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A joint research team has developed an innovative quantum-classical computing approach to design photochromic materials—light-sensitive compounds—offering a powerful tool to accelerate material discovery. Their findings were published in Intelligent Computing.

Building on their previous work in the same journal, the researchers introduced a computational-basis variational quantum deflation method as the foundation of their approach.

To validate its effectiveness, the team conducted a case study in photopharmacology, screening 4,096 diarylethene derivatives. They identified five promising candidates that exhibited two critical properties: large maximum absorbance wavelengths and high oscillator strengths. These characteristics are crucial for applications such as light-controlled drug delivery in photopharmacology.

For centuries, the I-Ching, or Book of Changes, has fascinated scholars, mystics, and seekers alike. It is often considered a mere divination tool, a mystical means of interpreting the world through the casting of hexagrams.

But what if the I-Ching is something more? What if it operates as a structured probability space, exhibiting patterns and behaviors reminiscent of quantum mechanics?

Our latest research suggests that the I-Ching might not be a random oracle but instead a system governed by deep mathematical structures.

An electrospray engine applies an electric field to a conductive liquid, generating a high-speed jet of tiny droplets that can propel a spacecraft. These miniature engines are ideal for small satellites called CubeSats that are often used in academic research.

Since engines utilize more efficiently than the powerful, chemical rockets used on the launchpad, they are better suited for precise, in-orbit maneuvers. The thrust generated by an electrospray emitter is tiny, so electrospray engines typically use an array of emitters that are uniformly operated in parallel.

However, these multiplexed electrospray thrusters are typically made via expensive and time-consuming semiconductor cleanroom fabrication, which limits who can manufacture them and how the devices can be applied.

Nanoparticle researchers spend most of their time on one thing: counting and measuring nanoparticles. Each step of the way, they have to check their results. They usually do this by analyzing microscopic images of hundreds of nanoparticles packed tightly together. Counting and measuring them takes a long time, but this work is essential for completing the statistical analyses required for conducting the next, suitably optimized nanoparticle synthesis.

Alexander Wittemann is a professor of colloid chemistry at the University of Konstanz. He and his team repeat this process every day. “When I worked on my , we used a large particle counting machine for these measurements. It was like a , and, at the time, I was really happy when I could measure three hundred nanoparticles a day,” Wittemann remembers.

However, reliable statistics require thousands of measurements for each sample. Today, the increased use of computer technology means the process can move much more rapidly. At the same time, the automated methods are very prone to errors, and many measurements still need to be conducted, or at least double-checked, by the researchers themselves.

Lithium nickel oxide (LiNiO2) has emerged as a potential new material to power next-generation, longer-lasting lithium-ion batteries. Commercialization of the material, however, has stalled because it degrades after repeated charging.

University of Texas at Dallas researchers have discovered why LiNiO2 batteries break down, and they are testing a solution that could remove a key barrier to widespread use of the material. They published their findings in the journal Advanced Energy Materials.

The team plans first to manufacture LiNiO2 batteries in the lab and ultimately to work with an industry partner to commercialize the technology.