Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: materials – Page 7

Skyrmions as Active Matter

Pairs of skyrmions—tiny whirlpools that emerge in some magnetic materials—might be able to self-propel, a behavior reminiscent of that of active-matter systems such as motile bacteria.

In nature, the collective motion of birds and fish can generate impressive dynamics and unique structures, as seen in flocks of starlings and shoals of sardines. The science of active matter studies such complex behaviors across a wide range of scales and origins [1], and it has attracted growing interest over the past three decades. Active matter encompasses not only living things but also inanimate objects. Examples include active colloids [2] and active liquid crystals [3] that are able to self-propel—that is, move by themselves powered by internal energy sources. Now Clécio de Souza Silva and colleagues at the Federal University of Pernambuco in Brazil have suggested an intriguing addition to the active-matter catalog: coupled pairs of skyrmions, whirlpool-like spin arrangements that emerge in certain magnetic materials.

MXene as a frame for 2D water films shows new properties

Water still has unknown sides. When water is forced into two dimensions by enclosing it in appropriate materials, new properties, phase transitions, and structures emerge. MXenes as a class of materials offer a unique platform for exploring these types of phenomena. MXenes consist of transition metal carbides and nitrides with a layered structure whose surfaces can help them absorb water easily. The water forms an extremely thin film between the individual layers.

A team led by Dr. Tristan Petit, HZB, and Yury Gogotsi, Drexel University, has investigated a series of MXene samples containing enclosed water and different ions at BESSY II using various analytical methods. The work is published in the journal Nature Communications.

X-ray revealed the formation of amorphous ice clusters in the enclosed water, which increases the distance between the MXene layers. The previously metallic MXene film then becomes a semiconductor.

A shape-changing antenna for more versatile sensing and communication

MIT researchers developed a method to design and fabricate reconfigurable antennas with adjustable frequency ranges. Users can adjust the frequency by squeezing, bending, or stretching the material, making the antenna more versatile for sensing and communication than traditional static antennas.

Unified theory may reveal more superconducting materials

Electricity flows through wires to deliver power, but it loses energy as it moves, delivering less than it started with. But that energy loss isn’t a given. Scientists at Penn State have found a new way to identify types of materials known as superconductors that allow power to travel without any resistance, meaning no energy is lost.

Novel method upgrades liquid crystals with better recall

Researchers have developed a novel way for liquid crystals to retain information about their movement. Using this method could advance technologies like memory devices and sensors, as well as pave the way to future soft materials that are both smart and flexible.

Liquid crystals, which are used in liquid crystal display (LCD) screens for TVs and phones, contain molecules that mimic the properties of both liquids and solids, giving them . While soft materials like liquids, gels and polymers have been widely used for their easy-to-process structures and lightweight properties, they tend to deform easily and often require replacement.

Everyday materials are made of molecules that align themselves in preferred directions. But liquid crystals could become much more useful if their molecules are all facing in one direction—obtaining what is called polar order.

Inspired by Death Valley, researchers mimic a mystery of nature to make ice move on its own

In Associate Professor Jonathan Boreyko’s Nature-Inspired Fluids and Interfaces Lab, Ph.D. student Jack Tapocik watched a disk-shaped chunk of ice resting on an engineered metal surface. As the ice melted, the water formed a puddle beneath.

Even after many seconds of melting, the ice disk remained adhered to the engineered surface. At first, Tapocik was tempted to conclude that nothing would happen, but he waited. His patience paid off. After a minute, the ice slingshot across the metal plate he designed, gliding along as if it was propelled supernaturally.

The results are important because they have a host of potential applications. The methods team members developed lay the foundation for rapid defrosting and novel methods of energy harvesting. Their work has been published in ACS Applied Materials & Interfaces.

View a PDF of the paper titled Characterizing Deep Research: A Benchmark and Formal Definition, by Abhinav Java and 8 other authors

Information tasks such as writing surveys or analytical reports require complex search and reasoning, and have recently been grouped under the umbrella of \textit{deep research} — a term also adopted by recent models targeting these capabilities. Despite growing interest, the scope of the deep research task remains underdefined and its distinction from other reasoning-intensive problems is poorly understood. In this paper, we propose a formal characterization of the deep research (DR) task and introduce a benchmark to evaluate the performance of DR systems. We argue that the core defining feature of deep research is not the production of lengthy report-style outputs, but rather the high fan-out over concepts required during the search process, i.e., broad and reasoning-intensive exploration. To enable objective evaluation, we define DR using an intermediate output representation that encodes key claims uncovered during search-separating the reasoning challenge from surface-level report generation. Based on this formulation, we propose a diverse, challenging benchmark LiveDRBench with 100 challenging tasks over scientific topics (e.g., datasets, materials discovery, prior art search) and public interest events (e.g., flight incidents, movie awards). Across state-of-the-art DR systems, F1 score ranges between 0.02 and 0.72 for any sub-category. OpenAI’s model performs the best with an overall F1 score of 0.55. Analysis of reasoning traces reveals the distribution over the number of referenced sources, branching, and backtracking events executed by current DR systems, motivating future directions for improving their search mechanisms and grounding capabilities. The benchmark is available at https://github.com/microsoft/LiveDRBench.

/* */