Lifeboat Foundation

Photosynthesis is one of the most efficient natural processes for converting light energy from the sun into chemical energy vital for life on earth. Proteins called photosystems are critical to this process and are responsible for the conversion of light energy to chemical energy.

Combining one kind of these proteins, called photosystem I (PSI), with platinum nanoparticles, microscopic particles that can perform a chemical reaction that produces hydrogen — a valuable clean energy source — creates a biohybrid catalyst. That is, the light absorbed by PSI drives hydrogen production by the platinum nanoparticle.

In a recent breakthrough, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and Yale University have determined the structure of the PSI biohybrid solar fuel catalyst. Building on more than 13 years of research pioneered at Argonne, the team reports the first high-resolution view of a biohybrid structure, using an electron microscopy method called cryo-EM. With structural information in hand, this advancement opens the door for researchers to develop biohybrid solar fuel systems with improved performance, which would provide a sustainable alternative to traditional energy sources.

Continue reading “Unveiling the structure of a photosynthetic catalyst that turns light into hydrogen fuel” »

Li-ion batteries that last beyond the life cycle of the EV can be bundled into energy storage solution for renewable energy projects.

Finding a reasonable hypothesis can pose a challenge when there are thousands of possibilities. This is why Dr. Joseph Sang-II Kwon is trying to make hypotheses in a generalizable and systematic manner.

Kwon, an associate professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, published his work on blending traditional physics-based scientific models with experimental data to accurately predict hypotheses in the journal Nature Chemical Engineering.

Kwon’s research extends beyond the realm of traditional chemical engineering. By connecting physical laws with machine learning, his work could impact renewable energy, smart manufacturing, and health care, outlined in his recent paper, “Adding big data into the equation.”

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.