The complex interactions between magnetite and aqueous Fe²⁺ (Fe²⁺_(aq)) pertain to many biogeochemical redox processes in anoxic subsurface environments. The effect of natural organic matter, abundant in these same environments, on Fe²⁺_(aq)-magnetite interactions is an additional complex that remains poorly understood. We investigated the influence of a model quinone molecule anthraquinone-2,6-disulfonate (AQDS) on Fe²⁺_(aq)-magnetite interactions by systematically studying equilibrium Fe²⁺_(aq) concentrations, rates and extents of AQDS reduction, and structural versus surface-localized Fe(II)/Fe(III) ratios in magnetite under different controlled experimental conditions. The equilibrium concentration of Fe²⁺_(aq) in Fe²⁺-amended magnetite suspensions with AQDS proportionally changes with solution pH or initial AQDS concentration, but independent of magnetite loadings through the solid concentrations that were studied here. The rates and extents of AQDS reduction by Fe²⁺-amended magnetite proportionally increased with solution pH, magnetite loading, and initial Fe²⁺_(aq) concentration, which correlates with the corresponding change of reduction potentials for the Fe²⁺-magnetite system. AQDS reduction by surface-associated Fe(II) in the Fe²⁺-magnetite suspensions induces solid-state migration of electron equivalents from particle interiors to the near-surface region and the production of nonmagnetic Fe(II)-containing species, which inhibits Fe²⁺_(aq) incorporation or electron injection into the magnetite structure. This study demonstrates the significant influence of quinones on reductive activity of the Fe²⁺-magnetite system.

Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.