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An electrochemical reaction that splits apart water molecules to produce oxygen is at the heart of multiple approaches aiming to produce alternative fuels for transportation. But this reaction has to be facilitated by a catalyst material, and today’s versions require the use of rare and expensive elements such as iridium, limiting the potential of such fuel production.

Now, researchers at MIT and elsewhere have developed an entirely new type of catalyst material, called a metal hydroxide-organic framework (MHOF), which is made of inexpensive and abundant components. The family of materials allows engineers to precisely tune the ’s structure and composition to the needs of a particular chemical process, and it can then match or exceed the performance of conventional, more expensive catalysts.

The findings are described today in the journal Nature Materials, in a paper by MIT postdoc Shuai Yuan, graduate student Jiayu Peng, Professor Yang Shao-Horn, Professor Yuriy Román-Leshkov, and nine others.

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Physicists searching—unsuccessfully—for today’s most favored candidate for dark matter, the axion, have been looking in the wrong place, according to a new supercomputer simulation of how axions were produced shortly after the Big Bang 13.6 billion years ago.

Using new calculational techniques and one of the world’s largest computers, Benjamin Safdi, assistant professor of physics at the University of California, Berkeley; Malte Buschmann, a postdoctoral research associate at Princeton University; and colleagues at MIT and Lawrence Berkeley National Laboratory simulated the era when axions would have been produced, approximately a billionth of a billionth of a billionth of a second after the universe came into existence and after the epoch of cosmic inflation.

The at Berkeley Lab’s National Research Scientific Computing Center (NERSC) found the ’s to be more than twice as big as theorists and experimenters have thought: between 40 and 180 microelectron volts (micro-eV, or μeV), or about one 10-billionth the mass of the electron. There are indications, Safdi said, that the mass is close to 65 μeV. Since physicists began looking for the axion 40 years ago, estimates of the mass have ranged widely, from a few μeV to 500 μeV.

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Imagine dropping a tennis ball onto a bedroom mattress. The tennis ball will bend the mattress a bit, but not permanently—pick the ball back up, and the mattress returns to its original position and strength. Scientists call this an elastic state.

On the other hand, if you drop something heavy—like a refrigerator—the force pushes the mattress into what scientists call a plastic state. The plastic state, in this sense, is not the same as the plastic milk jug in your refrigerator, but rather a permanent rearrangement of the atomic structure of a material. When you remove the refrigerator, the mattress will be compressed and, well, uncomfortable, to say the least.

But a material’s elastic-plastic shift concerns more than mattress comfort. Understanding what happens to a material at the atomic level when it transitions from elastic to plastic under high pressures could allow scientists to design stronger materials for spacecraft and nuclear fusion experiments.

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Scientists from Nanyang Technological University, Singapore (NTU Singapore), have developed a fast and low-cost imaging method that can analyze the structure of 3D-printed metal parts and offer insights into the quality of the material.

Most 3D-printed metal alloys consist of a myriad of microscopic crystals, which differ in shape, size, and atomic lattice orientation. By mapping out this information, scientists and engineers can infer the alloy’s properties, such as strength and toughness. This is similar to looking at wood grain, where wood is strongest when the grain is continuous in the same direction.

This new made-in-NTU technology could benefit, for example, the aerospace sector, where low-cost, rapid assessment of mission critical parts—turbine, fan blades and other components—could be a gamechanger for the maintenance, repair and overhaul industry.

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Radio-Frequency Pulse Enables Association of Triatomic Molecules in Ultracold 23 Na40 K + 40 K Gas.

Three-body system is already formidable in classical physics, not to mention the quantum state three-body system. But what if scientists can synthesize triatomic molecules under quantum constraints? It could serve as an appropriate platform to study three-body potential energy surface which is important but difficult to calculate.

Recently, Prof. PAN Jianwei and Prof. ZHAO Bo from the University of Science and Technology of China (USTC), collaborating with Prof. BAI Chunli from Institute of Chemistry of the Chinese Academy of Sciences, found strong evidence for association of triatomic molecules after applying a radio-frequency (rf) pulse to an ultracold mixture of 23 Na40 K and 40 K near Feshbach resonance. The work was published in the journal Nature.

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MIT chemists have discovered how the structure of the EmrE transporter changes as a compound moves through it. At left is the transporter structure at high pH. As the pH drops (right), the helices begin to tilt so that the channel is more open toward the outside of the cell, guiding the compound out. Credit: Courtesy of the researchers.

A new study sheds light on how a protein pumps toxic molecules out of bacterial cells.

MIT chemists have discovered the structure of a protein that can pump toxic molecules out of bacterial cells. Proteins similar to this one, which is found in E. coli, are believed to help bacteria become resistant to multiple antibiotics.

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A new machine-learning technique can pinpoint potential power grid failures and cascading traffic bottlenecks, in real time.

A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time.

Identifying a malfunction in the nation’s power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second.

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Sounds provide important information about how well a machine is running. ETH researchers have now developed a new machine learning method that automatically detects whether a machine is “healthy” or requires maintenance.

Whether railway wheels or generators in a power plant, whether pumps or valves—they all make sounds. For trained ears, these noises even have a meaning: devices, machines, equipment or rolling stock differently when they are functioning properly compared to when they have a defect or fault.

The sounds they make, thus, give professionals useful clues as to whether a machine is in a good—or “healthy”—condition, or whether it will soon require maintenance or urgent repair. Those who recognize in time that a machine sounds faulty can, depending on the case, prevent a costly defect and intervene before it breaks down. Consequently, the monitoring and analysis of sounds have been gaining in importance in the operation and maintenance of technical infrastructure—especially since the recording of tones, noises and acoustic signals is made comparatively cost-effective with modern microphones.

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