Lifeboat Foundation

Magnesium dimer (Mg₂) is a fragile molecule consisting of two weakly interacting atoms held together by the laws of quantum mechanics. It has recently emerged as a potential probe for understanding fundamental phenomena at the intersection of chemistry and ultracold physics, but its use has been thwarted by a half-century-old enigma—five high-lying vibrational states that hold the key to understanding how the magnesium atoms interact but have eluded detection for 50 years.

The lowest fourteen Mg₂ vibrational states were discovered in the 1970s, but both early and recent experiments should have observed a total of nineteen states. Like a quantum cold case, experimental efforts to find the last five failed, and Mg₂ was almost forgotten. Until now.

Piotr Piecuch, Michigan State University Distinguished Professor and MSU Foundation Professor of chemistry, along with College of Natural Science Department of Chemistry graduate students Stephen H. Yuwono and Ilias Magoulas, developed new, computationally derived evidence that not only made a quantum leap in first-principles quantum chemistry, but finally solved the 50-year-old Mg₂ mystery.

Molecular dynamics is at the point of simulating bulk matter – but don’t expect it to predict the future.

The TV series Devs took as its premise the idea that a quantum computer of sufficient power could simulate the world so completely that it could project events accurately back into the distant past (the Crucifixion or prehistory) and predict the future. At face value somewhat absurd, the scenario supplied a framework on which to hang questions about determinism and free will (and less happily, the Many Worlds interpretation of quantum mechanics).

Quite what quantum computers will do for molecular simulations remains to be seen, but the excitement about them shouldn’t eclipse the staggering advances still being made in classical simulation. Full ab initio quantum-chemical calculations are very computationally expensive even with the inevitable approximations they entail, so it has been challenging to bring this degree of precision to traditional molecular dynamics, where molecular interactions are still typically described by classical potentials. Even simulating pure water, where accurate modelling of hydrogen bonding and the ionic disassociation of molecules involves quantum effects, has been tough.

The National Weather Service on Tuesday evening urged anyone near the river to seek higher ground following “castastrophic dam failures” at the Edenville Dam, about 140 miles north of Detroit and the Sanford Dam, about seven miles downriver.

The Tittabawassee River rose another four feet by Wednesday morning, to 34.4 feet in Midland. According to the National Weather Service, the height has set a new record for the river, beating the previous record of 33.9 feet set during flooding in 1986.

Whitmer said downtown Midland, a city of 42,000 about 8 miles downstream from the Sanford Dam, faced an especially serious flooding threat. Dow Chemical Co.’s main plant sits on the city’s riverbank.

Strong coupling between cavity photon modes and donor/acceptor molecules can form polaritons (hybrid particles made of a photon strongly coupled to an electric dipole) to facilitate selective vibrational energy transfer between molecules in the liquid phase. The process is typically arduous and hampered by weak intermolecular forces. In a new report now published on Science, Bo Xiang, and a team of scientists in materials science, engineering and biochemistry at the University of California, San Diego, U.S., reported a state-of-the-art strategy to engineer strong light-matter coupling. Using pump-probe and two-dimensional (2-D) infrared spectroscopy, Xiang et al. found that strong coupling in the cavity mode enhanced the vibrational energy transfer of two solute molecules. The team increased the energy transfer by increasing the cavity lifetime, suggesting the energy transfer process to be a polaritonic process. This pathway on vibrational energy transfer will open new directions for applications in remote chemistry, vibration polariton condensation and sensing mechanisms.

Vibrational energy transfer (VET) is a universal process ranging from chemical catalysis to biological signal transduction and molecular recognition. Selective intermolecular vibrational energy transfer (VET) from solute-to-solute is relatively rare due to weak intermolecular forces. As a result, intermolecular VET is often unclear in the presence of intramolecular vibrational redistribution (IVR). In this work, Xiang et al. detailed a state-of-the-art method to engineer intermolecular vibrational interactions via strong light-matter coupling. To accomplish this, they inserted a highly concentrated molecular sample into an optical microcavity or placed it onto a plasmonic nanostructure. The confined electromagnetic modes in the setup then reversibly interacted with collective macroscopic molecular vibrational polarization for hybridized light-matter states known as vibrational polaritons.

