(AGENPARL) – ven 09 dicembre 2022 A weekly compendium of media reports on science and technology achievements at Lawrence Livermore National Laboratory. Though the Laboratory reviews items for overall accuracy, the reporting organizations are responsible for the content in the links below.
LLNL Report, Dec. 9, 2022
Mark Dreyer (front, left) and Roald Leif examine the injection port of the high-resolution liquid chromatography mass spectrometer right after the injection of a fentanyl sample while Carlos Valdez (back, left) and Todd Corzett monitor the analysis for the opioid. Photo by Garry McLeod/LLNL.
[It’s in the blood](https://medicalxpress.com/news/2022-12-technique-fentanyl-blood-urine.html)
A team of Lawrence Livermore National Laboratory (LLNL) scientists has developed a new technique to analyze fentanyl in human blood and urine samples that could aid work in the fields of medicine and chemical forensics.
“This technique is important because it allows a scientist to not only detect intact fentanyl in biological tissues, but it also provides a way to confirm its presence in the sample by chemically modifying the opioid,” said LLNL synthetic chemist Carlos Valdez.
Developed by a team of researchers led by Belgian scientist Paul Janssen, fentanyl has become one of the most employed opioids in the field of medicine. It is used in surgical procedures as well as for the management of pain in certain disease states.
However, the benefits of fentanyl use in medicine have been overshadowed by its use in hundreds of thousands of overdose deaths due to its illicit use, along with other similar analogs such as acetyl fentanyl.
[Read More](https://medicalxpress.com/news/2022-12-technique-fentanyl-blood-urine..html)
NIF’s 192 high-energy laser beams converge at the center of the Target Chamber to implode a tiny hydrogen fuel pellet and spark a thermonuclear fusion reaction.
LLNL researchers have discovered that a new magnetic field setup more than tripled the energy output of the fusion reaction hotspot in experiments, “approaching” the level required for self-sustaining ignition in plasmas. The field was particularly effective at trapping heat within the hotspot, boosting the energy yield.
The hotspot’s creation involved blasting 200 lasers at a fusion fuel pellet made from hydrogen isotopes like deuterium and tritium. The resulting X-rays made the pellet implode and thus produce the extremely high pressures and heat needed for fusion. The team achieved their feat by wrapping a coil around a pellet made using special metals.
The notion of using magnets to heat the fuel isn’t new. University of Rochester scientists found they could use magnetism to their advantage in 2012. The Lawrence Livermore study was far more effective, however, producing 40 percent heat and more than three times the energy.
LLNL researchers used the new high-throughput stable isotope probing pipeline and metagenomics to get the first look at the active microbiome surrounding a beneficial plant symbiont, arbuscular mycorrhizal fungi.
[Dishing the dirt](https://phys.org/news/2022-11-method-unearths-soil-microbial-interactions.html)
Linking the identity of wild microbes with their physiological traits and environmental functions is a key aim for environmental microbiologists. Of the techniques that strive for this goal, Stable Isotope Probing — SIP — is considered the most effective for studying active microorganisms in natural settings.
Lawrence Livermore scientists have developed a new technique — high-throughput SIP — that automates several steps in the process of stable isotope probing, allowing investigations of microbial activity of microorganisms under realistic conditions, without the need for lab culturing.
In SIP, active microbes are identified via incorporation of stable isotopes into their biomass. It is among the most powerful methods in microbial ecology since it can identify active microbes and their physiological traits (substrate use, cellular biochemistry, metabolism, growth, mortality) in complex communities under native conditions.
[Read More](https://phys.org/news/2022-11-method-unearths-soil-microbial-interactions.html)
An artist’s rendering shows how polymers can be represented as graphs for the machine learning model and how subtle changes in the connectivity and periodicity of polymers can have dramatic effects on their predicted properties; in this case the glass transition temperature. Credit: Eric Smith/LLNL.
[A more accurate prediction](https://novonite.com/ml-model-instantly-predicts-polymer-properties/)
Hundreds of millions of tons of polymer materials are being produced worldwide for use in a vast and expanding field of application with new material demands such as green chemical polymers, consumer packaging, adhesives, auto parts, fabrics and solar cells .
But discovering suitable polymer materials for use in these applications lies in accurately predicting the properties a candidate material will have. Gaining a quantitative understanding of the relationship between chemical structure and observable properties is particularly challenging for polymers, due to their complex 3D chemical assembly that can consist of extremely long chains of thousands of atoms.
Recently, a LLNL team of materials and computer scientists tackled this challenge with a data-driven approach. Using datasets of polymer properties, the researchers developed a new machine-learning (ML) model that can predict 10 different polymer properties more accurately than was possible with previous ML models.
[Read More](https://novonite.com/ml-model-instantly-predicts-polymer-properties/)
New research provides an improved understanding of the causes of historical changes in climate and increases confidence in model simulations of continued global warming over the 21st century. Image courtesy of NASA.
[It’s only natural](https://phys.org/news/2022-11-analysis-differences-satellites-climate.html)
Satellite observations and computer simulations are important tools for understanding past changes in Earth’s climate and for projecting future changes.
However, satellite observations consistently show less warming than climate model simulations from 1979 to the present, especially in the tropical troposphere (the lowest ~15 kilometers of Earth’s atmosphere). This difference has raised concerns that models may overstate future temperature changes.
Rather than being an indicator of fundamental model errors, the model-satellite difference can largely be explained by natural fluctuations in Earth’s climate and imperfections in climate-model forcing agents, according to new research by Lawrence Livermore National Laboratory (LLNL) scientists.
“Natural climate variability appears to have partly masked warming over the satellite era,” said Stephen Po-Chedley, a LLNL climate scientist.
[Read More](https://phys.org/news/2022-11-analysis-differences-satellites-climate.html)
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