Neutrino Interactions in Hot and Dense Matter

We study the charged and neutral current weak interaction rates relevant for the determination of neutrino opacities in dense matter found in supernovae and neutron stars. We establish an efficient formalism for calculating differential cross sections and mean free paths for interacting, asymmetric nuclear matter at arbitrary degeneracy. The formalism is valid for both charged and neutral current reactions. Strong interaction corrections are incorporated through the in-medium single particle energies at the relevant density and temperature. The effects of strong interactions on the weak interaction rates are investigated using both potential and effective field-theoretical models of matter. We investigate the relative importance of charged and neutral currents for different astrophysical situations, and also examine the influence of strangeness-bearing hyperons. Our findings show that the mean free paths are significantly altered by the effects of strong interactions and the multi-component nature of dense matter. The opacities are then discussed in the context of the evolution of the core of a protoneutron star.Comment: 41 pages, 25 figure

Determining Transition State Geometries in Liquids Using 2D-IR

Many properties of chemical reactions are determined by the transition state connecting reactant and product, yet it is difficult to directly obtain any information about these short-lived structures in liquids. We show that two-dimensional infrared (2D-IR) spectroscopy can provide direct information about transition states by tracking the transformation of vibrational modes as a molecule crossed a transition state. We successfully monitored a simple chemical reaction, the fluxional rearrangement of Fe(CO)5, in which the exchange of axial and equatorial CO ligands causes an exchange of vibrational energy between the normal modes of the molecule. This energy transfer provides direct evidence regarding the time scale, transition state, and mechanism of the reaction

Engineered 2D Ising interactions on a trapped-ion quantum simulator with hundreds of spins

The presence of long-range quantum spin correlations underlies a variety of physical phenomena in condensed matter systems, potentially including high-temperature superconductivity. However, many properties of exotic strongly correlated spin systems (e.g., spin liquids) have proved difficult to study, in part because calculations involving N-body entanglement become intractable for as few as N~30 particles. Feynman divined that a quantum simulator - a special-purpose "analog" processor built using quantum particles (qubits) - would be inherently adept at such problems. In the context of quantum magnetism, a number of experiments have demonstrated the feasibility of this approach. However, simulations of quantum magnetism allowing controlled, tunable interactions between spins localized on 2D and 3D lattices of more than a few 10's of qubits have yet to be demonstrated, owing in part to the technical challenge of realizing large-scale qubit arrays. Here we demonstrate a variable-range Ising-type spin-spin interaction J_ij on a naturally occurring 2D triangular crystal lattice of hundreds of spin-1/2 particles (9Be+ ions stored in a Penning trap), a computationally relevant scale more than an order of magnitude larger than existing experiments. We show that a spin-dependent optical dipole force can produce an antiferromagnetic interaction J_ij ~ 1/d_ij^a, where a is tunable over 0<a<3; d_ij is the distance between spin pairs. These power-laws correspond physically to infinite-range (a=0), Coulomb-like (a=1), monopole-dipole (a=2) and dipole-dipole (a=3) couplings. Experimentally, we demonstrate excellent agreement with theory for 0.05<a<1.4. This demonstration coupled with the high spin-count, excellent quantum control and low technical complexity of the Penning trap brings within reach simulation of interesting and otherwise computationally intractable problems in quantum magnetism.Comment: 10 pages, 10 figures; article plus Supplementary Material

DFT and time-resolved IR investigation of electron transfer between photogenerated 17- and 19-electron organometallic radicals

The photochemical disproportionation mechanism of [CpW(CO){sub 3}]{sub 2} in the presence of Lewis bases PR{sub 3} was investigated on the nano- and microsecond time-scales with Step-Scan FTIR time-resolved infrared spectroscopy. 532 nm laser excitation was used to homolytically cleave the W-W bond, forming the 17-electron radicals CpW(CO){sub 3} and initiating the reaction. With the Lewis base PPh{sub 3}, disproportionation to form the ionic products CpW(CO){sub 3}PPh{sub 3}{sup +} and CpW(CO){sub 3}{sup -} was directly monitored on the microsecond time-scale. Detailed examination of the kinetics and concentration dependence of this reaction indicates that disproportionation proceeds by electron transfer from the 19-electron species CpW(CO){sub 3}PPh{sub 3} to the 17-electron species CpW(CO){sub 3}. This result is contrary to the currently accepted disproportionation mechanism which predicts electron transfer from the 19-electron species to the dimer [CpW(CO){sub 3}]{sub 2}. With the Lewis base P(OMe){sub 3} on the other hand, ligand substitution to form the product [CpW(CO){sub 2}P(OMe){sub 3}]{sub 2} is the primary reaction on the microsecond time-scale. Density Functional Theory (DFT) calculations support the experimental results and suggest that the differences in the reactivity between P(OMe){sub 3} and PPh{sub 3} are due to steric effects. The results indicate that radical-to-radical electron transfer is a previously unknown but important process for the formation of ionic products with the organometallic dimer [CpW(CO){sub 3}]{sub 2} and may also be applicable to the entire class of organometallic dimers containing a single metal-metal bond

