Researchers have developed a blueprint for weaving hopfions—complex, knot-like light structures—into repeating spacetime crystals. By exploiting two-color beams, they can generate ordered chains and lattices with tunable topology, potentially revolutionizing data storage, communications, and photonic processing.
One of Earth’s most unique geological formations is volcanoes, as they can be located either on land or underwater. They are even found on other planets. These formations come in all shapes and sizes, varying from shields to composites and cinder cones. When they erupt, they spew lava. As more and more of the world’s volcanoes are waking, they are also erupting pure technology. That’s right, within these unique geological formations, there are valuable elements that could revolutionize the renewable industry.
The world is gradually transitioning to renewable energy sources as alternatives to burning fossil fuels. This transition forms part of a greater goal to reduce the total greenhouse gas emissions that contribute to climate change. Unfortunately, the renewable technologies that we rely on to harness energy from renewable sources are not as environmentally friendly as we want to believe.
According to the SPIE Digital Library, renewable energy technology needs particular elements for production, and obtaining these elements has proven to be challenging. Without these elements, we cannot address other challenges that these technologies face, which are intermittency and storage. For example, solar panels and wind turbines are both dependent on specific weather conditions, which result in intermittency in power supply.
Researchers in Germany have unveiled the Metafiber, a breakthrough device that allows ultra-precise, rapid, and compact control of light focus directly within an optical fiber. Unlike traditional systems that rely on bulky moving parts, the Metafiber uses a tiny 3D nanoprinted hologram on a dual-core fiber to steer light by adjusting power between its cores. This enables seamless, continuous focus shifts over microns with excellent beam quality.
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High frequency radio waves can wirelessly carry the vast amount of data demanded by emerging technology like virtual reality, but as engineers push into the upper reaches of the radio spectrum, they are hitting walls. Literally.
Ultrahigh frequency bandwidths are easily blocked by objects, so users can lose transmissions walking between rooms or even passing a bookcase.
Now, researchers at Princeton Engineering have developed a machine-learning system that could allow ultrahigh frequency transmissions to dodge those obstacles. In an article in Nature Communications, the researchers unveiled a system that shapes transmissions to avoid obstacles coupled with a neural network that can rapidly adjust to a complex and dynamic environment.
Over the past decades, engineers have introduced a wide range of computing systems inspired by the human brain or designed to emulate some of its functions. These include devices that artificially reproduce the behavior of brain cells (e.g., neurons), by processing and transmitting signals in the form of electrical pulses.
Most neuron-inspired devices developed so far use either electrons or photons to process and transmit information, rather than integrating the two. This is because photonic and electronic systems typically have very different architectures, and converting the signals they rely on can be challenging and lead to energy losses.
Researchers at Stanford University, Sandia National Laboratories, and Purdue University recently developed new electro–optical devices that can mimic neuron-like electrical pulses and simultaneously emit oscillating light. These devices, referred to as electro-optical Mott neurons, were introduced in a paper published in Nature Electronics.
Quantum technologies, devices that work by leveraging quantum mechanical effects, could outperform classical technologies in some fields and settings. The so-called spin (i.e., intrinsic angular momentum) carried by quantum particles is central to the functioning of quantum systems, as it can store quantum information.
To reliably share quantum information across a network, however, spins need to be linked to photons (i.e., particles of light). For decades, engineers and quantum physicists have thus been trying to devise approaches to interface spins and photons.
One strategy to achieve this entails the use of quantum dots, nanoscale semiconductor structures that can trap electrons or holes in distinct energy levels. When placed in carefully engineered optical resonators known as microcavities, these structures can generate individual photons. Nonetheless, ensuring that the coherence of spins is not disrupted by magnetic noise originating from nearby nuclear spins and thus facilitating the preservation of quantum information over time has so far proved challenging.
Batteries, like humans, require medicine to function at their best. In battery technology, this medicine comes in the form of electrolyte additives, which enhance performance by forming stable interfaces, lowering resistance and boosting energy capacity, resulting in improved efficiency and longevity.
Finding the right electrolyte additive for a battery is much like prescribing the right medicine. With hundreds of possibilities to consider, identifying the best additive for each battery is a challenge due to the vast number of possibilities and the time-consuming nature of traditional experimental methods.
Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are using machine learning models to analyze known electrolyte additives and predict combinations that could improve battery performance. They trained models to forecast key battery metrics, like resistance and energy capacity, and applied these models to suggest new additive combinations for testing.