Soil salinization is of great concern in the irrigated arid and semi-arid western United States due to its threat to sustainable agricultural productivity and thus is closely monitored. A widely accepted and traditional standard method for estimating soil salinity is the electrical conductivity of the saturated paste extracts (ECe). However, this method underestimates salinity due to ion pair formation in high ionic strength solution. Numerous studies have recommended the use of an electromagnetic induction (EMI) sensing technique to monitor field-scale soil salinity due to rapidness and non-destructiveness of the sampling. However, because the EMI measurement (ECa) is related to a host of soil properties, calibrating ECa to salinity in a non-homogeneous setting is particularly challenging. The main objective of this study is to understand the dynamics and interacting factors controlling soil salinity using an EMI sensor. Specifically, a correction is made for the underestimation of soil salinity from saturated paste extracts, and a calibration model is developed that is capable of predicting salinity directly from ECa despite the non-homogeneity of potential perturbing factors. A comparison is made of salinity measurement methods based on soil saturated pastes with respect to specific soil management goals. Results show that ion pairing exists even in low ionic strength solution and by diluting the saturated paste extracts to conductivities ≤ 0.03 dS m -1 (ECed), ion pairing is minimized. An improved salinity estimate is obtained by computing total dissolved solids (TDS, in mM) from the ECed values, and then multiplying the TDS by the dilution factor. We also developed a calibration model using quantile regression, which makes no assumption about the distribution of the errors, and which is capable of predicting low range soil salinity (such as that in calcareous soils) from ECa depth-weighted measurements (ECH25ECe). A comparison of ECe, ECed, ECH25ECe, and direct measurement of EC in soil pastes (“ Bureau of Soils Cup ” method, ECcup) across six depths, three texture groups, and the combinations of EC method and depth or texture groups, supports the use of the ECH25ECe method to rapidly and reliably monitor salinity in calcareous soils of arid and semiarid regions.

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第21887号 / エネ博第388号 / 新制||エネ||75(附属図書館) / 京都大学大学院エネルギー科学研究科エネルギー基礎科学専攻 / (主査)教授 齊藤 泰司, 教授 横峯 健彦, 准教授 伊藤 啓 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DFAM

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The pursuit of sustainable and high-performance materials is of utmost significance in driving the progress of battery technologies. Solid-state technology represents a promising avenue for the development of batteries with improved sustainability and performance. In this context, the present study delves into the examination of composition and the substitution of oxygen with nitrogen within the 50Li2O-xSiO2-(50-x)P2O5 glass system, specifically as applied to Solid-State electrolytes. The objective is to evaluate the influence of these factors on the electrical properties of the glass and their potential implications for Solid-State battery technology. The glass matrix was obtained through the melt-quenching technique, followed by comprehensive characterization using electrochemical impedance spectroscopy. The influence of varying silica content on the conductivity of the glass was investigated. This led to the selection of the glass system with the highest conductivity for further experiments involving nitridation. Subsequent experiments on nitridation aimed to explore the impact of nitrogen incorporation on the conductivity of the glass. By systematically varying the nitrogen content at different temperatures, the study sought to elucidate the relationship between nitrogen content and the resulting increase in glass conductivity.    The study reveals a noteworthy finding regarding the impact of nitrogen content on the conductivity of the glass. Specifically, when the nitrogen content was increased, the conductivity increased. In the case of a similar glass composition in pellet form, the conductivity at room temperature increased from Log σ = -8,009 (for glass without nitrogen) to Log σ = -6,951 (for nitrided glass). Additionally, the introduction of nitrogen into the glass resulted in a decrease in activation energy, being reduced from 0,66 eV (for oxide glass) to 0,60 eV (for oxynitride glass). These results indicate a clear correlation between increased nitrogen content and enhanced electrical properties of the investigated glasses; although obtaining a homogeneous bulk glass after nitridation was not feasible. Therefore, the nitrided samples were pelletized and sintered under different thermal conditions to obtain characterizable samples. The findings suggest that nitrogen substitution could be a promising approach for enhancing the electrical properties of the glasses of the title system of composition. Further investigation is required to optimize the process and achieve homogeneous bulk oxynitride glass.

