The future of the data center has spent months in limbo.
An antiferromagnet with a zigzag magnetic structure exhibits a diode effect that has potential applications in spintronics.
In a traditional diode, current flows in one direction only, thanks to an internal charge imbalance. Researchers have now shown a diode-like effect in an antiferromagnet with a zigzag magnetic structure [1]. The underlying mechanism is different from that in traditional diodes, as the zigzag pattern creates a combined magnetic and electric field that favors current flow in one direction. The strength of the diode effect in the antiferromagnet is relatively small, but rather than exploiting the effect to make a diode for conventional circuits, the team foresees possible applications in spintronics, devices that make use of electron spins.
A typical diode is a junction between two semiconductors having different charge carriers. The charge imbalance across this junction restricts current to flow in only one direction. Diode-like behavior can, in principle, occur in a single material, but it requires that the material’s internal structure is asymmetric in a particular way. This asymmetry should produce two effects: an internal electric field and an internal magnetic field. When those two fields are perpendicular to each other, they can exert a one-way force—called a toroidal moment—on electrons moving through the material, explains Kenta Sudo from Tohoku University in Japan.
Quantum computers, systems that process information leveraging quantum mechanical effects, could soon outperform classical computers on some complex computational problems. These computers rely on qubits, units of quantum information that share states with each other via a quantum mechanical effect known as entanglement.
Qubits are highly susceptible to noise in their surroundings, which can disrupt their quantum states and lead to computation errors. Quantum engineers have thus been trying to devise effective strategies to achieve fault-tolerant quantum computation, or in other words, to correct errors that arise when quantum computers process information.
Existing approaches work either by reducing the extra number of physical qubits needed per logical qubit (i.e., space overhead) or by reducing the number of physical operations needed to perform a single logical operation (i.e., time overhead). Effectively tackling both these goals together, which would enable more scalable systems and faster computations, has so far proved challenging.
A newly designed robust mechanophore provides early warning against mechanical failure while resisting heat and UV, report researchers from Institute of Science Tokyo. They combined computational chemistry techniques with thermal and photochemical testing to show that their mechanophore scaffold, called DAANAC, stays inert under environmental stress yet emits a clear yellow signal when mechanically activated. This could pave the way for smart, self-reporting materials in construction, transportation, and electronics.
High-performance polymers, such as plastics and elastomers, are essential materials in modern life that are present in everything from airplane parts to bridges and electronics. Because sudden failures in these sectors can be extremely dangerous and costly, ensuring the safety and longevity of high-performance polymers is a critical challenge.
Since damage is often invisible at the molecular level until it is too late, scientists have been actively developing compounds known as “mechanophores.” These molecular sensors, which can be embedded into the bulk of a polymeric material, serve as an early warning system by chemically reacting to mechanical stress and producing visible light via fluorescence or other phenomena.
Researchers at Kumamoto University, in collaboration with colleagues in South Korea and Taiwan, have discovered that a unique cobalt-based molecule with metal–metal bonds can function as a spin quantum bit (spin qubit)—a fundamental unit for future quantum computers. The findings provide a new design strategy for molecular materials used in quantum information technologies.
The study is published in the journal Chemical Communications.
One day they may come for us.
Are Berserker probes hunting advanced life? Exploring the deadliest Fermi Paradox solution.
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Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already known about genes, diseases, treatments, molecular pathways and symptoms in a structured network. Until now, they have lacked detailed, individual-level information about how the affected organ actually looks and functions.
The latest research, led by postdoctoral researcher Dr. Khaled Rjoob and group leader Professor Declan O’Regan from the Computational Cardiac Imaging Group at the MRC Laboratory of Medical Sciences, has advanced this technology by adding imaging data to a knowledge graph for the first time. CardioKG provides a detailed view of the heart’s structure and function which dramatically improves the accuracy of predicting which genes are linked to disease and whether existing drugs could treat them.
The work is published in the journal Nature Cardiovascular Research.