Toggle light / dark theme

Light is notoriously fast. Its speed is crucial for rapid information exchange, but as light zips through materials, its chances of interacting and exciting atoms and molecules can become very small. If scientists can put the brakes on light particles, or photons, it would open the door to a host of new technology applications.

Now, in a paper published on Aug. 17, in Nature Nanotechnology, Stanford scientists demonstrate a new approach to slow light significantly, much like an echo chamber holds onto sound, and to direct it at will. Researchers in the lab of Jennifer Dionne, associate professor of materials science and engineering at Stanford, structured ultrathin silicon chips into nanoscale bars to resonantly trap light and then release or redirect it later. These “high-quality-factor” or “high-Q” resonators could lead to novel ways of manipulating and using light, including new applications for quantum computing, virtual reality and augmented reality; light-based WiFi; and even the detection of viruses like SARS-CoV-2.

“We’re essentially trying to trap light in a tiny box that still allows the light to come and go from many different directions,” said postdoctoral fellow Mark Lawrence, who is also lead author of the paper. “It’s easy to trap light in a box with many sides, but not so easy if the sides are transparent—as is the case with many Silicon-based applications.”

Hong Kong’s biggest container port facility Kwai Tsing Container Terminals has been linked to around 65 coronavirus infection cases, Bloomberg reported.

According to local media outlets, the virus was most probably picked up from communal resting facilities and dormitories, where social distancing measures are difficult to implement as dozens of workers can be confined to the same place at the same time.

Most of the workers were linked to Wang Kee Port Operation Services Ltd. and were predominantly asymptomatic. Around 100 workers have reportedly been quarantined.

Combing through historical seismic data, researchers using a machine learning model have unearthed distinct statistical features marking the formative stage of slow-slip ruptures in the earth’s crust months before tremor or GPS data detected a slip in the tectonic plates. Given the similarity between slow-slip events and classic earthquakes, these distinct signatures may help geophysicists understand the timing of the devastating faster quakes as well.

“The found that, close to the end of the slow slip cycle, a snapshot of the data is imprinted with fundamental information regarding the upcoming failure of the system,” said Claudia Hulbert, a computational geophysicist at ENS and the Los Alamos National Laboratory and lead author of the study, published today in Nature Communications. “Our results suggest that slow-slip rupture may well be predictable, and because slow slip events have a lot in common with earthquakes, may provide an easier way to study the fundamental physics of earth rupture.”

Slow-slip events are earthquakes that gently rattle the ground for days, months, or even years, do not radiate large-amplitude seismic waves, and often go unnoticed by the average person. The classic quakes most people are familiar with rupture the ground in minutes. In a given area they also happen less frequently, making the bigger quakes harder to study with the data-hungry machine learning techniques.

Pleased to welcome author and NPR commentator Adam Frank, a professor of Physics and Astronomy at the University of Rochester in upstate New York. He is author of the 2018 WW Norton title “Light of the Stars: Alien Worlds and the Fate of the Earth.” Frank and colleagues just recently received a NASA grant to hunt for the signatures of advanced alien technology within our galaxy. Stay tuned.