Lifeboat Foundation

Gino Elia reviews The Last Writings of Thomas S Kuhn: Incommensurability in Science edited by Bojana Mladenovic.

The EnVision Venus orbiter could help explain why the hellish planet ended up so different from our own hospitable world.

Authored by James Rickards via DailyReckoning.com,

I’ve covered a wide variety of potential crises over the years.

These include natural disasters, pandemics, social unrest and financial collapse. That’s a daunting list.

Which direction would an S-shaped lawn sprinkler rotate if it were submerged and the flow were reversed? Experiments now provide a definitive answer.

Physicist Richard Feynman wondered what would happen if an S-shaped lawn sprinkler, which rotates as water squirts out, were placed underwater and had its flow direction reversed, so that it sucked water in. Which direction would it rotate? Experiments have given conflicting answers, but now researchers have provided what appears to be a definitive resolution [1]. When sucking water in, the sprinkler reverses its rotational direction, and the motion is unsteady and much slower. The explanation involves the details of fluid flow in the sprinkler geometry.

“The answer is perfectly clear at first sight,” wrote Feynman about this puzzle in his 1985 book, Surely You’re Joking, Mr. Feynman. “The trouble was, some guy would think it was perfectly clear [that the rotation would be] one way, and another guy would think it was perfectly clear the other way.” Since then, some experiments have shown steady reverse rotation [2, 3], some showed only transient rotation [4– 6], and some situations led to unsteady rotation that changed direction [3] or proceeded in a direction that depended on the experimental geometry [4– 6].

Theorists predict that the melting of a crystalline solid happens in three stages. First, a liquid film forms on the surface. Second, defects between neighboring crystallites fluidize, causing the crystal to lose its rigidity. And third, the remaining solid parts liquefy. Researchers have observed the first and third stages of this melting process but not, until now, the second. By measuring how laser light scatters off heated crystalline tin samples, Emil Polturak and Steve Lipson of Technion–Israel Institute of Technology have detected changes in the samples’ shape that they show correspond to the melting of defects known as grain boundaries [1]. The study provides an optical tool for examining melting stages in metallic crystals.

For their demonstration, Polturak and Lipson placed a 1-mm-thick tin sample inside a sealed chamber and directed a green laser beam at its surface. They then heated the sample from 175 ^o C to 232 ^o C—the bulk melting point of tin—while taking snapshots of the light that scattered off the sample’s surface. The duo then used these snapshots to search for changes in the profile of the surface as the sample melted.

Up to 224 ^o C, pairs of sequential images were close to identical. This correlation decreased by nearly 50% at 225 ^o C—the temperature predicted for the onset of grain-boundary melting in tin. Polturak and Lipson say that once boundaries become fluid, grains can reorient themselves to change the sample’s volume and shape, which can impact its surface profile. Being able to observe and distinguish the three stages of melting could improve models of melting—a phenomenon that, despite its ubiquity, Polturak and Lipson say remains a “work in progress” in terms of understanding.

Researchers have demonstrated an unprecedentedly low-frequency superconducting “fluxonium” qubit, which could facilitate experiments that probe macroscopic quantum phenomena.

Researchers have used nuclear magnetic resonance to observe a previously unseen intermediate state in which the protein lingers for an unexpectedly long time.

Researchers have used quantum computers to solve difficult physics problems. But claims of a quantum “advantage” must wait as ever-improving algorithms boost the performance of classical computers.

Quantum computers have plenty of potential as tools for carrying out complex calculations. But exactly when their abilities will surpass those of their classical counterparts is an ongoing debate. Recently, a 127-qubit quantum computer was used to calculate the dynamics of an array of tiny magnets, or spins—a problem that would take an unfathomably long time to solve exactly with a classical computer [1]. The team behind the feat showed that their quantum computation was more accurate than nonexact classical simulations using state-of-the-art approximation methods. But these methods represented only a small handful of those available to classical-computing researchers. Now Joseph Tindall and his colleagues at the Flatiron Institute in New York show that a classical computer using an algorithm based on a so-called tensor network can produce highly accurate solutions to the spin problem with relative ease [2].

Scientists used a laser-based technique to reveal hidden quantum properties of the material Ta2NiSe5, potentially advancing the development of quantum light sources.

Certain materials have desirable properties that are hidden, and just as you would use a flashlight to see in the dark, scientists can use light to uncover these properties.

Researchers at the University of California San Diego have used an advanced optical technique to learn more about a quantum material called Ta₂NiSe₅ (TNS). Their work was published in the journal Nature Materials.