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Category: chemistry – Page 155

Cage escape governs photoredox reaction rates and quantum yields

The 3 MLCT-excited [Ru(bpz)3]2+ and the spin-flip excited states of [Cr(dqp)2]3+ underwent photoinduced electron-transfer reactions with 12 amine-based electron donors similarly well, but provided cage escape quantum yields differing by up to an order of magnitude. In three exemplary benchmark photoredox reactions performed with different electron donors, the differences in the reaction rates observed when using either [Ru(bpz)3]2+ or [Cr(dqp)2]3+ as photocatalyst correlated with the magnitude of the cage escape quantum yields. These correlations indicate that the cage escape quantum yields play a decisive role in the reaction rates and quantum efficiencies of the photoredox reactions, and also illustrate that luminescence quenching experiments are insufficient for obtaining quantitative insights into photoredox reactivity.

From a purely physical chemistry perspective, these findings are not a priori surprising as the rate of photoproduct formation in an overall reaction comprising several consecutive elementary steps can be expressed as the product of the quantum yields of the individual elementary steps45,46. A recent report on solvent-dependent cage escape and photoredox studies suggested that the correlations between photoredox product formation rates and cage escape quantum yields might be observable11, but we are unaware of previous reports that have been able to demonstrate that the rate of product formation in several batch-type photoreactions correlates with the cage escape quantum yields determined from laser experiments. Synthetic photochemistry and mechanistic investigations are often conducted under substantially different conditions, which can lead to controversial discrepancies47,48,49, whereas here their mutual agreement seems remarkable, particularly given the complexity of the overall reactions.

The available data and the presented analysis suggest that the different cage escape behaviours of [Ru(bpz)3]2+ and [Cr(dqp)2]3+ originate in the fact that for any given electron donor, in-cage reverse electron transfer is ~0.3 eV more exergonic for the RuII complex than for the CrIII complex. Thermal reverse electron transfer between caged radical pairs therefore occurs more deeply in the Marcus inverted region with [Ru(bpz)3]2+ than with [Cr(dqp)2]3+, decelerating in-cage charge recombination in the RuII complex and increasing the cage escape quantum yields compared with the CrIII complex (Fig. 3D).

How Fear Unfolds inside Our Brains

The stress-induced mechanisms that cause our brain to produce feelings of fear in the absence of threats have been mostly a mystery. Now, neurobiologists at the University of California San Diego have identified the changes in brain biochemistry and mapped the neural circuitry that cause such a generalized fear experience. Their research, published in the journal Science on March 15, 2024, provides new insights into how fear responses could be prevented.

In their report, former UC San Diego Assistant Project Scientist Hui-quan Li, (now a senior scientist at Neurocrine Biosciences), Atkinson Family Distinguished Professor Nick Spitzer of the School of Biological Sciences and their colleagues describe the research behind their discovery of the neurotransmitters — the chemical messengers that allow the brain’s neurons to communicate with one another — at the root of stress-induced generalized fear.

Studying the brains of mice in an area known as the dorsal raphe (located in the brainstem), the researchers found that acute stress induced a switch in the chemical signals in the neurons, flipping from excitatory “glutamate” to inhibitory “GABA” neurotransmitters, which led to generalized fear responses.

Mimicking exercise with a pill

NEW ORLEANS, March 18, 2024 — Doctors have long prescribed exercise to improve and protect health. In the future, a pill may offer some of the same benefits as exercise. Now, researchers report on new compounds that appear capable of mimicking the physical boost of working out — at least within rodent cells. This discovery could lead to a new way to treat muscle atrophy and other medical conditions in people, including heart failure and neurodegenerative disease.

The researchers will present their results today at the spring meeting of the American Chemical Society (ACS). ACS Spring 2024 is a hybrid meeting being held virtually and in person March 17–21; it features nearly 12,000 presentations on a range of science topics.

“We cannot replace exercise; exercise is important on all levels,” says Bahaa Elgendy, the project’s principal investigator who is presenting the work at the meeting. “If I can exercise, I should go ahead and get the physical activity. But there are so many cases in which a substitute is needed.”

MIT Unveils the Dance of Protons: Pioneering Energy’s New Era

New insights into how proton-coupled electron transfers occur at an electrode could help researchers design more efficient fuel cells and electrolyzers.