The mechanism for iron-catalyzed alkene isomerization in solution

Here we report nano- through microsecond time-resolved IR experiments of iron-catalyzed alkene isomerization in room-temperature solution. We have monitored the photochemistry of a model system, Fe(CO){sub 4}({eta}{sup 2}-1-hexene), in neat 1-hexene solution. UV-photolysis of the starting material leads to the dissociation of a single CO to form Fe(CO){sub 3}({eta}{sup 2}-1-hexene), in a singlet spin state. This CO loss complex shows a dramatic selectivity to form an allyl hydride, HFe(CO){sub 3}({eta}{sup 3}-C{sub 6}H{sub 11}), via an internal C-H bond-cleavage reaction in 5-25 ns. We find no evidence for the coordination of an alkene molecule from the bath to the CO loss complex, but do observe coordination to the allyl hydride, indicating that it is the key intermediate in the isomerization mechanism. Coordination of the alkene ligand to the allyl hydride leads to the formation of the bis-alkene isomers, Fe(CO){sub 3}({eta}{sup 2}-1-hexene)({eta}{sup 2}-2-hexene) and Fe(CO){sub 3}({eta}{sup 2}-1-hexene){sub 2}. Because of the thermodynamic stability of Fe(CO){sub 3}({eta}{sup 2}-1-hexene)({eta}{sup 2}-2-hexene) over Fe(CO){sub 3}({eta}{sup 2}-1-hexene){sub 2} (ca. 12 kcal/mol), nearly 100% of the alkene population will be 2-alkene. The results presented herein provide the first direct evidence for this mechanism in solution and suggest modifications to the currently accepted mechanism

The critical role of logarithmic transformation in Nernstian equilibrium potential calculations

The membrane potential, arising from uneven distribution of ions across cell membranes containing selectively permeable ion channels, is of fundamental importance to cell signaling. The necessity of maintaining the membrane potential may be appreciated by expressing Ohm’s law as current = voltage/resistance and recognizing that no current flows when voltage = 0, i.e., transmembrane voltage gradients, created by uneven transmembrane ion concentrations, are an absolute requirement for the generation of currents that precipitate the action and synaptic potentials that consume >80% of the brain’s energy budget and underlie the electrical activity that defines brain function. The concept of the equilibrium potential is vital to understanding the origins of the membrane potential. The equilibrium potential defines a potential at which there is no net transmembrane ion flux, where the work created by the concentration gradient is balanced by the transmembrane voltage difference, and derives from a relationship describing the work done by the diffusion of ions down a concentration gradient. The Nernst equation predicts the equilibrium potential and, as such, is fundamental to understanding the interplay between transmembrane ion concentrations and equilibrium potentials. Logarithmic transformation of the ratio of internal and external ion concentrations lies at the heart of the Nernst equation, but most undergraduate neuroscience students have little understanding of the logarithmic function. To compound this, no current undergraduate neuroscience textbooks describe the effect of logarithmic transformation in appreciable detail, leaving the majority of students with little insight into how ion concentrations determine, or how ion perturbations alter, the membrane potential

Characterization of Fast Ion Transport via Position-Dependent Optical Deshelving

Ion transport is an essential operation in some models of quantum information processing, where fast ion shuttling with minimal motional excitation is necessary for efficient, high-fidelity quantum logic. While fast and cold ion shuttling has been demonstrated, the dynamics and specific trajectory of an ion during diabatic transport have not been studied in detail. Here we describe a position-dependent optical deshelving technique useful for sampling an ion's position throughout its trajectory, and we demonstrate the technique on fast linear transport of a $^{40}\text{Ca}^+$ ion in a surface-electrode ion trap. At high speed, the trap's electrode filters strongly distort the transport potential waveform. With this technique, we observe deviations from the intended constant-velocity (100 m/s) transport: we measure an average speed of 83(2) m/s and a peak speed of 251(6) m/s over a distance of 120 $\mu$

Conformational adaptation of Asian macaque TRIMCyp directs lineage specific antiviral activity

TRIMCyps are anti-retroviral proteins that have arisen independently in New World and Old World primates. All TRIMCyps comprise a CypA domain fused to the tripartite domains of TRIM5α but they have distinct lentiviral specificities, conferring HIV-1 restriction in New World owl monkeys and HIV-2 restriction in Old World rhesus macaques. Here we provide evidence that Asian macaque TRIMCyps have acquired changes that switch restriction specificity between different lentiviral lineages, resulting in species-specific alleles that target different viruses. Structural, thermodynamic and viral restriction analysis suggests that a single mutation in the Cyp domain, R69H, occurred early in macaque TRIMCyp evolution, expanding restriction specificity to the lentiviral lineages found in African green monkeys, sooty mangabeys and chimpanzees. Subsequent mutations have enhanced restriction to particular viruses but at the cost of broad specificity. We reveal how specificity is altered by a scaffold mutation, E143K, that modifies surface electrostatics and propagates conformational changes into the active site. Our results suggest that lentiviruses may have been important pathogens in Asian macaques despite the fact that there are no reported lentiviral infections in current macaque populations

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest