Brillouin microscopy is a revolutionary imaging technology that offers detailed insights into the mechanical properties of cells and tissues. The technology relies on Brillouin light scattering. When light interacts with a material, it scatters in a way that depends on the material’s mechanical properties. This scattering causes a shift in the frequency of the light, which scientists can measure to determine stiffness and viscosity. This non-invasive technique allows living tissues to be studied in great detail without needing to use chemical labels or physical contact. The field of Brillouin microscopy has seen significant advancements over the past two decades, primarily driven by the development of high-resolution optical spectrometers.

Before they can replace fossil fuels entirely, wind and solar power plants will need to provide electricity to the grid at all times of the day, and in unpredictable weather conditions. To ensure a consistent output, renewable sources can be coupled with energy-storing batteries. Ideally, these batteries can be charged up when excess energy is generated, and then release their energy when the grid’s demand for power outpaces its supply. However, even the most well-designed battery systems only have a limited storage capacity. If this limit is exceeded, any excess energy will simply be wasted. One possible solution to this problem is to combine electrical batteries with chemical energy storage.

Ice can cause serious damage to vehicles and infrastructure, including aircraft, pavements, power lines, and wind turbines. It is important to remove ice before it causes damage, but doing this manually is often expensive and energy-intensive, and sometimes even dangerous. Researchers have begun to develop so-called ‘super-hydrophobic’ coatings, which can repel incoming water droplets before they freeze. This not only prevents ice from building up; it also weakens the adhesion of ice that does freeze to the surface, allowing it be removed more easily.

From ChatGPT to Google Gemini, large language models are now increasingly important in our everyday lives. These models are part of the field of natural language processing – or ‘NLP’ – which studies how machines understand and generate human language. Most NLP systems are built using machine learning, and vast amounts of language data are used as training material. Afterwards, a successfully trained model should be able to handle new scenarios. This ability is called ‘generalization’. For large language models that generalize well, a conversation about a topic it hasn’t been trained on, such as new scientific discoveries, should not be a problem.

The energy demands required to operate our increasingly connected society is unsustainable. Data-centres, the Internet of Things, and Artificial Intelligence all create unsustainable power demands. As such, new energy-efficient technologies are needed to fulfil humanity’s computing needs into the future. Spin-based technologies could greatly reduce the energy requirements of computing. One reason that such technologies are more energy efficient is that far less energy is wasted as heat. Additionally, computation and memory can both occur within the same spin-based device. One promising spin-based technology is artificial spin ice (ASI).