Articles tagged with Chemical Modeling
Undergraduate students at Ohio University applied kinetic techniques learned in physical chemistry to analyze the COVID-19 spread data from the CDC. They developed a model that considers the effect of vaccination and made predictions for the following months, correctly predicting the surge in cases in Ohio.
Researchers discovered how coronaviruses use caspase-6 to cleave and neutralize the host's interferon response, facilitating viral replication. By inhibiting or overexpressing caspase-6, researchers reduced coronavirus replication in various models, suggesting a potential target for antiviral treatment.
The team is creating a virtual gut system to test chemical reactions and networks, which will help identify the best treatments for obesity. The MATOMIC project aims to develop mathematical models that can simulate the intestinal microbiome at an atomic level.
Researchers at UMass Amherst investigate how embryonic exposure to PFAS chemicals affects pancreatic development and glucose regulation. Preliminary data suggest a link between PFAS exposure and elevated fructosamine levels, indicating potential long-term diabetes risk.
Researchers at Eindhoven University of Technology accidentally discovered that adding more water to a liquid solution turns it back into a gel, and then further dilution forms another gel. The team's findings have significant implications for various fields in chemistry and biology.
Researchers developed open-source software SHRY to find distinct substitution patterns in disordered systems, reducing computation time. The software uses group theory and canonical augmentation to efficiently analyze crystal structures with random substitutions.
A research team from Tokyo University of Science has developed a new method to create copolymers with different metal species, which have potential uses in catalysis and drug discovery. The technique allows for controlling the composition of metal species in the resulting polymer.
An international team developed a new theoretical model that solves part of the 'solar problem' by considering the Sun's rotation and magnetic fields. The results reproduce the concentration of helium and lithium in the Sun's outer layers, providing insights into stellar physics.
Researchers have found a way to perform hydrogen atom transfer reactions with fewer chemicals and less cost, making it more efficient for industrial and academic settings. The new method uses electrochemistry to create cobalt hydride catalysts, reducing the need for expensive oxidants and reductants.
Researchers at the University of Pittsburgh have discovered a new catalyst, tungsten oxide, that can efficiently convert carbon dioxide into useful fuels and chemicals. This breakthrough could lead to a significant reduction in global warming by utilizing hydrogen produced from renewable energy sources.
Researchers at MIT developed an AI method that constrains machine-learning models to suggest molecules with producible chemical structures. The approach guarantees quality and speed, outperforming existing methods in proposing high-quality molecular structures.
Researchers aim to improve stability and efficiency of catalytic materials using quantum mechanics-based calculations and computational simulations. The goal is to create more effective catalysts that reduce pollution and energy consumption.
Researchers at Chalmers University of Technology have successfully converted solar energy into electricity using a thermoelectric generator. The new technology can store solar energy for up to 18 years and release it when needed, making it a promising solution for renewable energy production.
Researchers at George Washington University developed a novel tool to rapidly screen pesticides for safety and performance. The new approach can aid in the design of next-generation molecules to develop safer pesticides, with only 7% of analyzed compounds meeting safety criteria.
A team of researchers at The University of Tokyo has created a model that reveals the role of emergent elastic fields in chiral molecular and colloidal crystals. The findings provide a potential switch for developing new electro- and magneto-mechanical devices.
Researchers at the University of Tokyo have developed a new model to aid interpretation of atomic resolution molecular images. The Z-correlated molecular model accurately fits imaging data and helps chemists analyze electron microscope images without theoretical calculations.
Researchers propose that chaos terrains on Europa's surface could shuttle oxygen to the moon's subsurface ocean, where it could sustain life. The computer model showed that 86% of oxygen is transported through the ice via a 'porosity wave', potentially raising hopes for finding life in Europa's ocean.
Engineer Thomas Senftle at Rice University has won a prestigious NSF CAREER Award to improve catalysts through machine learning. He will develop open-source models to speed up the development of catalysts with optimized particle/support combinations, aiming to reduce unwanted molecules in water.
A new virtual ligand-assisted screening method enables efficient searching of ligands for optimal catalysis, accelerating reaction design. By simplifying ligand description with two metrics, researchers identify promising ligands and focus on real testing.
Pharmaceutical firms are working towards using machine learning to analyze vast stores of data, developing models that evolve and improve as the data are processed. However, experts agree that a fully functional end-to-end approach is still a ways off due to biology's complexity.
