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Categories: Mathematics: Modeling, Physics: General

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Achieving photochemical upconversion in a solid state is a step closer to reality, thanks to a new technique that could unlock vital innovations in renewable energy, water purification and advanced healthcare.

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A team of international researchers has uncovered further clues in the quest for insights into the nature of dark matter. The key to understanding this mystery could lie with the dark photon, a theoretical massive particle that may serve as a portal between the dark sector of particles and regular matter.

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New research is challenging the scientific status quo on the limits of the nuclear chart in hot stellar environments where temperatures reach billions of degrees Celsius.

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A major new study has revealed that American teenagers are more likely than any other nationality to brag about their math ability.

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With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.

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Lab-based studies reveal how carbon atoms diffuse on the surface of interstellar ice grains to form complex organic compounds, crucial to reveal the chemical complexity in the universe.

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The era of artificial-intelligence chatbots that seem to understand and use language the way we humans do has begun. Under the hood, these chatbots use large language models, a particular kind of neural network. But a new study shows that large language models remain vulnerable to mistaking nonsense for natural language. To a team of researchers, it's a flaw that might point toward ways to improve chatbot performance and help reveal how humans process language.

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Scientists have confirmed that human brains are naturally wired to perform advanced calculations, much like a high-powered computer, to make sense of the world through a process known as Bayesian inference.

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To address the lack of suitable training data for deep-learning semantic segmentation models in urban landscaping, researchers developed a method that generates a training dataset without the need for real images or a model of an existing city. The method, which is based on procedural modelling and image-to-image techniques, enables segmentation models to achieve comparable performance under some conditions at a fraction of the cost of real dataset generation.

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Physicists begin to shed light on one of the most important questions regarding quantum spin liquids, and they do so by introducing a new phase of matter.

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Researchers have developed a new simulation model, which can predict flooding during an ongoing disaster more quickly and accurately than currently possible.

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A research team has created an aluminum-nitride device that can convert visible light into deep-ultraviolet light through the process of second harmonic generation. This work can lead to the development of practical devices that can sterilize surfaces with ultraviolet radiation while using less energy.

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Using laser light, researchers have developed the most robust method currently known to control individual qubits made of the chemical element barium. The ability to reliably control a qubit is an important achievement for realizing future functional quantum computers.

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Researchers have found a way to maintain valley polarization at room temperature using novel materials and techniques.

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A new artificial intelligence tool can help people with Parkinson's disease remotely assess the severity of their symptoms within minutes. While expert neurologists performed slightly better than the AI model, the AI model outperformed the primary care physicians with UPDRS certification.

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Researchers have used machine learning to perform error correction for quantum computers -- a crucial step for making these devices practical -- using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections.

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A team of scientists developed a new machine learning model for discovering critical-element-free permanent magnet materials based on the predicted Curie temperature of new material combinations.

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Researchers have pushed the limits of the possible in the field of atomic-scale spin-optics, creating a spin-optical laser from monolayer-integrated spin-valley microcavities without requiring magnetic fields or cryogenic temperatures.

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Beyond-silicon technology demands ultra-high-performance field-effect transistors (FETs). Transition metal dichalcogenides (TMDs) provide an ideal material platform, but the device performances such as contact resistance, on/off ratio, and mobility are often limited by the presence of interfacial residues caused by transfer procedures. We show an ideal residue-free transfer approach using polypropylene carbonate (PPC) with a negligible residue for monolayer MoS2. By incorporating bismuth semimetal contact with atomically clean monolayer MoS2-FET on h-BN substrate, we obtain an ultralow Ohmic contact resistance approaching the quantum limit and a record-high on/off ratio of ~1011 at 15 K. Such an ultraclean fabrication approach could be the ideal platform for high-performance electrical devices using large-area semiconducting TMDs.

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Scientists demonstrated a conceptual breakthrough by fabricating atomically precise quantum antidots using self-assembled single vacancies in a two-dimensional transition metal dichalcogenide.