Energy dependence of heavy-ion initial condition in isobar collisions
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The use of carbon supports for late transition-metal nanoparticle catalysts has grown substantially in recent years due to efforts to develop electrocatalysts for clean energy applications and catalysts for new aqueous-phase biomass-related conversions and due to the evolution of new carbon materials with unique properties (e.g., graphene, carbon nanotubes, and so forth). However, much less is known about the bonding energetics of catalytic metal nanoparticles on carbon supports in comparison with oxide supports, which are more common for thermal catalysis. Here, we report the growth morphology and heats of adsorption of Ni vapor deposited onto graphene/Ni(111) at 300 K and 100 K using metal vapor single-crystal adsorption calorimetry and He + low-energy ion scattering (LEIS). These results provide the Ni chemical potential versus particle size, and the Ni/graphene adhesion energy. LEIS intensities suggest that Ni grows as flat-topped face-centered cubic islands with a nearly constant thickness of ~1.5 nm when deposited at 300 K. At 100 K, Ni grows as smaller nanoparticles, well modeled as hemispherical hexagonal close-packed nanoparticles with a density of ~2 × 10 16 particles/m 2 . The Ni chemical potential as a function of average particle diameter in the 0.5 to 4 nm range at 100 K was determined from the heats of Ni gas adsorption. Further, by fitting the measured chemical potential as a function of diameter, we determined an adhesion energy of 3.6 J/m 2 for large Ni particles on graphene/Ni(111). This adhesion energy is in good agreement with previous scanning tunneling microscopy and density functional theory investigations of Ni/graphene/Ni(111).
Carbon-supported late transition-metal nanoparticles are promising catalysts and electrocatalysts for wide-ranging applications. However, experimental investigations of the bonding energetics of metal nanoparticles on carbon supports are very limited. Here, in this work, we report heats of adsorption of Pd vapor deposited onto single-layer graphene(0001) supported on Ni(111) at 100 and 300 K as Pd grows particles of well-defined size in the range from three atom clusters to 6 nm diameter. Sizes were determined from He + low-energy ion scattering (LEIS). In this size range, the differential heat of Pd adsorption increases from 228 kJ/mol to within 10 kJ/mol of the heat of sublimation of bulk Pd (377 kJ/mol). The chemical potential of metal atoms in these nanoparticles as a function of average particle size was determined from these results. The adhesion energy at the Pd/graphene(0001)/Ni(111) interface was extracted from these data and found to be 3.5 J/m 2 for the largest Pd particles. For the three metal elements that have now been studied (Pd, Ni, and Ag), we found an increase in metal/graphene(0001)/Ni(111) adhesion energy with metal carbophilicity, which we defined here as the heat of C atom adsorption on that metal’s (111) surface estimated from published density functional theory calculations.
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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.
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.
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.
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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.
PFUNS was an integral experiment performed at the National Criticality Experiments Research Center (NCERC) by Los Alamos National Laboratory in 2024.
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Explore the source record for details and available documents.