Lifeboat Foundation

A peer-reviewed study by scientists at the Environmental Working Group estimates that more than 200 million Americans could have the toxic fluorinated chemicals known as PFAS in their drinking water at a concentration of 1 part per trillion, or ppt, or higher. Independent scientific studies have recommended a safe level for PFAS in drinking water of 1 ppt, a standard that is endorsed by EWG.

The study, published today in the journal Environmental Science & Technology Letters, analyzed publicly accessible drinking water testing results from the Environmental Protection Agency and U.S. Geological Survey, as well as state testing by Colorado, Kentucky, Michigan, New Hampshire, New Jersey, North Carolina and Rhode Island.

“We know drinking water is a major source of exposure of these toxic chemicals,” said Olga Naidenko, Ph.D., vice president for science investigations at EWG and a co-author of the new study. “This new paper shows that PFAS pollution is affecting even more Americans than we previously estimated. PFAS are likely detectable in all major water supplies in the U.S., almost certainly in all that use surface water.”

Several startups are now pursuing the potential of enzymatic synthesis as a faster and more efficient route for synthesizing longer DNA sequences than is possible with traditional chemical means.

A group of scientists at Northeastern University are making progress using nanotechnology to prevent, diagnose and fight the coronavirus.

Thomas Webster, professor of chemical engineering at Northeastern University, has been working with nanotechnology for decades. Now, he and his team are finding new applications with the coronavirus.

Chemical space contains every possible chemical compound. It includes every drug and material we know and every one we’ll find in the future. It’s practically infinite and can be frustratingly complex. That’s why some chemists are turning to artificial intelligence: AI can explore chemical space faster than humans, and it might be able to find molecules that would elude even expert scientists. But as researchers work to build and refine these AI tools, many questions still remain about how AI can best help search chemical space and when AI will be able to assist the wider chemistry community.

Outer space isn’t the only frontier curious humans are investigating. Chemical space is the conceptual territory inhabited by all possible compounds. It’s where scientists have found every known medicine and material, and it’s where we’ll find the next treatment for cancer and the next light-absorbing substance for solar cells.

But searching chemical space is far from trivial. For one thing, it might as well be infinite. An upper estimate says it contains 10¹⁸⁰ compounds, more than twice the magnitude of the number of atoms in the universe. To put that figure in context, the CAS database—one of the world’s largest—currently contains about 10⁸ known organic and inorganic substances, and scientists have synthesized only a fraction of those in the lab. (CAS is a division of the American Chemical Society, which publishes C&EN.) So we’ve barely seen past our own front doorstep into chemical space.

“Our goal was to integrate interactive functionalities directly into the fibers of textiles instead of just attaching electronic components to them,” says Jürgen Steimle, computer science professor at Saarland University. In his research group on human-computer interaction at Saarland Informatics Campus, he and his colleagues are investigating how computers and their operation can be integrated as seamlessly as possible into the physical world. This includes the use of electro-interactive materials.

Previous approaches to the production of these textiles are complicated and influence the haptics of the material. The new method makes it possible to convert textiles and garments into e-textiles, without affecting their original properties—they remain thin, stretchable and supple. This creates new options for quick and versatile experimentation with new forms of e-textiles and their integration into IT devices.

“Especially for devices worn on the body, it is important that they restrict movement as little as possible and at the same time can process high-resolution input signals”, explains Paul Strohmeier, one of the initiators of the project and a scientist in Steimle’s research group. To achieve this, the Saarbrücken researchers are using the in-situ polymerization process. Here, the electrical properties are “dyed” into the fabric: a textile is subjected to a chemical reaction in a water bath, known as polymerization, which makes it electrically conductive and sensitive to pressure and stretching, giving it so-called piezoresistive properties. By “dyeing” only certain areas of a textile or polymerizing individual threads, the computer scientists can produce customized e-textiles.

Could make awesome computers.

Materials scientists who work with nano-sized components have developed ways of working with their vanishingly small materials. But what if you could get your components to assemble themselves into different structures without actually handling them at all?

Verner Håkonsen works with cubes so tiny that nearly 5 billion of them could fit on a pinhead.

Continue reading “5 Billion of These Super-Strong Magnetic Supercrystals Can Fit on a Pinhead” »

Over the past decade or so, the performance of batteries has skyrocketed and their cost has plummeted. Given that many experts see the electrification of everything as key to decarbonizing our energy systems, this is good news. But for researchers like Chueh, the pace of battery innovation isn’t happening fast enough. The reason is simple: batteries are extremely complex. To build a better battery means ruthlessly optimizing at every step in the production process. It’s all about using less expensive raw materials, better chemistry, more efficient manufacturing techniques. But there are a lot of parameters that can be optimized. And often an improvement in one area—say, energy density—will come at a cost of making gains in another area, like charge rate.

Improving batteries has always been hampered by slow experimentation and discovery processes. Machine learning is speeding it up by orders of magnitude.

Caltech’s OrbNet deep learning tool outperforms state-of-the-art solutions.

Artificial intelligence (AI) machine learning is being applied to help accelerate the complex science of quantum mechanics—the branch of physics that studies matter and light on the subatomic scale. Recently a team of scientists at the California Institute of Technology (Caltech) published a breakthrough study in The Journal of Chemical Physics that unveils a new machine learning tool called OrbNet that can perform quantum chemistry computations 1,000 times faster than existing state-of-the-art solutions.

“We demonstrate the performance of the new method for the prediction of molecular properties, including the total and relative conformer energies for molecules in range of datasets of organic and drug-like molecules,” wrote the researchers.

Continue reading “AI Breakthrough Speeds Up Quantum Chemistry” »

The ingredients for reactions ancestral to metabolism could have formed very easily in the primordial soup, new work suggests.