Lifeboat Foundation

In recent years, several research teams worldwide have been trying to develop a new class of devices known as spintronics or spin transport electronics. These devices can encode, store, process and transmit data using the spin of electrons in certain materials.

The operation of spintronics relies on magneto-transport effects, such as giant magnetoresistance (GMR) and tunneling magnetoresistance (TMR), which enable the transport of electrons through a given material in the form of a magnetic field. A spintronic device is generally made of two conductive ferromagnetic layers separated by a non-magnetic metal layer (i.e., a spin valve) or an insulator layer (i.e., a magnetic tunnel junction).

Magneto-transport effects, which occur in a device’s spin valves and magnetic tunnel junctions, result in a relatively low resistance when the two magnetic layers are parallel and a relatively high resistance state when they are not. These effects are crucial to the functioning of many contemporary storage devices, including hard disk drives and magnetic random access memories (MRAMs).

Scientists have managed to draw at high resolution and speed, local patterns in organic semiconductor films used in optoelectronic and photonic applications. The new method enables the patterning of material characteristics and concomitant final properties, including molecular conformation, orientation, crystallinity and composition. The technique, published with open access in Nature Communications, has also been patented and industrial partners are sought for further co-development.

Bridging the gap between organic electronics and the worldwide deployed silicon electronics requires new low cost and low energy consumption fabrication methods and technologies. This work represents a key enabling technology to accelerate the use of flexible and light-weight organic electronics and photonics to the level of silicon-based devices.

The microstructure and composition of organic semiconductors need to be tuned locally in order to optimize their properties, such as charge carrier mobility, electrical conductivity and light emission; and expand their functionalities for the practical upscaling of applications such as organic transistors (OFETs) and light emitting diodes (OLEDs), organic photovoltaics (OPV), organic thermoelectric generators (OTEGs), and organic photonic structures.

🔬 The search for ancient life 🔴 Planetary evolution 👨‍🚀 Preparing for future human exploration.

There are so many reasons to study the Red Planet.

Researchers at Tel Aviv University, led by Prof. Yaniv Assaf of the School of Neurobiology, Biochemistry and Biophysics and the Sagol School of Neuroscience and Prof. Yossi Yovel of the School of Zoology, the Sagol School of Neuroscience, and the Steinhardt Museum of Natural History, conducted a first-of-its-kind study designed to investigate brain connectivity in 130 mammalian species. The intriguing results, contradicting widespread conjectures, revealed that brain connectivity levels are equal in all mammals, including humans.

“We discovered that brain connectivity —namely the efficiency of information transfer through the neural network —does not depend on either the size or structure of any specific brain,” says Prof. Assaf. “In other words, the brains of all mammals, from tiny mice through humans to large bulls and dolphins, exhibit equal connectivity, and information travels with the same efficiency within them. We also found that the brain preserves this balance via a special compensation mechanism: when connectivity between the hemispheres is high, connectivity within each hemisphere is relatively low, and vice versa.”

Participants included researchers from the Kimron Veterinary Institute in Beit Dagan, the School of Computer Science at TAU and the Technion’s Faculty of Medicine. The paper was published in Nature Neuroscience on June 8.

Carnegie Mellon today showed off new research into the world of robotic navigation. With help from the team at Facebook AI Research (FAIR), the university has designed a semantic navigation that helps robots navigate around by recognizing familiar objects.

The SemExp system, which beat out Samsung to take first place in a recent Habitat ObjectNav Challenge, utilizes machine learning to train the system to recognize objects. That goes beyond simple superficial traits, however. In the example given by CMU, the robot is able to distinguish an end table from a kitchen table, and thus extrapolate in which room it’s located. That should be more straightforward, however, with a fridge, which is both pretty distinct and is largely restricted to a singe room.

New research suggests over three dozen spots on the heated world could be ready to blow.

Researchers at DeepMind propose a new technique that automatically discovers a reinforcement learning algorithm from scratch.

A research team led by University of Alberta marine biologists has discovered a new species of sea sponge living off the coast of British Columbia, Canada.

These sponges form globally unique reefs that support deep-sea biodiversity and are found in all oceans of the world.

Cutting-edge neuroscience can identify your unique brain signature much like your thumbprint. What secrets does your “neural thumbprint” reveal about your mind’s inner workings?