The surface chemistry of pyrrhotites from intact particles directly collected from asteroid (162173) Ryugu was investigated by micro-Raman spectroscopy. The Raman peak characteristic to pyrrhotite was observed at around 115 cm-1 in Ryugu pyrrhotites, similar to freshly cleaved surfaces of terrestrial pyrrhotites. Additional Raman bands centered at around 220, 275, and 313 cm-1 with broadened features were also detected from the Ryugu pyrrhotites. The set of Raman bands at 220 and 275 cm-1 was assigned to typical Fe-S stretching vibrations of nu 2 (225 cm-1 ) and nu 1 (275 cm-1 ). These bands are not clearly observed in bulk crystals of pyrrhotite but appear in its nanoparticulate phase. These bands are ordinarily seen in amorphous monosulfides that formed under low oxygen fugacity ( f O 2 ) conditions in nature, indicating that the structural alteration of pyrrhotite surfaces occurred heterogeneously on the nanoscale under low f O 2 conditions. Further, the Raman band at 313 cm-1 was attributed to a characteristic tetrahedral bonding of Fe(III) in the lattice of Fe II 1-3x Fe III 1-2x S, followed by the local breakdown of the crystal lattice structures from planar bonding with Fe(II). In addition, some areas of the Ryugu pyrrhotite grains showed corroded structures with iridescence. Furthermore, assemblages of magnetite particles were also preferentially observed on small areas of the likely-dissolved pyrrhotite crystals in phyllosilicate matrices. These characteristic features in the Raman spectra and in corroded structures of Ryugu pyrrhotites record changes in the local environmental conditions via aqueous alteration. The corrosion of pyrrhotite crystals followed by the preferential formation of magnetite particles by asteroidal water is the likely product of dissolution of Fe(II) from the pyrrhotite surface and its oxidative precipitation in microchemical environments on the Ryugu parent body.

Background Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample.Methodology More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-the-art deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets.Result Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools have the potential to improve identification performance relative to using the tools individually.Conclusion We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content).

Updates and corrections are made to a number of numerical entries and their references in Table 1 of the published article. Updated plots of the final run of the comparison of the tip of the red giant branch (TRGB) and Cepheid distances with metallicity, as seen in the lower panels of Figures 1 and 2 in the published article, are shown in the composite figure given here (Figure 1). The main conclusion of the published article, that there is no statistically significant correlation of the zero-point of the Cepheid period-luminosity relation with metallicity, is unchanged. The updated "statistically flat" regression is now found to be Delta mu (o)(Cepheid - TRGB) = -0.028 (+/- 0.019) x ([O/H] - 8.50) - 0.014 (+/- 0.042).

Net-zero chemical production can be achieved through electrification, biomass-based processes, and carbon capture, utilization, and storage. However, these net-zero pathways require more resources than business-as-usual processes. One possibility to produce net-zero chemicals at a lower resource consumption is the combination of net-zero pathways based on locally available resources. This study determines the optimal combinations of net-zero pathways for producing chemicals with net-zero emissions that minimize the use of renewable energy, land, and water while complying with local waste biomass and CO2 storage availability. Waste biomass is defined as residue biomass that does not compete for land and water with other sectors. The analysis is performed worldwide at the country level and considers the production of ammonia, methanol, and plastics, which, when combined, account for similar to 5% of the global CO2 emissions. Findings show that, when considering net-zero pathways individually, waste biomass is preferably used for producing ammonia and methanol, whereas carbon capture and storage is preferably deployed for plastics production. At the same time, a mixed strategy using carbon capture, utilization, and storage, and waste biomass, allows one to achieve a net-zero chemical industry with a nearly 60% reduction in energy consumption and 90% reduction in land and water consumption, with respect to single-pathway strategies. Finally, we find that adopting a net-zero portfolio that minimizes water allows water consumption to be reduced by more than 90% and land consumption to be reduced by more than 70% at the cost of an energy increase of only 5%, when compared to the minimum-energy portfolio.

A stylized macro-scale energy model of least-cost electricity systems relying only on wind and solar generation was used to assess the value of different storage technologies, individually and combined, for the contiguous U.S. as well as for four geographically diverse U.S. load-balancing regions. For the contiguous U.S. system, at current costs, when only one storage technology was deployed, hydrogen energy storage produced the lowest system costs, due to its energy-capacity costs being the lowest of all storage technologies modeled. Additional hypothetical storage technologies were more cost-competitive than hydrogen (long-duration storage) only at very low energy-capacity costs, but they were more cost-competitive than Li-ion batteries (short-duration storage) at relatively high energy- and power-capacity costs. In all load-balancing regions investigated, the least-cost systems that included long-duration storage had sufficient energy and power capacity to also meet short-duration energy and power storage needs, so that the addition of short-duration storage as a second storage technology did not markedly reduce total system costs. Thus, in electricity systems that rely on wind and solar generation, contingent on social and geographic constraints, long-duration storage may cost-effectively provide the services that would otherwise be provided by shorter-duration storage technologies.

We present ultraviolet/optical/near-infrared observations and modeling of Type II supernovae (SNe II) whose early time (delta(t) < 2 days) spectra show transient, narrow emission lines from shock ionization of confined (r < 10(15) cm) circumstellar material (CSM). The observed electron-scattering broadened line profiles (i.e., IIn-like) of H i, He i/ii, C iv, and N iii/iv/v from the CSM persist on a characteristic timescale (t(IIn)) that marks a transition to a lower-density CSM and the emergence of Doppler-broadened features from the fast-moving SN ejecta. Our sample, the largest to date, consists of 39 SNe with early time IIn-like features in addition to 35 "comparison" SNe with no evidence of early time IIn-like features, all with ultraviolet observations. The total sample includes 50 unpublished objects with a total of 474 previously unpublished spectra and 50 multiband light curves, collected primarily through the Young Supernova Experiment and Global Supernova Project collaborations. For all sample objects, we find a significant correlation between peak ultraviolet brightness and both t(II)n and the rise time, as well as evidence for enhanced peak luminosities in SNe II with IIn-like features. We quantify mass-loss rates and CSM density for the sample through the matching of peak multiband absolute magnitudes, rise times, t(IIn), and optical SN spectra with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium radiative-transfer simulations. For our grid of models, all with the same underlying explosion, there is a trend between the duration of the electron-scattering broadened line profiles and inferred mass-loss rate: t(IIn) approximate to 3.8[M/ (0.01 M-circle dot yr(-1))] days.