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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9

Metabolic Redox Coupling Controls Methane Production in Permafrost‐Affected Peatlands Through Organic Matter Quality‐Dependent Energy Allocation

ABSTRACT Permafrost thaw represents one of Earth's largest climate feedback risks, potentially releasing vast carbon (C) stores as greenhouse gases (GHG). However, our ability to predict emissions remains limited by poor understanding of how changing organic matter (OM) composition affects microbial carbon processing. We test a metabolism‐centered redox framework, which views microbial processes as coupled oxidative‐reductive reactions, to mechanistically explain how organic matter metabolite quality controls greenhouse gas production in permafrost‐affected peatland ecosystems. Rather than relying solely on geochemical redox measurements, our approach examines how microbes balance electron flow through metabolic pathways. Using active layer peat (9–19 cm) from contrasting environments (bog and fen), we employed multi‐omics approaches, including metabolomics, metagenomics, and metatranscriptomics, to link OM chemistry to microbial function. Our results reveal distinct dissolved organic matter metabolite composition, with fen systems enriched in compounds with higher substrate quality (low molecular weight (MW) sugars with high H:C ratios and low aromaticity) and bog systems dominated by compounds with lower substrate quality (high MW phenols with lower H:C ratios and higher aromaticity). In fen samples, these sugar‐like compounds correlated with higher oxidative metabolism and methanogenesis, supported by increased glycolysis gene expression. Initially, electrons from increased oxidative metabolism were balanced through nitrate and sulfate reduction, but as these electron acceptors were depleted, methanogenesis increased to maintain redox balance. Fen samples showed rapid degradation of both high‐ and low‐substrate‐quality compounds, suggesting sufficient energy for efficient C cycling. Conversely, bog samples exhibited more polyphenolic compounds, lower glycolysis activity, and higher stress‐related gene expression, suggesting energy was diverted towards cell maintenance under acidic conditions rather than C processing. This approach suggests that predicting greenhouse gas emissions requires an understanding of how organic matter quality shapes microbial energy allocation strategies, providing a mechanistic framework for improving emission predictions from permafrost‐affected peatlands and similar ecosystems.

Biodiversity & Conservation↗

System size and energy dependence of the mean transverse momentum fluctuations at the LHC

Event-by-event fluctuations of the event-wise mean transverse momentum, $\langle$p T $\rangle$, of charged particles produced in proton–proton (pp) collisions at $\sqrt{s}$ = 5.02 TeV, Xe–Xe collisions at $\sqrt{s_{NN}}$ = 5.44 TeV, and Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV are studied using the ALICE detector based on the integral correlator $\langle$$\langle$Δp T Δp T $\rangle$$\rangle$. The correlator strength is found to decrease monotonically with increasing produced charged-particle multiplicity measured at midrapidity in all three systems. In Xe–Xe and Pb–Pb collisions, the multiplicity dependence of the correlator deviates significantly from a simple power-law scaling as well as from the predictions of the HIJING and AMPT models. The observed deviation from power-law scaling is expected from transverse radial flow in semicentral to central Xe–Xe and Pb–Pb collisions. In pp collisions, the correlation strength is also studied by classifying the events based on the transverse spherocity, S 0 , of the particle production at midrapidity, used as a proxy for the presence of a pronounced back-to-back jet topology. Low-spherocity (jetty) events feature a larger correlation strength than those with high spherocity (isotropic). The strength and multiplicity dependence of jetty and isotropic events are well reproduced by calculations with the PYTHIA 8 and EPOS LHC models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

SoW: Energy-Dependent Fission Product Yields

The purpose of this LLNL subcontract is to capitalize on our already established long-term relationship with Duke University and Triangle Universities Nuclear Laboratory (TUNL) to study fission observables like Fission Product Yields (FPY), fission cross-section and other data relevant to our programmatic needs. LLNL with Duke university partnership, wants to further develop new experimental capabilities to measure critical nuclear data important for LLNL stakeholders. The new FPY data will provide important benchmarks for nuclear theory and evaluation currently developed by the nuclear data and theory group. The improved FPY data is of interest to many major laboratory programs, including nuclear chemistry, nuclear forensics, safeguard, and nonproliferation communities. The impact of new FPY data, obtained by a graduate student from Duke (Aitor Bracho), will result in the U.S. Nuclear Data Program.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Energy Dependent Fission Product Yields

This project utilized a 10-meter Fast Transfer System (called RABITTS) and Decay Station. FPYs are measured using neutron activation of U-235 and Pu-239 followed by gamma ray spectroscopy. We irradiated targets with mono-energetic neutrons produced at the TUNL tandem accelerator laboratory. The gamma spectra collected in these target irradiations are being analyzed to determine FPY values.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

FORTRAN 4 programs for the extraction of potential well parameters from the energy dependence of total elastic scattering cross sections

An attempt has been made to keep the programs as subroutine oriented as possible. Usually only the main programs are directly concerned with the problem of total cross sections. In particular the subroutines POLFIT, BILINR, GASS59/MAXLIK, SYMOR, MATIN, STUDNT, DNTERP, DIFTAB, FORDIF, EPSALG, REGFAL and ADSIMP are completely general, and are concerned only with the problems of numerical analysis and statistics. Each subroutine is independently documented.

Labudde, R. A.↗