Lifeboat Foundation

Water scarcity is a growing problem around the world. Desalination of seawater is an established method to produce drinkable water but comes with huge energy costs. For the first time, researchers use fluorine-based nanostructures to successfully filter salt from water. Compared to current desalination methods, these fluorous nanochannels work faster, require less pressure and less energy, and are a more effective filter.

If you’ve ever cooked with a nonstick Teflon-coated frying pan, then you’ve probably seen the way that wet ingredients slide around it easily. This happens because the key component of Teflon is fluorine, a lightweight element that is naturally water repelling, or hydrophobic. Teflon can also be used to line pipes to improve the flow of water. Such behavior caught the attention of Associate Professor Yoshimitsu Itoh from the Department of Chemistry and Biotechnology at the University of Tokyo and his team. It inspired them to explore how pipes or channels made from fluorine might operate on a very different scale, the nanoscale.

“We were curious to see how effective a fluorous nanochannel might be at selectively filtering different compounds, in particular, water and salt. And, after running some complex computer simulations, we decided it was worth the time and effort to create a working sample,” said Itoh. “There are two main ways to desalinate water currently: thermally, using heat to evaporate seawater so it condenses as pure water, or by reverse osmosis, which uses pressure to force water through a membrane that blocks salt. Both methods require a lot of energy, but our tests suggest fluorous nanochannels require little energy, and have other benefits too.”

A researcher from Skoltech has filled in the gaps connecting quantum simulators with more traditional quantum computers, discovering a new computationally universal model of quantum computation, the variational model. The paper was published as a Letter in the journal Physical Review A. The work made the Editors’ Suggestion list.

A quantum simulator is built to share properties with a target quantum system we wish to understand. Early quantum simulators were ‘dedicated’—that means they could not be programmed, tuned or adjusted and so could mimic one or very few target systems. Modern quantum simulators enable some control over their settings, offering more possibilities.

In contrast to quantum simulators, the long-promised quantum computer is a fully programmable quantum system. While building a fully programmable quantum processor remains elusive, noisy quantum processors that can execute short quantum programs and offer limited programmability are now available in leading laboratories around the world. These quantum processors are closer to the more established quantum simulators.

A team of researchers and engineers at Canadian company Xanadu Quantum Technologies Inc., working with the National Institute of Standards and Technology in the U.S., has developed a programmable, scalable photonic quantum chip that can execute multiple algorithms. In their paper published in the journal Nature, the group describes how they made their chip, its characteristics and how it can be used. Ulrik Andersen with the Technical University of Denmark has published a News & Views piece in the same journal issue outlining current research on quantum computers and the work by the team in Canada.

Scientists around the world are working to build a truly useful quantum computer that can perform calculations that would take traditional computers millions of years to carry out. To date, most such efforts have been focused on two main architectures—those based on superconducting electrical circuits and those based on trapped-ion technology. Both have their advantages and disadvantages, and both must operate in a supercooled environment, making them difficult to scale up. Receiving less attention is work using a photonics-based approach to building a quantum computer. Such an approach has been seen as less feasible because of the problems inherent in generating quantum states and also of transforming such states on demand. One big advantage photonics-based systems would have over the other two architectures is that they would not have to be chilled—they could work at room temperature.

In this new effort, the group at Xanadu has overcome some of the problems associated with photonics-based systems and created a working programmable photonic quantum chip that can execute multiple algorithms and can also be scaled up. They have named it the X8 photonic quantum processing unit. During operation, the chip is connected to what the team at Xanadu describe as a “squeezed light” source—infrared laser pulses working with microscopic resonators. This is because the new system performs continuous variable quantum computing rather than using single-photon generators.

Foresight Molecular Machines Group.
Program & apply to join: https://foresight.org/molecular-machines/

Joe Lyding.
Silicon-Based Nanotechnology: There’s Still Plenty of Room at the Bottom.
Joe Lyding is a distinguished professor in Electrical and Computer Engineering at the University of Illinios. His career includes constructing the first atomic resolution scanning tunneling microscope, discovering new industrial uses for deuterium, studying quantum size effects down to 2nm lateral graphene dimensions, and much more. His current research is focused on carbon nanoelectronics. Specifically using carbon nanoelectronics based on carbon nanotubes and graphene for future semiconducting device applications.

Continue reading “J. Lyding & L. Grill | Silicon-Based Nanotechnology & Manipulating Single Molecules on Surfaces” »

NVIDIA has published the source code of its Linux kernel modules for the R515 driver, allowing developers to provide greater integration, stability, and security for Linux distributions.

The source code has been published to NVIDIA’s GitHub repository under a dual licensing model that combines the GPL and MIT licenses, making the modules legally re-distributable.

The products supported by these drivers include all models built on the Turing and Ampere architecture, released after 2018, including the GeForce 30 and GeForce 20 series, the GTX 1,650 and 1,660, and data center-grade A series, Tesla, and Quadro RTX.

Deep reinforcement learning.

The system is so efficient because it uses deep reinforcement learning, meaning it actually adapts its processes when it is not doing well and continues improving when it makes progress.

“We have set this up as a traffic control game. The program gets a ‘reward’ when it gets a car through a junction. Every time a car has to wait or there’s a jam, there’s a negative reward. There’s actually no input from us; we simply control the reward system,” said Dr. Maria Chli, a reader in Computer Science at Aston University.

In a ghastly vision of a future cut off from sunlight, the machine overloads in the Matrix movie series turned to sleeping human bodies as sources of electricity. If they’d had sunlight, algae would undoubtedly have been the better choice.

Engineers from the University of Cambridge in the UK have run a microprocessor for more than six months using nothing more than the current generated by a common species of cyanobacteria. The method is intended to provide power for vast swarms of electronic devices.

“The growing Internet of Things needs an increasing amount of power, and we think this will have to come from systems that can generate energy, rather than simply store it like batteries,” says Christopher Howe, a biochemist and (we assume) non-mechanical human.

Logic gates are the fundamental building blocks of computers, and researchers at the University of Rochester have now developed the fastest ones ever created. By zapping graphene and gold with laser pulses, the new logic gates are a million times faster than those in existing computers, demonstrating the viability of “lightwave electronics.”

Logic gates take two inputs, compare them, and then output a signal based on the result. They can, for example, output a 1 if both incoming signals are a 1 or a 0, or if either or neither of them is a 1, among other “rules.” Billions of individual logic gates are crammed into chips to create processors, memory and other electronic components.

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Glucose is the sugar we absorb from the foods we eat. It is the fuel that powers every cell in our bodies. Could glucose also power tomorrow’s medical implants?

Engineers at MIT and the Technical University of Munich think so. They have designed a new kind of glucose fuel cell that converts glucose directly into electricity. The device is smaller than other proposed glucose fuel cells, measuring just 400 nanometers thick. The sugary power source generates about 43 microwatts per square centimeter of electricity, achieving the highest power density of any glucose fuel cell to date under ambient conditions.

Silicon chip with 30 individual glucose micro fuel cells, seen as small silver squares inside each gray rectangle. (Image: Kent Dayton)