A key chemical reaction — in which the movement of protons between the surface of an electrode and an electrolyte drives an electric current — is a critical step in many energy technologies, including fuel cells and the electrolyzers used to produce hydrogen gas.

For the first time, MIT chemists have mapped out in detail how these proton-coupled electron transfers happen at an electrode surface. Their results could help researchers design more efficient fuel cells, batteries, or other energy technologies.

Measurement of non-monotonic Casimir forces between silicon nanostructures

Like Brian Greer has said the casimir technologies can power anything and create a free society a free utopia without the need for using any chemicals and it has been known since the 1950s in the physics community.

Previous demonstrations of the elusive Casimir force between interfaces exhibit monotonic dependence on surface displacement. Now a non-monotonic dependence of the force has been shown experimentally by exploting nanostructured surfaces.

Quantum Leap in Material Science: Researchers Unveil AI-Powered Atomic Fabrication Technique

Researchers at the National University of Singapore (NUS) have developed an innovative method for creating carbon-based quantum materials atom by atom. This method combines the use of scanning probe microscopy with advanced deep neural networks. The achievement underlines the capabilities of artificial intelligence (AI) in manipulating materials at the sub-angstrom level, offering significant advantages for basic science and potential future uses.

Open-shell magnetic nanographenes represent a technologically appealing class of new carbon-based quantum materials, which host robust π-spin centers and non-trivial collective quantum magnetism. These properties are crucial for developing high-speed electronic devices at the molecular level and creating quantum bits, the building blocks of quantum computers.

Despite significant advancements in the synthesis of these materials through on-surface synthesis, a type of solid-phase chemical reaction, achieving precise fabrication and tailoring of the properties of these quantum materials at the atomic level has remained a challenge.

Extreme treatment for alcoholism slashes drinking by 90% in monkeys

According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.

The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.

There is currently no way to reverse the changes in the brain brought about by AUD, but a team of US researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.

This unconventional superconductor is the first grown naturally

Scientists at Ames National Laboratory have revealed the first unconventional superconductor with a chemical composition naturally found in the Earth’s crust. Named “miassite,” this mineral joins a rare league of only four natural substances capable of exhibiting superconductivity under laboratory conditions.

According to the research team’s study published in the journal Communication Materials, the discovery holds promise for future advancements in sustainable and cost-effective technologies.

Novel Lensless Light Diffraction Method Detects Viral Infection

“Viruses, infections, and pandemics have become recurrent features in our lives, profoundly impacting human existence and even extending their reach to animals. Despite this, accessible, rapid, and affordable virus detection methods have been lacking,” said Xingcai Zhang, PhD, researcher, Harvard University, told GEN. “Our study aims to visualize viral infection states, predict infection duration, unravel the infection process, explore inhibition methods, and contribute to understanding viral disease transmission and pathogenesis.”

Viral infection of cells causes stress resulting in cell morphology differences over time. This study leveraged those known morphological changes to discern between infected and non-infected cells in culture. The standard practice for identifying infected cells, the methyl thiazolyl tetrazolium (MTT) assay, requires the use of reagent treatments and chemical reactions which can take upwards of 40 hours per sample, which is destroyed in the process.

The method proposed in this paper uses a lensless light diffraction platform to detect diffraction patterns, which can be used to extract information such as contrast and inverse differential moment which are used to create diffraction fingerprints. The fingerprints can be monitored continuously in the same samples as there is no inherent damage to cells.

WholeGraph Storage: Optimizing Memory and Retrieval for Graph Neural Networks

🧠 New Graph Neural Network Technique 🔥

NVIDIA researchers developed WholeGraphStor, a novel #GNN memory optimization.

Storing entire graphs in a compressed format reduces memory footprint,…

Graph neural networks (GNNs) have revolutionized machine learning for graph-structured data. Unlike traditional neural networks, GNNs are good at capturing intricate relationships in graphs, powering applications from social networks to chemistry. They shine particularly in scenarios like node classification, where they predict labels for graph nodes, and link prediction, where they determine the presence of edges between nodes.

Processing large graphs in a single forward or backward pass can be computationally expensive and memory-intensive.

The workflow for large-scale GNN training typically starts with subgraph sampling to use mini-batch training. This entails feature gathering to capture needed contextual information in a subgraph. Following these, the extracted features and subgraphs are employed in neural network training. This stage is where GNNs showcase proficiency in aggregating information and enabling the iterative propagation of node knowledge.

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