With the advent of machine learning and artificial intelligence, materials science could be poised to take its next big leap in efficiency since the introduction of computational chemistry.
Back in the old days—the really old days—the task of designing materials was laborious. Investigators, over the course of 1,000-plus years, tried to make gold by combining things like lead, mercury, and sulfur, mixed in what they hoped would be just the right proportions. Even famous scientists like Tycho Brahe, Robert Boyle, and Isaac Newton tried their hands at the fruitless endeavor we call alchemy.
Materials science has, of course, come a long way. For the past 150 years, researchers have had the benefit of the periodic table of elements upon which to draw, which tells them that different elements have different properties, and one can’t magically transform into another. Moreover, in the past decade or so, machine learning tools have considerably boosted our capacity to determine the structure and physical properties of various molecules and substances.
New research by a group led by Ju Li—the Tokyo Electric Power Company Professor of Nuclear Engineering at MIT and professor of materials science and engineering—offers the promise of a major leap in capabilities that can facilitate materials design. The results of their investigation appear in Nature Computational Science.
Researchers from Tokyo Metropolitan University have created nanostructured alumina surfaces which are strongly antibacterial but can be used to culture cells. They found that anodic porous alumina (APA) surfaces prepared using electrochemistry in concentrated sulfuric acid had unprecedented resistance to bacterial growth, but did not hamper cell cultures.
The work is published in the journal Langmuir.
The team’s technology promises to have a big impact on regenerative medicine, where high quality cell cultures without bacterial contamination may be produced without antibiotics.
Quantum computers may soon dramatically enhance our ability to solve problems modeled by nonreversible Markov chains, according to a study published on the pre-print server arXiv.
The researchers from Qubit Pharmaceuticals and Sorbonne University, demonstrated that quantum algorithms could achieve exponential speedups in sampling from such chains, with the potential to surpass the capabilities of classical methods. These advances — if fully realized — have a range of implications for fields like drug discovery, machine learning and financial modeling.
Markov chains are mathematical frameworks used to model systems that transition between various states, such as stock prices or molecules in motion. Each transition is governed by a set of probabilities, which defines how likely the system is to move from one state to another. Reversible Markov chains — where the probability of moving from, let’s call them, state A to state B equals the probability of moving from B to A — have traditionally been the focus of computational techniques. However, many real-world systems are nonreversible, meaning their transitions are biased in one direction, as seen in certain biological and chemical processes.
In a significant step toward creating a sustainable and circular economy, Rice University researchers have published a study in the journal Carbon demonstrating that carbon nanotube (CNT) fibers can be fully recycled without any loss in their structure or properties. This discovery positions CNT fibers as a sustainable alternative to traditional materials like metals, polymers and the much larger carbon fibers, which are notoriously difficult to recycle.
“Recycling has long been a challenge in the materials industry—metals recycling is often inefficient and energy-intensive, polymers tend to lose their properties after reprocessing and carbon fibers cannot be recycled at all, only downcycled by chopping them up into short pieces,” said corresponding author Matteo Pasquali, director of Rice’s Carbon Hub and the A.J. Hartsook Professor of Chemical and Biomolecular Engineering, Materials Science and NanoEngineering and Chemistry.
An unplugged electric instrument may function, but it sounds much better when it is connected to an amplifier. Similarly, toxins and other small molecules at low concentrations in the environment or human body may emit quiet signals that are undetectable without specialized lab technology.
Now, thanks to a “cool trick” in biochemistry used to adapt a sensing platform already being deployed by Northwestern scientists to measure toxins in drinking water, researchers can detect and even measure chemicals at low enough concentrations to have use outside the lab. By attaching circuitry akin to a volume knob to “turn up” weak signals, the team has opened the door for the system to be applied to disease detection and monitoring in the human body for nucleic acids like DNA and RNA, as well as bacteria such as E. coli.
The results, which describe a system that is 10 times more sensitive than previous cell-free sensors built by the team, are published in the journal Nature Chemical Biology.
A research team developed electrokinetic mining (EKM), an eco-friendly method for extracting rare earth elements. EKM reduces environmental harm, lowers resource use, and achieved over 95% recovery in industrial tests, marking a breakthrough in sustainable mining.
On-adsorption rare earth deposits (IADs) are the primary source of heavy rare earth elements (HREE), meeting over 90% of global demand. However, the widely used ammonium-salt-based in-situ mining method has caused significant environmental damage.
To promote sustainable rare earth element (REE) extraction, Professors Jianxi Zhu and Hongping He from the Guangzhou Institute of Geochemistry at the Chinese Academy of Sciences (CAS) have developed an environmentally friendly and efficient electrokinetic mining (EKM) technology.
Scientists at Penn State have discovered a method to induce ferroelectric properties in non-ferroelectric materials by layering them with ferroelectric materials, a phenomenon termed proximity ferroelectricity.
This breakthrough offers a novel approach to creating ferroelectric materials without altering their chemical composition, preserving their intrinsic properties, and potentially revolutionizing data storage, wireless communication, and the development of next-generation electronic devices.
New ferroelectric materials without chemical alterations.
In this video, we simplify gluconeogenesis, an essential metabolic pathway that helps your body maintain glucose levels during fasting or intense activity.
We’ll walk you through:
✔️ What gluconeogenesis is and why it’s important.
✔️ Key steps in the pathway.
✔️ Enzymes involved and their regulation.
✔️ How it ties into other metabolic processes.
Continue reading “Gluconeogenesis: Welcome to EasyPeasy Learning!” »
Stanford researchers have introduced a software tool that accelerates and enhances the analysis of single atom catalysts, offering profound implications for the development of more efficient catalysts.
Catalysts play an essential role in everyday life, from helping bread rise to converting raw materials into fuels more efficiently. Now, researchers at SLAC have developed a faster method to advance the discovery of an exciting new type of catalyst known as single atom catalysts.
The role of catalysts in modern chemistry.