Researchers at University of Texas at Austin create first-ever biologically authentic computer model of HIV-1 virus liposome, shedding light on replication and infectivity. The study reveals key characteristics of the liposome's asymmetry and its role in shaping macroscopic properties.
The study of MUNC long non-coding RNA reveals the importance of experimentally determining its structure to identify functional domains. The researchers found that two structural domains, including six common 'hairpins,' were crucial for regulating gene expression and muscle cell differentiation.
Researchers analyzed over 1.5 million SARS-CoV-2 genome sequences to understand the evolution of the virus, finding that increased infectivity is a driving force behind its spread. The omicron variant is predicted to compromise current vaccines and therapies, highlighting the need for new generation of vaccines and monoclonal antibodies.
A field study in South Korea has provided valuable tests of photochemical models' ability to represent atmospheric chemistry, especially in polluted environments. The analysis of airborne measurements of hydroxyl radical and other chemical species showed agreement with model values when uncertainties are considered.
Researchers from Waseda University have developed an alternative technique, sampled current voltammetry (SCV), to accurately determine the activity of electrocatalysts used in water-splitting reactions. The study shows that SCV can provide reliable measurements of electrocatalytic performance at constant steady-state applied voltages.
Scientists at the University of Missouri study photodissociation reactions on the quantum level, revealing strong quantum effects that challenge classical 'billiard-ball' models. The research could lead to a better understanding of atmospheric chemistry and develop new theoretical frameworks.
Scientists have developed a fusion protein that successfully blocks replication of SARS-CoV-2 and related viruses in cell culture tests. The protein combines ACE2 with human antibody fragments, providing reliable protection against future mutations.
A new study published in Nature Communications reveals chemical heterogeneities in Apollo 17 sample troctolite 76535, indicating an early rapid cooling of the Moon. This finding challenges previous estimates of a 100-million-year cooling duration and supports initial rapid cooling of magmas within the lunar crust.
A new project led by University of Illinois researchers will develop machine learning models to predict the reactivity of thousands of organic contaminants in engineered and natural environments. This will help scientists better model pollutant fate and transport, leading to more accurate contaminant risk assessments.
Researchers developed a machine learning method to assign NMR spectra of organic crystals probabilistically from their 2D chemical structures. The approach uses a database of chemical shifts for organic solids, reducing computational cost by up to 10,000 times compared to current methods.
Researchers at Tokyo University of Science have reported the first-ever observation of long-range ferromagnetic order in icosahedral quasicrystals. The discovery was made using conventional X-ray diffraction, magnetic susceptibility, and specific heat measurements.
Scientists at the University of Bath have developed a new method for synthesizing primary amines, used in over half of all pharmaceuticals, significantly reducing energy usage and chemical waste. This breakthrough aims to speed up drug discovery by making it easier to synthesize new chemical structures for testing.
Chemists at Johannes Gutenberg University Mainz have developed a method to produce cement by milling instead of burning lime, reducing CO2 emissions. The process could be implemented on an industrial scale, but further development is needed.
Researchers at the University of Gothenburg discovered a new chemistry involving water-soluble particle surfaces, which may be useful for sewage treatment and other industrial applications. The study found that these surfaces can absorb water and dissolve, promoting previously undiscovered chemical reactions.
Researchers linked microscopic and macroscopic approaches to describe a technologically important chemical reaction under realistic conditions. This allows understanding why catalyst particle size plays a crucial role in chemical processes.
Researchers used machine learning to analyze core-loss spectroscopy data, revealing connections between spectral data and material properties. The study successfully predicted intensive and extensive material properties, enabling high-throughput development of new materials.
Researchers found a decline in pollutant concentrations worldwide during the 2020 lockdown, particularly in Central Europe. The study attributed these reductions to the lockdown's impact on sectors like road and air transport, but noted that weather conditions also played a significant role.
A new framework from Cornell University's Fengqi You and Xiang Zhao provides a comprehensive analysis of the environmental impacts of plastic waste chemical recycling. The framework quantifies climate change and human toxicity, offering insights into optimal technologies and market dynamics.
The Center for Chemical Currencies of a Microbial Planet (C-CoMP) will conduct transformative research on the ocean chemical-microbe network and its role in climate change. The center aims to improve our understanding of this network's dynamics, sensitivity to climate change, and feedbacks on the planet.