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The objectives of this research were to 1) monitor in-situ moisture and diffusivity for both conventional concrete and concrete containing pre-wetted lightweight fine aggregate (LWFA), 2) compare deck performance in terms of early-age cracking, compressive strength, and chloride ingress, and 3) compare concrete properties in terms of compressive strength, chloride permeability, elastic modulus, and water content in the laboratory using cylinders cast in the field at the time of deck construction. The research involved field and laboratory evaluations of four newly constructed bridge decks located in northern Utah, two constructed using conventional concrete and two constructed using pre-wetted LWFA to promote internal curing. Data from sensors embedded in the concrete decks indicate that the moisture content of the internally cured concrete was consistently 1.5 to 4 percentage points higher than the moisture content of the conventional concrete for the first 6 months following deck construction. By 1 year, however, the internally cured concrete showed little difference in moisture content compared to the conventional concrete. While the internally cured concrete decks had a higher average moisture content, the electrical conductivity values were not consistently higher than those measured on the conventional concrete decks during the first approximately 8 to 10 months. However, after 8 to 10 months, both internally cured concrete decks exhibited higher electrical conductivity values than those measured on the conventional concrete decks. Laboratory compressive strength data indicate that, for the first 6 months following deck construction, the two concrete mixtures exhibited very similar strength gain characteristics. However, at 1 year, the conventional concrete was stronger by an average of 12.9 percent, or nearly 900 psi, than the internally cured concrete. In rapid chloride permeability testing, the internally cured concrete consistently passed between 13.1 and 17.5 percent less current than that passed by the conventional concrete. Laboratory free-free resonant testing at 1 year showed that the modulus of the internally cured concrete was 3.9 percent lower, on average, than that of the conventional concrete. For the tested specimens, the moisture content of the internally cured concrete was 0.5 percentage points higher, on average, than that of the conventional concrete. In the field, Schmidt rebound hammer testing showed that the internally cured concrete was neither consistently stronger nor weaker than the conventional concrete. On average, the internally cured concrete exhibited higher chloride concentrations than the conventional concrete. On average, the conventional concrete bridge decks had 4.6, 21.5, and 2.8 times more cracking than the internally cured concrete decks at 5 months, 8 months, and 1 year, respectively. At 1 year, very distinctive reflection cracks from the joints between the underlying pre-cast half-deck panels were observed on all of the decks.

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Yes / Archaeological research at Stonehenge (UK) is increasingly aimed at understanding the dynamic of the wider archaeological landscape. Through the application of state-of-the-art geophysical techniques, unprecedented insight is being gathered into the buried archaeological features of the area. However, applied survey techniques have rarely targeted natural soil variation, and the detailed knowledge of the palaeotopography is consequently less complete. In addition, metallic topsoil debris, scattered over different parts of the Stonehenge landscape, often impacts the interpretation of geophysical datasets. The research presented here demonstrates how a single multi-receiver electromagnetic induction (EMI) survey, conducted over a 22 ha area within the Stonehenge landscape, offers detailed insight into natural and anthropogenic soil variation at Stonehenge. The soil variations that were detected through recording the electrical and magnetic soil variability, shed light on the genesis of the landscape, and allow for a better definition of potential palaeoenvironmental and archaeological sampling locations. Based on the multi-layered dataset, a procedure was developed to remove the influence of topsoil metal from the survey data, which enabled a more straightforward identification of the detected archaeology. The results provide a robust basis for further geoarchaeological research, while potential to differentiate between modern soil disturbances and the underlying sub-surface variations can help in solving conservation and management issues. Through expanding this approach over the wider area, we aim at a fuller understanding of the human–landscape interactions that have shaped the Stonehenge landscape.

Digital soil mapping (DSM) provides a cost-effective approach for characterizing the spatial variation in soil properties which contributes to inconsistent productivity. This study utilized Random Forest (RF) models to facilitate DSM of apparent soil electrical conductivity (ECa), estimated cation exchange capacity (CEC), and soil organic matter (SOM) in agricultural fields across the Lower Mississippi Alluvial Valley. The RF models were trained and tested using in situ collected ECa, CEC, and SOM data, paired with a bare soil composite of Landsat 9 imagery. Field data and imagery were collected during the study period of 2019 through 2023. Models ranged from fair to moderate in accuracy (R2 from 0.27 to 0.68). The contrasting performance between CEC/SOM and ECa models is likely due to the dynamic nature of soil properties. Accordingly, models could have benefitted from covariates such as soil moisture, topography, and climatic factors, or higher spectral resolution imagery, such as hyperspectral.