Taking a page from Jonathan Swift’s “Gulliver’s Travels”, a team of scientists has created malleable and microscopic self-assembling particles that can serve as the next generation of building blocks in the creation of synthetic materials.
“Our work turns the tiniest of particles from inflexible, Lego-like pieces into ones that can transform themselves into a range of shapes,” explains Stefano Sacanna, an assistant professor in NYU’s Department of Chemistry and the senior author of the paper, which appears in the journal Nature Communications. “With the ability to change their contours, these particles mimic alterations that occur in nature.”
The research focused on engineering particles a micrometer in width—about 1/200th the width of a strand of human hair.
Specifically, it aimed to enhance the adaptability of colloids—small particles suspended within a fluid medium. Such everyday items such as paint, milk, gelatin, glass, and porcelain are composed of colloidal dispersions, but it’s their potential to control the flow of light that has scientists focused on creating exotic colloidal geometries.
New York University (NYU) is a private, nonsectarian American research university based in New York City.
Founded in 1831, NYU is now one of the largest private universities in the United States. Its main campus is situated at Washington Square in Greenwich Village, and it also has campuses located overseas.
NYU was the founding member of the League of World Universities, an international organization consisting of rectors and presidents from urban universities across six continents. The league and its 47 representatives gather every two years to discuss global issues in education. L. Jay Oliva formed the organization in 1991 just after he was inaugurated president of New York University.
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New York University (NYU) research articles from Innovation Toronto
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A team of scientists has developed an algorithm that captures our learning abilities, enabling computers to recognize and draw simple visual concepts that are mostly indistinguishable from those created by humans.
The work, which appears in the latest issue of the journal Science, marks a significant advance in the field—one that dramatically shortens the time it takes computers to “learn” new concepts and broadens their application to more creative tasks.
“Our results show that by reverse engineering how people think about a problem, we can develop better algorithms,” explains Brenden Lake, a Moore-Sloan Data Science Fellow at New York University and the paper’s lead author. “Moreover, this work points to promising methods to narrow the gap for other machine learning tasks.”