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Category: solar power

Researchers at North Carolina State University have demonstrated a new technique that uses light to tune the optical properties of quantum dots—making the process faster, more energy-efficient and environmentally sustainable—without compromising material quality.

The findings are published in the journal Advanced Materials.

“The discovery of quantum dots earned the Nobel Prize in chemistry in 2023 because they are used in so many applications,” says Milad Abolhasani, corresponding author of a paper on the work and ALCOA Professor of Chemical and Biomolecular Engineering at NC State. “We use them in LEDs, , displays, quantum technologies and so on. To tune their , you need to tune the bandgap of quantum dots—the minimum energy required to excite an electron from a bound state to a free-moving state—since this directly determines the color of light they emit.

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This process, which cannot be understood satisfactorily by classical physics alone, occurs constantly in green plants and other photosynthetic organisms, such as photosynthetic bacteria. However, the exact mechanisms have still not been fully elucidated. Hauer and first author Erika Keil see their study as an important new basis in the effort to clarify how chlorophyll, the pigment in leaf green, works.

Applying these findings in the design of artificial photosynthesis units could help to utilize solar energy with unprecedented efficiency for electricity generation or photochemistry.

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Attosecond time-resolved experiments have revealed the increasing importance of electronic correlations in the collective plasmon response as the size of the system decreases to sub-nm scales.

The study, published in the journal Science Advances, was led by the University of Hamburg and DESY as part of a collaboration with Stanford, SLAC National Accelerator Laboratory, Ludwig-Maximilians-Universität München, Northwest Missouri State University, Politecnico di Milano and the Max Planck Institute for the Structure and Dynamics of Matter.

Plasmons are collective electronic excitations that give rise to unique effects in matter. They provide a means of achieving extreme light confinement, enabling groundbreaking applications such as efficient solar energy harvesting, ultrafine sensor technology, and enhanced photocatalysis.

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Transparent aluminum oxide (TAlOx), a real material despite its sci-fi name, is incredibly hard and resistant to scratches, making it perfect for protective coatings on electronics, optical sensors, and solar panels. On the sci-fi show Star Trek, it is even used for starship windows and spacefaring aquariums.

Current methods of making TAlOx are expensive and complicated, requiring high-powered lasers, vacuum chambers, or large vats of dangerous acids. That may change thanks to research co-authored by Filipino scientists from the Ateneo de Manila University.

Instead of immersing entire sheets of metal into acidic solutions, the researchers applied microdroplets of acidic solution onto small aluminum surfaces and applied an . Just two volts of electricity—barely more than what’s found in a single AA household flashlight battery—was all that was needed to transform the metal into glass-like TAlOx.

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Researchers are breaking new ground with halide perovskites, promising a revolution in energy-efficient technologies.

By exploring these materials at the nanoscale.

The term “nanoscale” refers to dimensions that are measured in nanometers (nm), with one nanometer equaling one-billionth of a meter. This scale encompasses sizes from approximately 1 to 100 nanometers, where unique physical, chemical, and biological properties emerge that are not present in bulk materials. At the nanoscale, materials exhibit phenomena such as quantum effects and increased surface area to volume ratios, which can significantly alter their optical, electrical, and magnetic behaviors. These characteristics make nanoscale materials highly valuable for a wide range of applications, including electronics, medicine, and materials science.

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How can machine learning help determine the best times and ways to use solar energy? This is what a recent study published in Advances in Atmospheric Sciences hopes to address as a team of researchers from the Karlsruhe Institute of Technology investigated how machine learning algorithms can be used to predict and forecast weather patterns to enable more cost-effective approaches for using solar energy. This study has the potential to help enhance renewable energy technologies by fixing errors that are often found in current weather prediction models, leading to more efficient use of solar power by predicting when weather patterns will enable the availability of the Sun for solar energy needs.

For the study, the researchers used a combination of statistical methods and machine learning algorithms to help predict the most efficient times of day that photovoltaic (PV) power generation will achieve maximum production output. Their methods used what’s known as post-processing, which involves correcting weather forecasting errors before that data enters PV models, resulting in changing PV model predictions, resulting in establishing more accurate weather forecasting from machine learning algorithms.

“One of our biggest takeaways was just how important the time of day is,” said Dr. Sebastian Lerch, who is a professor at the Karlsruhe Institute of Technology and a co-author on the study. “We saw major improvements when we trained separate models for each hour of the day or fed time directly into the algorithms.”

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Researchers have been working for decades to understand the architecture of the subatomic world. One of the knottier questions has been where the proton gets its intrinsic angular momentum, otherwise referred to as its spin.

Nuclear physicists surmise that the proton’s spin most likely comes from its constituents: quarks bound together by gluons carrying the strong force. But the details of the quark and gluon contributions have remained elusive.

Now, a new investigation from an international collaboration of physicists compiles evidence from observational results and analysis using lattice quantum chromodynamics (QCD) to present a compelling argument regarding how much of the proton’s spin comes from its gluons.

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Excitons, encountered in technologies like solar cells and TVs, are quasiparticles formed by an electron and a positively charged “hole,” moving together in a semiconductor. Created when an electron is excited to a higher energy state, excitons transfer energy without carrying a net charge. While their behavior in traditional semiconductors is well understood, excitons act differently in organic semiconductors.

Recent research led by condensed matter physicist Ivan Biaggio focuses on understanding the mechanisms behind dynamics, quantum entanglement, and dissociation in organic molecular crystals.

The paper is published in the journal Physical Review Letters.

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