Manufacturing Bits: April 25

Making strange hadrons
CERN, the European Organization for Nuclear Research, has produced and observed what it says are the world’s first sub-atomic particles called strange hadrons.

Strange hadrons are well-known sub-atomic particles with names such as Kaon, Lambda, Xi and Omega, according to CERN. Strange hadrons have never been observed until now. The observation could shed light on sub-atomic particles and the state of matter in the beginning of the universe.

Strange hadrons were produced in a particle accelerator called the Large Hadron Collider (LHC). The LHC is situated in a tunnel 100 meters underground on the Franco-Swiss border near Geneva, Switzerland.

All matter is made up of atoms. Atoms have a nucleus, which has protons and neutrons. These are surrounded by electrons. Meanwhile, protons and neutrons are made of sub-atomic particles called quarks. Quarks are bounded together by another sub-atomic particle called gluons.

Then, a hadron is a composite particle made of quarks. Meanwhile, strange hadrons and strange quarks fall into a category called strange matter. Strange hadrons consist of at least one strange quark.

Meanwhile, in the strange hadron experiment, researchers created protons in the LHC. This, in turn, created proton collisions.
During the process, researchers observed so-called strange hadrons in certain proton collisions. At the same time, strange hadrons were created.

The results also show that the production rate of these strange hadrons increases during the collision process. “We are again learning a lot about this primordial state of matter. Being able to isolate the quark-gluon-plasma-like phenomena in a smaller and simpler system, such as the collision between two protons, opens up an entirely new dimension for the study of the properties of the fundamental state that our universe emerged from,” said Federico Antinori of CERN.

As the number of particles produced in proton collisions (the blue lines) increase, the more of these so-called strange hadrons are measured (as shown by the orange to red squares in the graph) (Image: ALICE/CERN)

Speeding up catalyst R&D
The Department of Energy’s SLAC National Accelerator Laboratory and Stanford University have devised a machine learning algorithm to speed up the process of developing new catalysts.

Catalysts are substances, which increase the rate of a chemical reaction. Catalysts are used to make fuel, fertilizer and other industrial goods.

Developing a catalyst could follow a number of possible paths, a time consuming and expensive process. In response, researchers from SLAC and Stanford have taken a step towards reducing the time to find a catalyst—machine learning. More specifically, researchers utilize a discipline called density functional theory (DFT), which is a way to compute and predict the electronic structure of matter.

A subset of machine learning is called unsupervised machine learning. This makes use of artificial neural networks to crunch the data in a system. Basically, neural network algorithms crunch on data long enough to identify patterns. Over time, it learns which of those attributes are important.

For this study, SLAC and Stanford looked at syngas. Syngas is a combination of carbon monoxide and hydrogen. Researchers looked at a reaction that turns syngas into fuels and industrial chemicals. In the lab, they found the syngas flows over the surface of a rhodium catalyst. This, in turn, caused chemical reactions. The reactions produced several products, such as ethanol, methane and acetaldehyde.

Researchers were interested in how easily the catalytic reaction proceeds. “Designing a novel catalyst to speed a chemical reaction is a very daunting task,” said Thomas Bligaard, a staff scientist at the SUNCAT Center for Interface Science and Catalysis, a joint SLAC/Stanford institute. “There’s a huge amount of experimental work that normally goes into it.”

Using machine learning and DFT, researchers reduced the time to obtain the information. “It only took seconds or minutes to weed out the paths that were not interesting. In the end there were only about 10 reaction barriers that were important,” said Zachary Ulissi, a postdoctoral researcher at SUNCAT.

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