Lifeboat Foundation

A typical nuclear reactor uses only a small fraction of its fuel rod to produce power before the energy-generating reaction naturally terminates. What is left behind is an assortment of radioactive elements, including unused fuel, that are disposed of as nuclear waste in the United States. Although certain elements recycled from waste can be used for powering newer generations of nuclear reactors, extracting leftover fuel in a way that prevents possible misuse is an ongoing challenge.

Now, Texas A&M University engineering researchers have devised a simple, proliferation-resistant approach for separating out different components of nuclear waste. The one-step chemical reaction, described in the February issue of the journal Industrial & Engineering Chemistry Research, results in the formation of crystals containing all of the leftover nuclear fuel elements distributed uniformly.

The researchers also noted that the simplicity of their recycling approach makes the translation from lab bench to industry feasible.

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To trace the trail of the virus worldwide, Lim’s team is using a new technology called next-generation sequencing at ASU’s Genomics Facility, to rapidly read through all 30,000 chemical letters of the SARS-CoV-2 genetic code, called a genome.

Each sequence is deposited into a worldwide gene bank, run by a nonprofit scientific organization called GISAID. To date, over 16,000 SARS-CoV-2 sequences have been deposited GISAID’s EpiCoVTM Database. The sequence data shows that SARS-CoV-2 originated a single source from Wuhan, China, while many of the first Arizona cases analyzed showed travel from Europe as the most likely source.

Now, using a pool of 382 nasal swab samples obtained from possible COVID-19 cases in Arizona, Lim’s team has identified a SARS-CoV-2 mutation that had never been found before—where 81 of the letters have vanished, permanently deleted from the genome.

HOUSTON, May 1, 2020 — Rice University researchers plan to reconfigure their wastewater-treatment technology to capture and deactivate the virus that causes COVID-19. Their chemical-free nanotechnology, introduced earlier this year as a way to kill bacterial “superbugs” and degrade their antibiotic resistance genes in wastewater, will use graphitic carbon nitride to selectively adsorb viruses and then disable them by activating nearby catalysts with light. The team believes that this photocatalytic approach to disinfection — what it calls the “trap-and-zap” treatment approach — could be used to recognize coronaviruses that cause not only COVID-19 but also MERS and SARS.

German researchers have identified a strain of bacterium that not only breaks down toxic plastic, but also uses it as food to fuel the process, according to The Guardian.

The scientists discovered the strain of bacteria, known as pseudomonas bacteria, at a dump site loaded with plastic waste, where they noticed that it was attacking polyurethane. Polyurethane’s are ubiquitous in plastic products because they are pliable and durable. However, when they reach the end of their usefulness and end up in landfills, they decompose slowly and slowly release toxic chemicals into the soil as they degrade. They are also notoriously difficult to recycle, according to Courthouse News.

The first step in many light-driven chemical reactions, like the ones that power photosynthesis and human vision, is a shift in the arrangement of a molecule’s electrons as they absorb the light’s energy. This subtle rearrangement paves the way for everything that follows and determines how the reaction proceeds.

Now scientists have seen this first step directly for the first time, observing how the molecule’s electron cloud balloons out before any of the atomic nuclei in the molecule respond.

While this response has been predicted theoretically and detected indirectly, this is the first time it’s been directly imaged with X-rays in a process known as molecular movie-making, whose ultimate goal is to observe how both electrons and nuclei act in real time when chemical bonds form or break.

Researchers have developed a number of potassium ion (K⁺) probes to detect fluctuating K⁺ concentrations during a variety of biological processes. However, such probes are not sensitive enough to detect physiological fluctuations in living animals and it is not easy to monitor deep tissues with short-wavelength excitations that are in use so far. In a new report, Jianan Liu and a team of researchers in neuroscience, chemistry, and molecular engineering in China, describe a highly sensitive and selective nanosensor for near infrared (NIR) K⁺ ion imaging in living cells and animals. The team constructed the nanosensor by encapsulating upconversion nanoparticles (UCNPs) and a commercial potassium ion indicator in the hollow cavity of mesoporous silica nanoparticles and coated them with a K⁺ selective filter membrane. The membrane adsorbed K⁺ from the medium and filtered away any interfering cations. In its mechanism of action, UCNPs converted NIR to ultraviolet (UV) light to excite the potassium ion indicator and detect fluctuating potassium ion concentrations in cultured cells and in animal models of disease including mice and zebrafish larvae. The results are now published on Science Advances.

