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Increases in organic carbon within agricultural soils are widely recognized as a "negative emission" that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS). [ABSTRACT FROM AUTHOR]

The C-alkylpyrogallol[4]arene macrocycles are known to form self-assembled dimeric and hexameric nanocapsules. Several conformations of alkyl-substituted pyrogallol[4]arenes have been found to be thermodynamically stable, with the most stable structures typically being a chair and cone. However, experimental evidence suggests that aryl-substituted pyrogallol[4]arenes exist in the chair conformation, implying that because for nanocapsule formation the cone is the necessary conformer of the subunits of these capsules, the chair must undergo some transition to the cone. To benchmark the intrinsic behavior of these systems, we here use quantum chemical calculations and molecular dynamics simulations to (1) investigate conformers of the R=H system and their solvent interactions to understand the structures and their relative stabilities, (2) explore interconversions between conformers, and (3) gain preliminary insight into the mechanism by which self-assembly of the nanocapsule occurs. [ABSTRACT FROM AUTHOR]

The C-alkylpyrogallol[4]arene macrocycles are known to form self-assembled dimeric and hexameric nanocapsules. Several conformations of alkyl-substituted pyrogallol[4]arenes have been found to be thermodynamically stable, with the most stable structures typically being a chair and cone. However, experimental evidence suggests that aryl-substituted pyrogallol[4]arenes exist in the chair conformation, implying that because for nanocapsule formation the cone is the necessary conformer of the subunits of these capsules, the chair must undergo some transition to the cone. To benchmark the intrinsic behavior of these systems, we here use quantum chemical calculations and molecular dynamics simulations to (1) investigate conformers of the R=H system and their solvent interactions to understand the structures and their relative stabilities, (2) explore interconversions between conformers, and (3) gain preliminary insight into the mechanism by which self-assembly of the nanocapsule occurs. [ABSTRACT FROM AUTHOR]