Search for: All records

This study investigates mechanisms that generate regularly spaced iron-rich bands in upland soils. These striking features appear in soils worldwide, but beyond a generalized association with changing redox, their genesis is yet to be explained. Upland soils exhibit significant redox fluctuations driven by rainfall, groundwater changes, or irrigation. Pattern formation in such systems provides an opportunity to investigate the temporal aspects of spatial self-organization, which have been heretofore understudied. By comparing multiple alternative mechanisms, we found that regular iron banding in upland soils is explained by coupling two sets of scale-dependent feedbacks, the general principle of Turing morphogenesis. First, clay dispersion and coagulation in iron redox fluctuations amplify soil Fe(III) aggregation and crystal growth to a level that negatively affects root growth. Second, the activation of this negative root response to highly crystalline Fe(III) leads to the formation of rhythmic iron bands. In forming iron bands, environmental variability plays a critical role. It creates alternating anoxic and oxic conditions for required pattern-forming processes to occur in distinctly separated times and determines durations of anoxic and oxic episodes, thereby controlling relative rates of processes accompanying oxidation and reduction reactions. As Turing morphogenesis requires ratios of certain process rates to be within a specific range, environmental variability thus modifies the likelihood that pattern formation will occur. Projected changes of climatic regime could significantly alter many spatially self-organized systems, as well as the ecological functioning associated with the striking patterns they present. This temporal dimension of pattern formation merits close attention in the future. 

ABSTRACT Environmental scientists are increasingly returning to Mössbauer spectroscopy (MBS) to reveal details about iron (Fe)‐bearing phases in soils and sediments. MBS is particularly powerful at distinguishing between Fe(II) and Fe(III) and, given appropriate background information, can offer exceptionally precise information on Fe speciation in compositionally complex environmental samples. However, there are relatively few accessible guides for analyzing environmental samples by MBS. In this review, we seek to distill the essential understanding of MBS for earth scientists and provide guidance on analysis, spectral fitting, and interpretation for new practitioners and a consolidation of approaches for experienced users. As a rule, Fe phases in soils and sediments are more disordered and complex than synthetic or geogenic Fe minerals. We cover the most successful ways MBS can be applied to soils, including the determination of Fe(II)/Fe(III) ratios, characterization of Fe (oxyhydr)oxide crystallinity, and the use of⁵⁷Fe isotope spikes, as well as highlighting how to avoid common pitfalls and arrive at Fe phase identification and quantification by leveraging complimentary data and environment context. We outline procedures for sample preparation, analysis, and spectral fitting using decision trees based on the analytical goals and sample conditions. The fitting and interpretation of magnetically ordered ferrous phases at low temperature is lacking in the literature and so we offer an expanded discussion of approaches to these challenging spectra. We provide a discussion and fitting guidance for the most common Fe phases in soils and sediments organized around environmental contexts: young soils (and sediments derived from them) dominated by aluminosilicates, highly weathered soils rich in Fe oxides, organic‐rich soils, soils in sulfur‐rich environments, and soils exposed to anoxia. For each context, we describe expected Fe phases and their characteristic spectral features while emphasizing the importance of complementary analyses for reliable interpretation. Finally, we identify two critical needs in the field: improved theoretical frameworks for fitting low‐temperature ferrous octets and Fe–sulfur phases and a need for standardization of parameter reporting and data sharing within the environmental MBS community. This review aims to both facilitate broader adoption of MBS in the environmental sciences and advance the technique's application to complex natural samples. 

Abstract Multiprincipal-element alloys are an enabling class of materials owing to their impressive mechanical and oxidation-resistant properties, especially in extreme environments^1,2. Here we develop a new oxide-dispersion-strengthened NiCoCr-based alloy using a model-driven alloy design approach and laser-based additive manufacturing. This oxide-dispersion-strengthened alloy, called GRX-810, uses laser powder bed fusion to disperse nanoscale Y₂O₃particles throughout the microstructure without the use of resource-intensive processing steps such as mechanical or in situ alloying^3,4. We show the successful incorporation and dispersion of nanoscale oxides throughout the GRX-810 build volume via high-resolution characterization of its microstructure. The mechanical results of GRX-810 show a twofold improvement in strength, over 1,000-fold better creep performance and twofold improvement in oxidation resistance compared with the traditional polycrystalline wrought Ni-based alloys used extensively in additive manufacturing at 1,093 °C^5,6. The success of this alloy highlights how model-driven alloy designs can provide superior compositions using far fewer resources compared with the ‘trial-and-error’ methods of the past. These results showcase how future alloy development that leverages dispersion strengthening combined with additive manufacturing processing can accelerate the discovery of revolutionary materials. 

After 4.5 billion years as an evolving and dynamic planet, the Earth continues to evolve but with human‐altered dynamics. Earth scientists have special opportunities and responsibilities to accelerate our understanding of Earth's changes that are transforming our most remarkable home. 

We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal. 

Abstract Aluminum (Al)‐bearing and iron (Fe)‐bearing minerals, especially short‐range‐ordered (SRO) phases, are thought to protect soil organic C (SOC). However, it remains methodologically challenging to assess the influence of Al vs. Fe minerals or metal complexes. Whereas SRO Al and Fe phases share some properties, Al dissolved by oxalate (Al_ox) often correlates stronger with SOC than Fe dissolved by oxalate (Fe_ox) or citrate–dithionite (Fe_cd). To further evaluate these relationships, we analyzed a large North American soil dataset from the National Ecological Observatory Network. A strong relationship between Al_oxand SOC (and weaker Fe_ox‐SOC relationship) persisted even after excluding soils rich in SRO minerals (Andisols and Spodosols). Al dissolved by oxalate was strongly correlated with citrate–dithionite‐extractable Al (Al_cd; slope = 0.92,R² = .69), and discrepancies could be explained (R² = .87) by greater dissolution of Al‐substituted Fe phases by citrate–dithionite and greater dissolution of aluminosilicates by oxalate. Aluminum dissolved by oxalate and Al_cdwere both strong SOC predictors despite their differing relationships with silicon (Si). Al dissolved by oxalate and Si_oxstrongly covaried (R² = .79), but Al_cdwas inconsistently related to Si_cd(R² = .18). Similar relationships of Al_oxand Al_cdwith SOC, despite differences in minerals extracted by oxalate and citrate–dithionite, suggest that Al‐OC complexes (as opposed to aluminosilicate or iron‐bearing minerals) were the best SOC predictor. This raises important questions: do Al‐OC complexes indicate protection from decomposition or simply reflect greater intensity of mineral weathering by organic acids; and, if the latter, then perhaps SOC input is driving Al_oxand SOC correlations rather than Al phase composition or abundance.