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The crack resistance of polymer materials is explained by a new model that incorporates a network of stretchable polymer chains.

Plastics and other polymer materials are often very resistant to cracking—a fact that models have not been able to accurately capture. Now a research team has developed a model of polymer fracture that explains how these materials remain intact under intense stretching. [1]. The key to the model is that it accounts for polymer chains that extend deep within the material and that can share the strain that would break a material with more localized chains. The insights could lead to the development of new structures with an enhanced resistance to shocks.

Researchers typically study fracture by cutting a small notch or crack into a material and then pulling it apart. The amount of work required to enlarge the crack is called the fracture energy. For most materials, the fracture energy is equal to the energy it takes to break the molecular bonds located along the crack tip, where the enlargement occurs. For polymers, the situation is more complex, as the molecules are long chains. In the 1960s, theorists came up with a model of polymer fracture based on the rupture of individual chains at the crack tip [2]. “The problem is that this model underestimates by a factor of 10 to 100 the energy required to fracture a polymer material,” says Xuanhe Zhao from the Massachusetts Institute of Technology.

Particles dispersed in a liquid typically jitter aimlessly in response to the random buffeting they receive from the molecules that surround them. But if the liquid is subjected to a steep temperature gradient, this random motion can become directional as the temperature gradient sets up flows that move the particles from hot regions of the liquid to colder ones. The theory of this so-called thermophoresis is actively developing, but direct observations of both the suspended particles and the liquid molecules are scant. Now Tetsuro Tsuji of Kyoto University in Japan and his colleagues have experimentally characterized the tiny surface flows that drive thermophoresis [1]. Those flows could be harnessed to move and concentrate DNA and other large biomolecules that are suspended in liquids.

For their experiments, the team glued a single polystyrene sphere, 7 µm in diameter, to the lid of a tiny transparent box. They filled the box with water laced with 500-nm-diameter fluorescent tracers. Shining a laser up through the bottom of the box, the team repeatedly drew a circle around the sphere, a process that trapped tracers located within the circle of light. The team focused a second laser, tuned to one of water’s absorption bands, at a spot 18 µm from the polystyrene sphere, locally heating the water to create a temperature gradient in the liquid and across the sphere.

Using a microscope the team observed that, after a few seconds, the tracers started flowing over the sphere’s surface, moving from the sphere’s cold end to its warmer one. From the observations, the researchers showed that this flow imparted momentum to the sphere. They also inferred the force that would have propelled the sphere away had it not been immobilized. Modeling the system under different conditions confirmed the inferences.

The onset time for “viscous fingering”—an instability that can occur at a gas–liquid boundary—depends on the compressibility of the gas, offering a way to control the behavior.

For the Gallery of Fluid Motion, researchers take the director’s chair and create videos on 3D printer patterns, frost formation, and paint swirls.

The APS Division of Fluid Dynamics has announced the 2023 winners of its annual Gallery of Fluid Motion video and poster contest. The videos below received the Milton van Dyke Award, which recognizes both videos and posters. A new traveling exhibit of past winners is currently on display at the National Academy of Sciences.

A first-principles model accounts for the wide range of critical temperatures (T_c’s) for four materials and suggests a parameter that determines T_c in any high-temperature superconductor.

Since the first high-temperature superconducting materials, known as the cuprates, were discovered in 1986, researchers have struggled to explain their properties and to find materials with even higher superconducting transition temperatures (T_c’s). One puzzle has been the cuprates’ wide variation in T_c, ranging from below 10 K to above 130 K. Now Masatoshi Imada of Waseda University in Japan and his colleagues have used first-principles calculations to determine the order parameters—which measure the density of superconducting electrons—for four cuprate materials and have predicted the T_c’s based on those order parameters [1]. The researchers have also found what they believe is the fundamental parameter that determines T_c in a given material, which they hope will lead to the development of higher-temperature superconductors.

For each material, Imada and his colleagues applied the basic principles of quantum mechanics, focusing on the planes of copper and oxygen atoms that are known to host the superconducting electrons. They used a combination of numerical techniques, including one supplemented by machine learning, and did not require any adjustable parameters.

A measurement of the charge radius of an aluminum nucleus probes the assumption that there are only three families of quarks.

In the standard model of particle physics, matter is made of elementary particles called quarks and leptons. Quarks are the heavy constituents that form, for example, protons and neutrons, whereas leptons are the light constituents, such as the electron. The six known quarks—up, down, charm, strange, top, and bottom—are split into three families. But could there be a fourth family? Answering that question would require hundreds of different measurements in particle and nuclear physics. However, not all these measurements are yet available or precise enough, and many parameter values are only inferred or extrapolated. Now Peter Plattner at CERN in Switzerland and his colleagues show how a single one of these measurements can shift our understanding of this fundamental question [1].

In the quantum-mechanical framework of the standard model, quarks can oscillate among their different flavors. The best-known example occurs in the beta decay of radioactive nuclei: a proton is transformed into a neutron (or vice versa) when one of its quarks oscillates from up to down (or down to up). The rate of beta decay depends on many factors involving both nuclear and atomic physics, but the rate at which the quarks oscillate is described by a single quantity: V_ud, the so-called matrix element of the transformation of an up quark into a down quark.

A dynamical tension model captures how cells swap places with their neighbors in epithelial tissues, explaining observed phase transitions and cellular architectures.

Epithelial tissues line the surfaces of every organ in our bodies. In the earliest stages of organ development and in wound healing, the cells that make up these simple sheets constantly rearrange themselves, exchanging positions like molecules in a liquid. But this fluidization is often hindered by the formation of multicell clusters, whose origins remain unclear. Using a dynamical structural model, Fernanda Pérez-Verdugo and Shiladitya Banerjee of Carnegie Mellon University in Pennsylvania now identify the mechanical prerequisites that lead to the formation and dissolution of these stabilized clusters [1]. They show how dynamic feedback between tension and strain controls the tissue’s material properties.

Existing models of tissue fluidity treat epithelial tissues as foam-like, polygonal networks of cells whose edges join at triple points. However, these models fail to explain the mechanisms underpinning cell neighbor exchanges. In particular, they oversimplify such exchanges by treating them as an instantaneous process, thereby avoiding the impact of exchanges that stall midprocess. One resulting discrepancy with experimental results is the absence of stable “rosette” structures that are observed in developing tissues where four or more cells meet.

Update from 02. December 2023:

LAION releases the 70 billion version of LeoLM trained with 65 billion tokens. It is based on Llama-2-70b, but according to LAION it can beat Meta’s base model — in both German and English.

“With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption,” the team writes.

On July 21, 2017, a group of dignitaries, scientists and engineers gathered in Lead, South Dakota, to hold a unique groundbreaking ceremony—at a research institution in a former gold mine, about one mile underground. The ceremony marked the beginning of construction for the Deep Underground Neutrino Experiment.

DUNE will study neutrinos, fundamental particles of matter that are abundant across the universe but difficult to catch. Over 100 trillion of them flow harmlessly and undetectably through your body each second.

Neutrinos come in three flavors—electron neutrinos, muon neutrinos and tau neutrinos—and they oscillate between those flavors as they travel. This means that a neutrino first produced as an electron neutrino can become a muon or tau neutrino.