The pandemic COVID-19 has spread to all over the world and greatly threatens safety and health of people. COVID-19 is highly infectious and with high mortality rate. As no effective antiviral treatment is currently available, new drugs are urgently needed. We employed transcriptional analysis to uncover potential antiviral drugs from natural products or FDA approved drugs. We found liquiritin significantly inhibit replication of SARS-CoV-2 in Vero E6 cells with EC₅₀ = 2.39 μM. Mechanistically, we found liquiritin exerts anti-viral function by mimicking type I interferon. Upregulated genes induced by liquiritin are enriched in GO categories including type I interferon signaling pathway, negative regulation of viral genome replication and etc. In toxicity experiment, no death was observed when treated at dose of 300 mg/kg for a week in ICR mice. All the organ indexes but liver and serum biochemical indexes were normal after treatment. Liquiritin is abundant in licorice tablet (~0.2% by mass), a traditional Chinese medicine. Together, we recommend liquiritin as a competitive candidate for treating COVID-19. We also expect liquiritin to have a broad and potent antiviral function to other viral pathogens, like HBV, HIV and etc.

The authors have declared no competing interest.

Skoltech researchers have offered a solution to the problem of searching for materials with required properties among all possible combinations of chemical elements. These combinations are virtually endless, and each has an infinite multitude of possible crystal structures; it is not feasible to test them all and choose the best option (for instance, the hardest compound) either in an experiment or in silico. The computational method developed by Skoltech professor Artem R. Oganov and his PhD student Zahed Allahyari solves this major problem of theoretical materials science. Oganov and Allahyari presented their method in the MendS code (stands for Mendelevian Search) and tested it on superhard and magnetic materials.

“In 2006, we developed an algorithm that can predict the crystal structure of a given fixed combination of chemical elements. Then we increased its predictive powers by teaching it to work without a specific combination — so one calculation would give you all stable compounds of given elements and their respective crystal structures. The new method tackles a much more ambitious task: here, we pick neither a precise compound nor even specific chemical elements — rather, we search through all possible combinations of all chemical elements, taking into account all possible crystal structures, and find those that have the needed properties (e.g., highest hardness or highest magnetization)” says Artem Oganov, Skoltech and MIPT professor, Fellow of the Royal Society of Chemistry and a member of Academia Europaea.

The researchers first figured out that it was possible to build an abstract chemical space so that compounds that would be close to each other in this space would have similar properties. Thus, all materials with peculiar properties (for example, superhard materials) will be clustered in certain areas, and evolutionary algorithms will be particularly effective for finding the best material. The Mendelevian Search algorithm runs through a double evolutionary search: for each point in the chemical space, it looks for the best crystal structure, and at the same time these found compounds compete against each other, mate and mutate in a natural selection of the best one.

The global pandemic of COVID-19 cases caused by infection with SARS-CoV-2 is ongoing, with no approved antiviral intervention. We describe here the effects of treatment with interferon (IFN)-α2b in a cohort of confirmed COVID-19 cases in Wuhan, China. In this uncontrolled, exploratory study, 77 adults hospitalized with confirmed COVID-19 were treated with either nebulized IFN-α2b (5 mU b.i.d.), arbidol (200 mg t.i.d.) or a combination of IFN-α2b plus arbidol. Serial SARS-CoV-2 testing along with hematological measurements, including cell counts, blood biochemistry and serum cytokine levels, and temperature and blood oxygen saturation levels, were recorded for each patient during their hospital stay. Treatment with IFN-α2b with or without arbidol significantly reduced the duration of detectable virus in the upper respiratory tract and in parallel reduced duration of elevated blood levels for the inflammatory markers IL-6 and CRP. These findings suggest that IFN-α2b should be further investigated as a therapy in COVID-19 cases.

