Mines Repository (Colorado School of Mines)
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Evaluating the impact of phase modifiers on trivalent f-element coordination and organic phase composition in solvent extraction
No full textIncludes bibliographical references.2024 Spring.Efforts to address climate change depend on enhancing separations of f-elements, but challenges emerge because of their comparable physical and chemical properties. Intra-group separations (within Ln or An) can be achieved by utilizing size-based separations that exploit the decrease in ionic radii across the period. Meanwhile, successful inter-group separations (between An and Ln) have been achieved by employing ligands containing softer nitrogen or sulfur donor atoms. Different extractants are typically chosen based on whether the targeted separation is intra-group or inter-group, but additional methods to enhance f-element separations remain the same. Selectivity in solvent extraction is determined by both inner-sphere coordination (choice of extractant) and long-range dispersion interactions (choice of solvent). Traditionally, modifications to extractant design involve adjusting the functional group responsible for metal coordination or the non-polar alkyl tails to improve separations. Since selectivity depends on both inner-sphere coordination and long-range dispersion interactions, adding a phase modifier to the diluent – an organic molecule slightly more polar than the diluent – may offer a simpler way to enhance f-element separations.
This work examines how phase modifiers alter the composition of the organic phase and affect the complexation of f-elements, shedding light on the structural changes induced by these modifiers. The impact of alcohol alkyl chain length on the extraction of Ln, H2O, and HNO3 in TODGA in n-dodecane is explored, alongside the influence of these alcohols on vibrational spectra. Furthermore, the implications of alcohol structure and inorganic acids on extraction and Ln inner-sphere complexation in TODGA in n-dodecane are investigated. The methodologies developed to study the role of phase modifiers in TODGA systems are applied to ADAAM-EH systems to investigate the effects of phase modifiers on f-element selectivity, inner-sphere complexation, and organic phase composition. The key finding of this work is that the inclusion of phase modifiers impacts f-element selectivity due to their role in the organic phase. The role of phase modifiers is dependent upon the flexibility of the metal-extractant complex in the absence of phase modifiers. This work demonstrates that solvent modifications can be leveraged for f-element separations by influencing either inner-sphere or outer-sphere coordination
Apophyllite
No full textPhotographed by Ron Wolf.Glassy transparent mass of blocky apophyllite crystals, Mumbai district, Maharashtra, India
Andradite garnet
No full textPhotographed by Ron Wolf.Blocky crystals of dark grey bronze andradite garnet, Cardenas, Chihuahua, Mexico
Image to image models for big, non-stationary spatial data
No full textA prior title of this was "Vision transformers for parameter estimation of non-stationary spatial data."The ability to draw important conclusions regarding Earth and climate science problems is often limited by insufficient quantification of uncertainty in non-stationary, gridded data from climate models and satellites. The extreme computational demands of climate models make it infeasible to generate large ensembles, while satellites often struggle with measurement errors, computational artifacts, and extended revisit times. A solution to this problem is to model the data using a statistical distribution, allowing for efficient simulation of additional samples. However, fitting the model is often challenging due to key parameters varying across the domain (non-stationarity) and traditional methods becoming infeasible as the size of the data increases. Recent deep learning approaches address the computational costs by segmenting the data and performing parameter estimation locally. While this is a significant improvement, this strategy limits the ability to capture long-range correlation patterns, which are often exhibited by physical phenomena such as jet streams and along coastlines. We address this problem by adapting image-to-image models such as Vision Transformers, U-nets, and hybrids of the two to the task of non-stationary parameter estimation. Once the models have been trained to estimate the parameter grids, we rapidly simulate ensembles with a spatial autoregressive (SAR) model, allowing for pixel-wise uncertainty quantification. As an example, we apply this framework to analyze global surface temperature fields from a climate model dataset
Celestine
No full textPhotographed by Ron Wolf.Mass of grey to blue-grey prismatic celestine crystals
Quantitative analysis of in-well distributed acoustic sensing measurements during hydraulic fracturing
No full textIncludes bibliographical references.2024 Spring.Distributed acoustic sensing (DAS) serves as a tool in the oil and gas industry for continuously monitoring wellbore activity over the lifetime of a well. The data used in this thesis was recorded from a permanently installed fiber optic cable in a treatment well during hydraulic fracturing operations. Our observations of harmonics, energy decay and erosion, tube waves, and fiber breakage, we believe can expand DAS applications in wellbore monitoring. The harmonics we hypothesize can be utilized to approximate the length of structures in the near-wellbore region (NWR). The observed energy decay likely relates to portions of the wellbore eroding. By identifying the energy decay and its correlation to erosion, we anticipate understanding how energy decay impacts fluid flow allocation, while characterizing erosion provides further insight into wellbore and NWR variations. Additionally, we observed that tube waves act as a metric for stage isolation during perforation shots and hydraulic fracturing activities. Finally, we utilized the fiber breakage event that occurred during hydraulic fracturing to propose a methodology to identify the signs of a fiber breakage event before the fiber break occurs. This thesis proposes that further investigation into these observations can provide benefits for the use of DAS in wellbore monitoring and can assist in justifying the cost of fiber optic installation
Generalizing with neural networks on mazes
No full textGeneralizing from training data is a key issue in machine learning, especially since in practice the test data is often drawn from a different distribution. Unlike traditional fixed-depth networks such as multilayer perceptrons, recurrent and implicit neural networks are particularly suitable for out-of-distribution generalization because their variable-depth allows them to scale up computation to solve harder problems during test-time. We quantify the generalization ability of these models in the context of solving mazes, where we can easily generate out-of-distribution shifts while retaining a ground-truth solution. We show that a model trained on mazes without loops fails to generalize to mazes with loops; instead, the model emulates the 'dead-end filling' algorithm. Finally, we show that diversifying the training data very slightly by adding some loops to relatively few mazes dramatically increasing overall generalization. This indicates a switch in the underlying algorithm that the model is learning to emulate
Rhodochrosite
No full textPhotographed by Ron Wolf.Deep pink crystal of rhodochrosite in smaller crystal matrix
Pyrite
No full textPhotographed by Ron Wolf.Mass of metallic gold pyrite crystals showing striated surfaces