Robotic probe quickly measures key properties of new materials

robotic-probe-quickly-measures-key-properties-of-new-materials

Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties.

A fully autonomous robotic system developed by MIT researchers could speed things up.

Their system utilizes a robotic probe to measure an important electrical property known as photoconductance, which is how electrically responsive a material is to the presence of light.

The researchers inject materials-science-domain knowledge from human experts into the machine-learning model that guides the robot’s decision making. This enables the robot to identify the best places to contact a material with the probe to gain the most information about its photoconductance, while a specialized planning procedure finds the fastest way to move between contact points.

During a 24-hour test, the fully autonomous robotic probe took more than 125 unique measurements per hour,

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Different anesthetics, same result: unconsciousness by shifting brainwave phase

different-anesthetics,-same-result:-unconsciousness-by-shifting-brainwave-phase

At the level of molecules and cells, ketamine and dexmedetomidine work very differently, but in the operating room they do the same exact thing: anesthetize the patient. By demonstrating how these distinct drugs achieve the same result, a new study in animals by neuroscientists at The Picower Institute for Learning and Memory at MIT identifies a potential signature of unconsciousness that is readily measurable to improve anesthesiology care.

What the two drugs have in common, the researchers discovered, is the way they push around brain waves, which are produced by the collective electrical activity of neurons. When brain waves are in phase, meaning the peaks and valleys of the waves are aligned, local groups of neurons in the brain’s cortex can share information to produce conscious cognitive functions such as attention, perception, and reasoning, says Picower Professor Earl K. Miller, senior author of the new study in Cell Reports.

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MIT physicists discover a new type of superconductor that’s also a magnet

mit-physicists-discover-a-new-type-of-superconductor-that’s-also-a-magnet

Magnets and superconductors go together like oil and water — or so scientists have thought. But a new finding by MIT physicists is challenging this century-old assumption.

In a paper appearing today in the journal Nature, the physicists report that they have discovered a “chiral superconductor” — a material that conducts electricity without resistance, and also, paradoxically, is intrinsically magnetic. What’s more, they observed this exotic superconductivity in a surprisingly ordinary material: graphite, the primary material in pencil lead.

Graphite is made from many layers of graphene — atomically thin, lattice-like sheets of carbon atoms — that are stacked together and can easily flake off when pressure is applied, as when pressing down to write on a piece of paper. A single flake of graphite can contain several million sheets of graphene, which are normally stacked such that every other layer aligns.

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With AI, researchers predict the location of virtually any protein within a human cell

with-ai,-researchers-predict-the-location-of-virtually-any-protein-within-a-human-cell

A protein located in the wrong part of a cell can contribute to several diseases, such as Alzheimer’s, cystic fibrosis, and cancer. But there are about 70,000 different proteins and protein variants in a single human cell, and since scientists can typically only test for a handful in one experiment, it is extremely costly and time-consuming to identify proteins’ locations manually.

A new generation of computational techniques seeks to streamline the process using machine-learning models that often leverage datasets containing thousands of proteins and their locations, measured across multiple cell lines. One of the largest such datasets is the Human Protein Atlas, which catalogs the subcellular behavior of over 13,000 proteins in more than 40 cell lines. But as enormous as it is, the Human Protein Atlas has only explored about 0.25 percent of all possible pairings of all proteins and cell lines within the database.

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