Light-based quantum technologies, such as quantum communication and photonic quantum computing, require reliable sources of individual photons and, ideally, pairs of entangled photons. Semiconductor quantum dots are promising candidates for this purpose. These nanostructures have electrical conductivity between that of insulators and conductors and are capable of confining electrons and holes. This property causes them to emit light at well-defined frequencies when excited by a laser.
Making computer chips smaller is not just about better design. It also depends on a critical step in manufacturing called patterning, where nanoscale structures are carved into materials to form the circuits inside everything from smartphones to advanced sensors.
To create these patterns, engineers use a hard mask, a thin, durable material layer that protects selected regions while the exposed areas are etched away.
“As chips get smaller, the manufacturing process becomes much more demanding,” said Saptarshi Das, Penn State Ackley Professor of Engineering Science and professor of engineering science and mechanics. “The mask used to define these patterns must survive extremely harsh processing conditions. If the mask degrades, the patterns cannot be transferred reliably.”
A team of physicists has experimentally confirmed a long-predicted sequence of exotic magnetic phases in an atomically thin material. When cooled, the material forms tiny magnetic vortices before transitioning into a second ordered magnetic state—exactly as predicted by a famous theoretical model from the 1970s. Observing both phases together for the first time validates key ideas about how magnetism behaves in two dimensions. The findings could help inspire ultracompact technologies built on nanoscale magnetic control.
Engineering silicon carbide (SiC) with tailored morphologies for electronics and structural reinforcement materials has always been a costly and time-consuming affair, but scientists can now do it in a flash. A new study shows how discarded glass and silicon-rich coal waste can be turned into valuable SiC nanowires in seconds using a process known as Fluorine-Assisted Flash (FAF) Joule heating, where a quick pulse of electricity instantly heats up the reaction mixture to extremely high temperatures.
In FAF, the fluorine additives trigger the catalytic materials, such as the iron oxides found naturally in waste glass, to act as seeds that drive selective growth of one-dimensional nanowires in under a minute and with an impressive yield of 96%. When used as a reinforcement material in composites, SiC nanowires emerged as clear winners over SiC powders in providing hardness and wear resistance. The findings are published in Matter.
Physicists at the University of Colorado Boulder have demonstrated a new kind of vacuum ultraviolet laser that is 100 to 1,000 times more efficient than existing technologies of its kind. The researchers say the device could one day allow scientists to observe phenomena currently out of reach for even the most powerful microscopes—such as following fuel molecules in real time as they undergo combustion, spotting incredibly small defects in nanoelectronics and more.
The new laser might also allow for practical, ultraprecise nuclear clocks that rely on an energy transition in the nuclei of thorium atoms. These long sought-after devices could, theoretically, allow researchers to robustly track time with unprecedented precision.
The group is led by physicists Henry Kapteyn and Margaret Murnane, fellows of JILA, a joint research institute between CU Boulder and the U.S. National Institute of Standards and Technology (NIST). Jeremy Thurston, who earned his doctorate in physics from CU Boulder in 2024, spearheaded work on the new laser.
Researchers at The University of Texas at Dallas have developed a new electrolyte system that significantly boosts the energy-harvesting performance of twistrons, which are carbon nanotube yarns that generate electricity when repeatedly stretched. The findings could aid in the manufacturing of intelligent textiles, such as fabrics used to make spacesuits, that would power wearable electronic devices or sensors by harvesting energy from human motion.
In a study published in ACS Nano, the UT Dallas scientists and their collaborators reported that replacing conventional water with heavy water in the neutral electrolyte solution that bathes the twistrons significantly increased energy output from the yarns.
Normal water comprises hydrogen and oxygen atoms. In heavy water, the hydrogen is replaced with deuterium, a form of hydrogen that contains an added neutron in its nucleus.
Drug delivery researchers have vastly improved the potential of genetic therapies by overcoming the challenge of consistently getting genes and gene-editing tools where they need to be within cells. Findings of the study spearheaded by Oregon State University College of Pharmacy graduate student Antony Jozić are published in Nature Biotechnology.
When gene therapies enter a cell, they are often sent to lysosomes, the cell’s trash and recycling centers, where therapeutic genetic material is broken down before it can work. For gene therapies to succeed, they must avoid disposal and reach the part of the cell where they can function.
AI has designed candidate drugs for antibiotic-resistant infections and genetic diseases. But efforts to incorporate AI into the design of lipid nanoparticles (LNPs), the revolutionary delivery vehicles behind mRNA therapies like the COVID-19 vaccines, have been much more limited.
Designing LNPs is especially challenging: Each formulation combines multiple lipid components whose ratios influence how the particle delivers genetic instructions inside cells. Scientists still lack a clear map connecting those chemical inputs to biological outcomes.
The reason? There simply isn’t enough data.
Scientists usually study the molecular machinery that controls gene expression from the perspective of a linear, two-dimensional genome—even though DNA and its bound proteins function in three dimensions (3D). To better understand how key components of this machinery, such as super-enhancers, regulate genes in this 3D reality, scientists at St. Jude Children’s Research Hospital have developed a new algorithm called BOUQUET.
Using machine learning, BOUQUET reveals that sets of genes and their regulatory elements can interact within protein condensates, high-density membraneless droplets, in cells’ nuclei. The findings, which provide new insight into how cells regulate the genes that control their specialized identities, were published today in Nucleic Acids Research.
Cells express certain sets of genes to carry out specific functions; for example, a blood cell and a brain cell express different context-specific genes. There are 3 billion base pairs of human DNA, and the genes involved in cell identity are scattered throughout. Even more challenging, enhancers, DNA elements that activate gene expression, can be thousands of DNA bases away from their target genes.