One of the most tedious and difficult tasks for university assistants in university research laboratories is long hours of microscopic examination of material samples and attempts to find monolayers.
These two-dimensional materials – less than 1 / 100,000thu Human Hair Width – They are highly sought after for use in electronics, photonics and optoelectronic devices due to their unique properties.
“Research labs hire armies of college students to do nothing but look for monolayers,” said Jaime Cardenas, an assistant professor of optics at the University of Rochester. “It’s very tedious, and if you get tired, you may miss some monolayers or you may start making misidentification.”
Even after all that work, laboratories have to re-examine materials by expensive Raman spectroscopy or atomic force microscopy.
Jesús Sánchez Juárez, a doctoral student at Cardenas Lab, has made life much easier for those college students, their research laboratories and companies that face similar difficulties in detecting monolayers.
Breakthrough technology, automated scanning equipment described in Optical materials Expresscan detect monolayers with an accuracy of 99.9 percent – which surpasses any other method to date.
For a fraction of the price. In a much shorter time. With easily accessible materials.
“One of the main goals was to develop a system with a very small budget so that students and labs could replicate these methodologies without having to invest thousands and thousands of dollars just to buy the necessary equipment,” says Sánchez Juárez, lead author of the paper.
For example, the device he created can be replicated with a cheap microscope with a 5X objective and a cheap OEM camera (original equipment manufacturer).
“We are very excited,” says Cardenas. “Jesus did a few things here that are new and different, he applied artificial intelligence in a new way to solve the big problem in using 2D materials.”
Many laboratories have attempted to eliminate the need for human scanning of costly backup characterization tests by training an artificial intelligence (AI) neural network to scan monolayers. Most labs that have tried this approach are trying to build a network from scratch, which will take a long time, says Cardenas.
Instead, Sánchez Juárez started with a publicly available neural network called AlexNet, which is already trained in object recognition.
He then developed a new process that inverts the images of the materials so that everything that was bright in the original image appeared black instead, and vice versa. Inverted images go through further processing steps. At the time, the images “do not look good at all for the human eye,” says Cardenas, “but it is easier for the computer to separate the monolayers from the substrates on which they are applied.
Bottom line: Compared to those long and tedious hours of college student scanning, Sánchez Juárez’s system can process 100 images covering 1 cm x 1 cm samples in nine minutes with almost 100 percent accuracy.
“Our demonstration paves the way for the automated production of single-layer materials for use in research and the industrial environment by significantly reducing processing time,” writes Sánchez Juárez in the article. Applications include 2D materials suitable for photodetectors, excitonic light emitting devices (LEDs), lasers, optical spin-valley current generation, single photon emissions and modulators.
Other co-authors include Marissa Granados Baez, a doctoral student at Cardenas Lab, and Alberto A. Aguilar-Lasserre, a professor at the Instituto Tecnológico de Orizaba.
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Materials provided by University of Rochester. The original was written by Bob Marcotte. Note: The content can be adjusted in terms of style and length.
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