.
Buonassisi emphasized the system's ability to optimize outcomes for specific conditions or objectives, beyond just power output. It holds potential for balancing multiple criteria like cost and durability. The Department of Energy, which supported the research, encourages commercialization, and the team is actively collaborating with existing perovskite manufacturers to transfer the technology.
Currently, several Chinese companies are exploring perovskite-based solar panel production, focusing on high-value applications such as building-integrated solar tiles. Despite ongoing R&D efforts, a consensus on the ideal manufacturing technology remains elusive. Liu highlighted that RSPP still has significant competitive potential.
Professor Ted Sargent of the University of Toronto, who was not involved in the study, praised the work as a significant advancement in machine learning-driven manufacturing. He noted its applicability to other industries, such as LEDs, other photovoltaic technologies, and graphene production.
Beyond the core team, the project benefited from contributions by Austin Flick and Thomas Colburn of Stanford University, along with Zekun Ren of SMART. Additional funding came from MIT's Energy Program, the National Science Foundation Graduate Research Fellowship Program, and the SMART program.
The Tig Welding Handle have several series types for you to choice ,the standard (small ),the standard (medium),the standard (large),the ribbed .Every style is design according to the human habits .The customer can choice the different hanlde based on their need and the love habits .The handle is very popular all over the world with the good quality and the best price .You will be like it .
Tig Welding Handle, Standard Tig Handle, Ribbed Tig Handle
EDAWELD COMPANY LIMITED , https://www.jsedaweld.com