American Physical Soc

American Physical Soc
American Physical Soc is listed in the Publishers category in Ridge, New York. Displayed below are the social networks for American Physical Soc which include a Facebook page, a Google Plus page, a Linkedin company page, a Twitter account and a YouTube channel. The activity and popularity of American Physical Soc on these social networks gives it a ZapScore of 98.

Contact information for American Physical Soc is:
1 Research Rd
Ridge, NY 11961
(631) 205-1392

"American Physical Soc" - ZapScore Report

American Physical Soc has an overall ZapScore of 98. This means that American Physical Soc has a higher ZapScore than 98% of all businesses on Zappenin. For reference, the median ZapScore for a business in Ridge, New York is 29 and in the Publishers category is 34. Learn more about ZapScore

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Social Posts for American Physical Soc

Latest research in fluid dynamics - @PhysRevFluids

Establish a collaboration with an institution through the MSI Travel Award:

Less than a week until the deadline for the Brazil-U.S. Exchange Program! Apply now:

The general theory of relativity (GR) is pretty solid, as physics theories go, but there are reasons to keep testing it. For example, maybe there is a more general theory that includes GR but also allows a unification of gravity with quantum mechanics. The latest test involves observations of stars orbiting the supermassive black hole at the center of the Galaxy. These stars experience stronger gravity than the solar system bodies involved in previous tests. From UCLA, UC Berkeley, and California Institute of Technology - Caltech.
Observations of the orbits of two stars at the center of the Milky Way constrain gravitational models involving a hypothetical fifth force.

The deadline to apply for the International Research Travel Award Program is less than a week away! Apply now:†…

RT @APSPhysicsDC: Slashing budgets for DOE Science (-17%), NSF (-11%), NIST R&D (-15%) damages innovation, the economy, & the STEM workforc…

Quantum-secure signals cannot be intercepted without the sender finding out. These signals have been sent long distances over fiber-optic lines, but beyond 100 km, you need some kind of "repeater" to boost the intensity. Now three separate experiments show that such repeaters made with solid-state devices are feasible, explains physicist Joshua Nunn of the University of Bath. Research from the University of Geneva and ICFO in Barcelona.
Crystals with rare-earth ions could lead to quantum repeaters that enable secure quantum communications over long distances.

A narrow tube will suck up liquid without any suction being applied, an effect called capillary action. It occurs because the liquid is attracted to the walls, but in a shaken bed of sand or other small particles, there is no such attraction. So why does capillary action still occur? Computer simulations show that the answer involves friction with the walls and convection associated with the so-called Brazil nut effect. From Universität zu Köln and FAU Germany.
Numerical simulations show that a previously observed capillary-like action in vibrating grain systems is due to convective motion of the grains.

After years of careful experiments, François-Marie Raoult found that the presence of a given mole fraction of any dissolved substance in any liquid caused the same fractional reduction in the vapor pressure. He later published an article on these results titled "General Law of the Vapor Pressure of Solvents" in the French Journal Competes Rendus on this day in 1887. The universality of "Raoult's Law" led to a burst of creative activity concerning liquids and solutions.
May 23, 1887: François-Marie Raoult and Raoult’s Law

Topological phases of matter are a hot topic in part because they are robust, even when there are outside fluctuations, so they may be stable platforms for new kinds of electronic devices. But finding topological states can be hard. Now researchers from Cornell University have found a way to identify these states using machine learning, explains D-Wave Systems Inc. physicist Juan Carrasquilla.
A new machine-learning algorithm based on a neural network can tell a topological phase of matter from a conventional one.