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Categories: Chemistry: Thermodynamics, Mathematics: Modeling

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Splash a few drops of water on a hot pan and if the pan is hot enough, the water will sizzle and the droplets of water seem to roll and float, hovering above the surface. The temperature at which this phenomenon, called the Leidenfrost effect, occurs is predictable, usually happening above 230 degrees Celsius. A team has now discovered a method to create the aquatic levitation at a much lower temperature.

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Closing in on the theoretical maximum efficiency, devices for turning heat into electricity are edging closer to being practical for use on the grid, according to new research.

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Researchers have engineered string-like resonators capable of vibrating longer at ambient temperature than any previously known solid-state object -- approaching what is currently only achievable near absolute zero temperatures. Their study pushes the edge of nanotechnology and machine learning to make some of the world's most sensitive mechanical sensors.

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Engineers have modeled a new way to recycle polystyrene that could become the first viable way of making the material reusable.

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Researchers have unveiled a novel method to prepare quantum operations on a given quantum computer, using a machine learning generative model to find the appropriate sequence of quantum gates to execute a quantum operation. The study marks a significant step forward in unleashing the full extent of quantum computing.

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Carbon nanotube (CNT) yarns are promising for flexible and fabric-type wearable materials that can convert waste heat into thermoelectricity. To improve the thermoelectric properties of CNT yarns, researchers dispersed CNT filaments in a highly viscous glycerol, enabling the production of CNT yarn with highly aligned bundles together with surfactants that prevent increased thermal conductivity. This innovative approach can significantly improve carbon nanotube-based thermoelectric materials, making it possible to power wearable devices using just body heat.

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Thermoelectric materials could play an important role in the clean energy transition, as they can produce electricity from sources of heat that would otherwise go to waste. Researchers report a new approach to efficiently predict when thermoelectric materials will have improved performance in converting heat into electricity.

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Large language model-based chatbots can't effectively recognize users' motivation when they are hesitant about making healthy behavior changes, but they can support those who are committed to take action, say researchers.

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Researchers used generative AI to develop a physics-informed technique to classify phase transitions in materials or physical systems that is much more efficient than existing machine-learning approaches.

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Researchers have determined that a protein prediction technology can yield accurate results in the hunt to efficiently find the best possible drug candidates for many conditions.

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Strongly interacting systems play an important role in quantum physics and quantum chemistry. Stochastic methods such as Monte Carlo simulations are a proven method for investigating such systems. However, these methods reach their limits when so-called sign oscillations occur. This problem has now been solved using the new method of wavefunction matching.

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Researchers have developed a new method that can lead to significant energy savings in buildings. The team identified 28 major heat loss regions in a multi-unit residential building with the most severe ones being at wall intersections and around windows. A potential energy savings of 25 per cent is expected if 70 per cent of the discovered regions are fixed.

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Instead of burning fossil fuels to smelt steel and cook cement, researchers in Switzerland want to use heat from the sun. The proof-of-concept study uses synthetic quartz to trap solar energy at temperatures over 1,000 C (1,832 F), demonstrating the method's potential role in providing clean energy for carbon-intensive industries.

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Researchers are using artificial intelligence to perfect the design of the vessels surrounding the super-hot plasma, optimize heating methods and maintain stable control of the reaction for increasingly long periods. A new article explains how a researcher team used machine learning to avoid magnetic perturbations, or disruptions, which destabilize fusion plasma.

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A special review examines highly-cited papers in statistical ecology. The review, which covers a century of research, details how models and concepts have evolved alongside increasing computational power.

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A research team has demonstrated that a simple stack of thermoelectric and magnetic material layers can exhibit a substantially larger transverse thermoelectric effect -- energy conversion between electric and heat currents that flow orthogonally to each other within it -- than existing magnetic materials capable of exhibiting the anomalous Nernst effect. This mechanism may be used to develop new types of thermoelectric devices useful in energy harvesting and heat flux sensing.

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Physicists and computer scientists have recently expanded the modern theory of the thermodynamics of computation. By combining approaches from statistical physics and computer science, the researchers introduce mathematical equations that reveal the minimum and maximum predicted energy cost of computational processes that depend on randomness, which is a powerful tool in modern computers.

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A research team has demonstrated that an iron-based amorphous alloy, widely used as a soft magnetic material in transformers and motors, can be transformed into a 'transverse' thermoelectric conversion material that converts electric and thermal currents in orthogonal directions, with just a short period of heat treatment. This is the first example that highlights the importance of microstructure engineering in the development of transverse thermoelectric conversion materials, and provides new design guidelines for materials development to realize environmentally friendly power generation and thermal management technologies using magnetic materials.

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Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

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Researchers present Tripath: new, deep learning models that can use 3D pathology datasets to make clinical outcome predictions. The research team imaged curated prostate cancer specimens, using two 3D high-resolution imaging techniques. The models were then trained to predict prostate cancer recurrence risk on volumetric human tissue biopsies. By comprehensively capturing 3D morphologies from the entire tissue volume, Tripath performed better than pathologists and outperformed deep learning models that rely on 2D morphology and thin tissue slices.