In December 2019, an outbreak of pneumonia was reported in Wuhan, Hubei province, China, resulting from infection with a novel coronavirus (CoV), severe acute respiratory syndrome (SARS)-CoV-2. SARS-CoV-2 is a novel, enveloped betacoronavirus with phylogenetic similarity to SARS-CoV (1). Unlike the coronaviruses HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU, that are pathogenic in humans and are associated with mild clinical symptoms, SARS-CoV-2 resembles both SARS-CoV and Middle East respiratory syndrome (MERS), with the potential to cause more severe disease. A critical distinction is that CoVs that infect the upper respiratory tract tend to cause a mild disease, whereas CoVs that infect both upper and lower respiratory tracts (such as SARS-CoV-2 appears to be) may cause more severe disease. Coronavirus disease (COVID)-19, the disease caused by SARS-CoV-2, has since spread around the globe as a pandemic.

In the absence of a SARS-CoV-2-specific vaccine or an approved antiviral, a number of antivirals are currently being evaluated for their therapeutic effectiveness. Type I IFNs-α/β are broad spectrum antivirals, exhibiting both direct inhibitory effects on viral replication and supporting an immune response to clear virus infection (2). During the 2003 SARS-CoV outbreak in Toronto, Canada, treatment of hospitalized SARS patients with an IFN-α, resulted in accelerated resolution of lung abnormalities (3). Arbidol (ARB) (Umifenovir) (ethyl-6-bromo-4-[(dimethylamino)methyl]-5-hydroxy-1-methyl-2 [(phenylthio)methyl]-indole-3-carboxylate hydrochloride monohydrate), a broad spectrum direct-acting antiviral, induces IFN production and phagocyte activation. ARB displays antiviral activity against respiratory viruses, including coronaviruses (4).

High-fidelity modelling of nanosecond repetitively pulsed discharges (NRPDs) is burdened by the multiple time and length scales and large chemistry mechanisms involved, which prohibit detailed analyses and parametric studies. In the present work, we propose a ‘frozen electric-field’ modelling approach to expedite the NRPD simulations without adverse effects on the solution accuracy. First, a burst of nanosecond voltage pulses is simulated self-consistently until the discharge reaches a stationary state. The calculated spatial distributions and temporal evolution of the electric field, electron density and electron energy during the last pulse are then stored in a library and the electrical characteristics of subsequent pulses are frozen at these values. This strategy allows the timestep for numerical integration to be increased by four orders of magnitude (from 10⁻¹³ to 10⁻⁹ s), thereby significantly improving the computational efficiency of the process. Reduced calculations of a burst of 50 discharge pulses show good agreement with the predictions from a complete plasma model (electrical characteristics calculated during each pulse). The error in species densities is less than 20% at the centre of the discharge volume and about 30% near the boundaries. The deviations in temperature, however, are much lower, at 5% in the entire domain. The model predictions are in excellent agreement with measured ignition delay times and temperatures in H₂–air mixtures subject to dielectric barrier NRPD over a pressure range of 54–144 Torr with equivalence ratios of 0.7–1.2. The OH density increases with pressure and triggers low-temperature fuel oxidation, which leads to rapid temperature rise and ignition. The ignition delay decreases by a factor of 2, with an increase in pressure from 54 to 144 Torr. In contrast, an increase in the H₂–air equivalence ratio from 0.7 to 1.2 marginally decreases the ignition delay by about 20%. This behaviour is attributed to the insensitivity of OH production rates to the variation in the equivalence ratio.

Carbon nanotubes embedded in leaves detect chemical signals that are produced when a plant is damaged.

MIT engineers have developed a way to closely track how plants respond to stresses such as injury, infection, and light damage, using sensors made of carbon nanotubes. These sensors can be embedded in plant leaves, where they report on hydrogen peroxide signaling waves.

Plants use hydrogen peroxide to communicate within their leaves, sending out a distress signal that stimulates leaf cells to produce compounds that will help them repair damage or fend off predators such as insects. The new sensors can use these hydrogen peroxide signals to distinguish between different types of stress, as well as between different species of plants.