Researchers at Goethe University Frankfurt and Bonn have synthesized molecular nano spheres made of silicon atoms, known as silafulleranes, which can encapsulate chloride ions. The discovery of these new compounds may lead to improved applications in electronics, solar cells, and batteries.
Researchers from Osaka City University have developed a Bayesian phase difference estimation (BPDE) algorithm that directly calculates the energy difference between two relevant quantum states. This breakthrough enables precise accuracy in chemistry problems and overcomes limitations of conventional full-CI calculations.
The researcher is investigating the inflammatory responses produced by exposure to chemical agents, comparing different chemical exposures at both whole-body and tissue levels. The study aims to develop a common treatment for chemical exposures and diagnose the responsible chemicals.
Michigan Tech researchers developed a model to calculate how particular chemicals break down in surface water using singlet oxygen, which degrades contaminants and helps protect our waterways. The study's findings can aid environmental engineers and scientists in estimating half-lives of chemicals and predicting their degradation rates.
Researchers at UC Santa Cruz heated meteorite samples in a high-temperature furnace and analyzed the gases released. The study suggests that the initial atmospheres of terrestrial planets may differ significantly from current theoretical models.
Researchers created a new type of machine learning model to predict power-conversion efficiency of materials for next-generation organic solar cells. The approach is quick and easy to use, providing important data on chemical fragments that affect performance.
Researchers aim to develop a new method of electrolysis that uses electricity instead of high pressure and temperature, reducing energy efficiency. The goal is to create an environment-friendly process for recycling residues from plastic production.
A recent study led by a Texas A&M University professor reveals that South Asia will be threatened by frequent extreme heat and high air pollution days. By 2050, occurrences of both extremes are projected to increase in frequency by 75% and 175%, respectively, posing significant risks to human health.
Research suggests a hydraulic fracturing chemical can block the effects of testosterone and other male sex hormones. The study found that exposure to this chemical could lead to abnormal reproductive function, male infertility, and disrupted testicular and prostate development.
Researchers discovered significant chemical differences between PM1 and PM2.5 aerosol species, affecting particle acidity and water content predictions. These findings are crucial for accurately modeling air pollution in highly polluted regions, where the distinction is critical.
Researchers used mathematical models to discover that leaking metabolites can provide both selfish and selfless benefits for cells. Leaking allows cells to enhance their growth by making internal chemical reactions more efficient, but it also enables a mutually beneficial exchange of nutrients with other cell types.
Researchers at University of Arizona develop mathematical model to simulate PFAS migration through soil and groundwater. The majority of PFAS accumulate in the soil, where they migrate down slowly, posing a significant risk to human health.
Researchers created a neural network that analyzes fundamental chemical laws to predict tautomerism constants and acidity. The model enhances the accuracy of physicochemical property predictions for designed medications and materials.
A new AI model predicts physicochemical properties of compounds by analyzing fundamental chemical laws, enhancing the precision of medication and material design. The model uses neural networks to forecast chemical reaction parameters.
The study estimated nitrogen deposition in northern China using a combination of remote sensing data and atmospheric chemical transport model simulations, revealing an average flux of 54.5 kg N per hectare. This represents 10% of the annual nitrogen application for winter wheat-summer maize rotation in the region.
Researchers develop algorithm that can predict complex chemical reactions and provide a 'chemical map' to desired products, reducing time in preclinical drug discovery and materials science. The model achieves high accuracy by distilling patterns of reactivity from millions of published chemical reactions.
Researchers found that when zebrafish are exposed to certain toxins, their gut sheds up to a quarter of its cells through a process called delamination. This may be an effective defense mechanism, and further study is needed to understand its implications.
Bin Wang, a University of Oklahoma assistant professor, has been awarded a five-year grant to develop an all-optical process for chemical transformation with high efficiency and selectivity. His research aims to control light-driven reactions at the molecular level using computational modeling.
Researchers have developed a computational model for human MEK1, a protein with potential as a drug target for various human cancers. The model provides insights into the structure and function of MEK1, which can help develop new classes of inhibitors for cancer therapeutics.
Researchers at Argonne National Laboratory developed a new molecular model of water using machine learning, achieving high accuracy and reducing computational cost. The coarse-grained model replicates the behavior of water at the atomic scale, with applications in fields like materials science and climate modeling.
Carla Ng will use her NSF CAREER award to study the health and environmental impact of per- and polyfluorinated alkyl substances. She aims to identify ways to eliminate them from the environment and design selective sorbents for efficient removal.