The most abundant intracellular cation potassium (K⁺) is extremely crucial in a variety of biological processes including neural transmission, heartbeat, muscle contraction and kidney function. Variations in the intracellular or extracellular K⁺ concentration (referred herein as [K⁺]) suggest abnormal physiological functions including heart dysfunction, cancer, and diabetes. As a result, researchers are keen to develop effective strategies to monitor the dynamics of [K⁺] fluctuations, specifically with direct optical imaging.

Most existing probes are not sensitive to K⁺ detection under physiological conditions and cannot differentiate fluctuations between [K⁺] and the accompanying sodium ion ([Na⁺]) during transmembrane transport in the Na⁺/K⁺ pumps. While fluorescence lifetime imaging can distinguish K⁺ and Na⁺ in water solution, the method requires specialized instruments. Most K⁺ sensors are also activated with short wavelength light including ultraviolet (UV) or visible light—leading to significant scattering and limited penetration depth when examining living tissues. In contrast, the proposed near-infrared (NIR) imaging technique will offer unique advantages during deep tissue imaging as a plausible alternative.

Using machine learning three groups, including researchers at IBM and DeepMind, have simulated atoms and small molecules more accurately than existing quantum chemistry methods. In separate papers on the arXiv preprint server the teams each use neural networks to represent wave functions of electrons that surround the molecules’ atoms. This wave function is the mathematical solution of the Schrödinger equation, which describes the probabilities of where electrons can be found around molecules. It offers the tantalising hope of ‘solving chemistry’ altogether, simulating reactions with complete accuracy. Normally that goal would require impractically large amounts of computing power. The new studies now offer a compromise of relatively high accuracy at a reasonable amount of processing power.

Each group only simulates simple systems, with ethene among the most complex, and they all emphasise that the approaches are at their very earliest stages. ‘If we’re able to understand how materials work at the most fundamental, atomic level, we could better design everything from photovoltaics to drug molecules,’ says James Spencer from DeepMind in London, UK. ‘While this work doesn’t achieve that quite yet, we think it’s a step in that direction.’

Two approaches appeared on arXiv just a few days apart in September 2019, both combining deep machine learning and Quantum Monte Carlo (QMC) methods. Researchers at DeepMind, part of the Alphabet group of companies that owns Google, and Imperial College London call theirs Fermi Net. They posted an updated preprint paper describing it in early March 2020.¹ Frank Noé’s team at the Free University of Berlin, Germany, calls its approach, which directly incorporates physical knowledge about wave functions, PauliNet.²

The synthesis of plastic precursors, such as polymers, involves specialized catalysts. However, the traditional batch-based method of finding and screening the right ones for a given result consumes liters of solvent, generates large quantities of chemical waste, and is an expensive, time-consuming process involving multiple trials.

Ryan Hartman, professor of chemical and biomolecular engineering at the NYU Tandon School of Engineering, and his laboratory developed a lab-based “intelligent microsystem” employing machine learning, for modeling chemical reactions that shows promise for eliminating this costly process and minimizing environmental harm.

In their research, “Combining automated microfluidic experimentation with machine learning for efficient polymerization design,” published in Nature Machine Intelligence, the collaborators, including doctoral student Benjamin Rizkin, employed a custom-designed, rapidly prototyped microreactor in conjunction with automation and in situ infrared thermography to study exothermic (heat generating) polymerization—reactions that are notoriously difficult to control when limited experimental kinetic data are available. By pairing efficient microfluidic technology with machine learning algorithms to obtain high-fidelity datasets based on minimal iterations, they were able to reduce chemical waste by two orders of magnitude and catalytic discovery from weeks to hours.