Researchers study the transition from bound states in the continuum (BICs) to quasi-BIC caused by out-of-plane asymmetry and illustrate how quality factors of BIC resonances are valuable tools for precise chip patterning accuracy.
The handedness or chirality of a golf club, a baseball glove, or certain crystal lattices is plain to see: Their structures are such that one cannot be overlaid on its mirror image. Now Takayuki Ishitobi of the Japan Atomic Energy Agency and Kazumasa Hattori of Tokyo Metropolitan University have discovered that a crystal whose atomic structure is achiral can still host a chiral electronic state, which they dub purely electronic chirality (PEC) [1].
Four years ago, theorists found that the chirality of a crystalline structure can be quantified with a single number G0, which is given by the inner product of polar and axial vectors. The polar one is the electric dipole moment. The axial one is the electric toroidal dipole, which quantifies the geometric relationship between the electrons’ spin and orbital axes, and which is present in a few crystals with the requisite intricate arrangement of orbitals. Ishitobi and Hattori sought crystals whose atomic structures were achiral, but in which electronic interactions could induce an electric toroidal dipole and, therefore, a nonzero G0.
In some crystals, the conduction electrons occupy 2D planes. Ishitobi and Hattori realized that, if such a crystal also possesses atoms with electric quadrupole moments, the internal electric field could couple these quadrupoles to the electric toroidal dipole. A PEC would arise if the electric quadrupole has a specific arrangement and if the crystal has a certain lattice structure. From their calculations, the researchers determined that the intermetallic compound uranium rhodium stannide ticks all the boxes. They also found that the adoption of PEC by this material’s electrons could account for an unexplained phase transition at a temperature of 54 K.
The company is already the world’s largest battery maker, supplying cells to major automakers. With this latest development, the battery giant is positioning itself at the center of the race to deliver gasoline-like convenience without sacrificing durability.
The core challenge engineers set out to address was whether an EV battery could withstand repeated ultra-fast charging without rapid degradation. A 5C charge rate means an 80-kilowatt-hour battery pack could theoretically accept around 400 kilowatts of power. That level of charging can refill a battery in roughly 12 minutes, similar to a typical gas stop.
Fast charging has long been associated with faster wear. The engineers tested whether the chemistry could handle that stress over time. According to the company, the answer was yes. Under standard conditions at 68°F, the battery retained at least 80 percent of its original capacity after 3,000 full charge-and-discharge cycles.
When AI systems fail, will they fail by systematically pursuing the wrong goals, or by being a hot mess? We decompose the errors of frontier reasoning models into bias (systematic) and variance (incoherent) components and find that, as tasks get harder and reasoning gets longer, model failures become increasingly dominated by incoherence rather than systematic misalignment. This suggests that future AI failures may look more like industrial accidents than coherent pursuit of a goal we did not train them to pursue.
Engineers have long battled a problem that can cause loud, damaging oscillations inside gas turbines and aircraft engines: combustion instability. These unwanted pressure fluctuations create vibrations so intense that they can cause fatal structural damage to combustor walls, posing a serious threat in many applications. Combustion instability occurs when acoustic waves, heat release, and flow patterns interact in a strong feedback loop, amplifying each other until the entire system becomes unstable.
The complex interaction has made it difficult to predict when and where dangerous oscillations will emerge. This challenge has motivated researchers to seek new analytical frameworks that can capture the key driving regions of combustion instability.
Now, a research team led by Professors Hiroshi Gotoda from Tokyo University of Science and Ryoichi Kurose from Kyoto University, Japan, has developed an innovative approach using network science to understand and suppress combustion instability. Their paper, published in the journal Physical Review Applied on July 1, 2025, applies complex network analysis to spray combustion instability in a backward-facing step combustor.
A high-severity security flaw has been disclosed in OpenClaw (formerly referred to as Clawdbot and Moltbot) that could allow remote code execution (RCE) through a crafted malicious link.
The issue, which is tracked as CVE-2026–25253 (CVSS score: 8.8), has been addressed in version 2026.1.29 released on January 30, 2026. It has been described as a token exfiltration vulnerability that leads to full gateway compromise.
“The Control UI trusts gatewayUrl from the query string without validation and auto-connects on load, sending the stored gateway token in the WebSocket connect payload,” OpenClaw’s creator and maintainer Peter Steinberger said in an advisory.
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Next, the study’s authors will examine whether more ZEVs are associated with fewer asthma-related hospitalizations and emergency room visits.
Their work adds to the extensive research on whether EVs are better for the planet long-term than their gas-powered counterparts. Despite imperfections such as mining, the findings are clear on that front. The USC team is showing that when it comes to the air we breathe and public health, the benefits of EVs are undeniable.
“These findings show that cleaner air isn’t just a theory—it’s already happening in communities across California,” declared Sandrah Eckel, the study’s lead author.
On a foggy morning off the coast of Finland, the sea looks perfectly ordinary. A few fishing boats, a cargo ship on the horizon, the low hum of engines and gulls complaining overhead. Yet under that flat grey surface, survey vessels are tracing invisible lines, mapping the seabed for something that sounds like science fiction: a high‑speed train that will dive under the Baltic and emerge on another continent.
On deck, an engineer in a neon jacket points to the radar screen like someone tracing the outline of a new city. He talks about boring through rock, laying tracks where only fish and submarines have passed. His words hang in the cold air.
Soon, a train will cross here faster than most people cross a city.
As of January 2026, the global race for semiconductor supremacy has reached a fever pitch, centered on a massive, truck-sized machine that costs more than a fleet of private jets. ASML (NASDAQ: ASML) has officially transitioned its “High-NA” (High Numerical Aperture) Extreme Ultraviolet (EUV) lithography systems into high-volume manufacturing, marking the most significant shift in silicon fabrication in over a decade. While the industry grapples with the staggering $350 million to $400 million price tag per unit, Intel (NASDAQ: INTC) has emerged as the aggressive vanguard, betting its entire “IDM 2.0” turnaround strategy on being the first to operationalize these tools for the next generation of “Angstrom-class” processors.
The transition to High-NA EUV is not merely a technical upgrade; it is a fundamental reconfiguration of how the world’s most advanced AI chips are built. By enabling higher-resolution circuitry, these machines allow for the creation of transistors so small they are measured in Angstroms (tenths of a nanometer). For an industry currently hitting the physical limits of traditional EUV, this development is the “make or break” moment for the continuation of Moore’s Law and the sustained growth of generative AI compute.