The French company says its Inelio thermal battery can store solar power in the form of heat for heating and cooling applications, as well as for producing domestic hot water, while maximizing self-consumption. It can reportedly provide a hot water temperature of up to 65 C.
The sun, the essential engine that sustains life on Earth, generates its tremendous energy through the process of nuclear fusion. At the same time, it releases a continuous stream of neutrinos—particles that serve as messengers of its internal dynamics. Although modern neutrino detectors unveil the sun’s present behavior, significant questions linger about its stability over periods of millions of years—a timeframe that spans human evolution and significant climate changes.
Finding answers to this is the goal of the LORandite EXperiment (LOREX) that requires a precise knowledge of the solar neutrino cross section on thallium. This information has now been provided by an international collaboration of scientists using the unique facilities at GSI/FAIR’s Experimental Storage Ring ESR in Darmstadt to obtain an essential measurement that will help to understand the long-term stability of the sun. The results of the measurements have been published in the journal Physical Review Letters.
LOREX is the only long-time geochemical solar neutrino experiment still actively pursued. Proposed in the 1980s, it aims to measure solar neutrino flux averaged over a remarkable four million years, corresponding to the geological age of the lorandite ore.
How can tree placement impact urban temperatures? This is what a recent study published in Communications Earth & Environment hopes to address as an international team of researchers investigated how tree planting locations plays a vital role in mitigating the effects of climate change on urban environments. This study holds the potential to help researchers, climate scientists, the public, and city planners have the necessary tools and resources to combat climate change while still providing adequate ecology for their surroundings.
For the study, the researchers conducted a literature review on 182 past studies discussing how tree planting can decrease temperatures in urban environments, including 110 cities or regions worldwide and 17 climates, with the goal of quantifying this temperature decrease on a global scale. In the end, the team found that 83 percent of the cities used in the study experienced average monthly peak temperatures below 26 degrees Celsius (79 degrees Fahrenheit) while also noting that tree planting contributes to a decrease of 12 degrees Celsius (54 degrees Fahrenheit) in pedestrian-level temperatures.
“Our study provides context-specific greening guidelines for urban planners to more effectively harness tree cooling in the face of global warming,” said Dr. Ronita Bardhan, who is an Associate Professor of Sustainable Built Environment at the University of Cambridge and a co-author on the study. “Our results emphasize that urban planners not only need to give cities more green spaces, they need to plant the right mix of trees in optimal positions to maximize cooling benefits.”
A German firm tests NASA-developed nickel-hydrogen batteries in a renewable energy project for efficient, long-lasting storage.
Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional atomic structures of highly complex materials.
This research highlights LLNL’s efforts in advancing machine learning for materials science research and supporting the Lab’s mission to develop innovative technological solutions for energy and sustainability.
The study, recently published in Machine Learning: Science and Technology, represents a potential leap forward in the application of AI for materials characterization and inverse design.
Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional (3D) atomic structures of highly complex materials.
This research highlights LLNL’s efforts in advancing machine learning for materials science research and supporting the Lab’s mission to develop innovative technological solutions for energy and sustainability.
The study, recently published in Machine Learning: Science and Technology, represents a potential leap forward in the application of AI for materials characterization and inverse design.
Forest ecosystems of the future will have to cope with very different conditions to those of today. For this reason, researchers at the Technical University of Munich (TUM) state that a strategic approach to forest management is crucial. To this end, the research team has developed iLand: a simulation model that can compute long-term developments of large forest landscapes, right down to the individual tree—including disturbances from bark beetles to wildfires.
Charred tree trunks and blackened soil are typical of the desolation that a forest fire leaves behind. Inevitably, the question arises whether it will be possible to restore a green natural landscape. According to Rupert Seidl, Professor of Ecosystem Dynamics and Forest Management, this is possible, but the “how” decides how much the new forest will benefit the climate, nature and people.
“Today’s forest ecosystems are not particularly well adapted to future climate conditions,” says Seidl. “Over the next decades they will presumably come under increasing pressure from water shortage and insect pests, and may even die off. This is why it makes sense to use measures such as the reforestation of disturbed areas to strategically select tree species and take future developments into consideration.”
Water electrolysis is a cornerstone of global sustainable and renewable energy systems, facilitating the production of hydrogen fuel. This clean and versatile energy carrier can be utilized in various applications, such as chemical CO2 conversion, and electricity generation. Utilizing renewable energy sources such as solar and wind to power the electrolysis process may help reduce carbon emissions and promote the transition to a low-carbon economy.
The development of efficient and stable anode materials for the Oxygen Evolution Reaction (OER) is essential for advancing Proton Exchange Membrane (PEM) water electrolysis technology. OER is a key electrochemical reaction that generates oxygen gas (O₂) from water (H₂O) or hydroxide ions (OH⁻) during water splitting.
This seemingly simple reaction is crucial in energy conversion technologies like water electrolysis as it is hard to efficiently realize and a concurrent process to the wanted hydrogen production. Iridium (Ir)-based materials, particularly amorphous hydrous iridium oxide (am-hydr-IrOx), are at the forefront of this research due to their high activity. However, their application is limited by high dissolution rates of the precious iridium.
Researchers at the University of Reading and University College London have developed a new artificial intelligence model that can predict how atoms arrange themselves in crystal structures. Called CrystaLLM, the technology works similarly to AI chatbots, by learning the “language” of crystals by studying millions of existing crystal structures. It could lead to faster discovery of new materials for everything from solar panels to computer chips.
Wuhan University-led research is reporting the development of a revivable self-assembled supramolecular biomass fibrous framework (a novel foam filter) that efficiently removes microplastics from complex aquatic environments.
Plastic waste is a growing global concern due to significant levels of microplastic pollution circulating in soil and waterways and accumulating in the environment, food webs and human tissues. There are no conventional methods for removing microplastics, and developing strategies to handle diverse particle sizes and chemistries is an engineering challenge.
Researchers have been looking for affordable, sustainable materials capable of universal microplastic adsorption. Most existing approaches involve expensive or difficult-to-recover adsorbents, fail under certain environmental conditions, or only target a narrow range of microplastic types.
