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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simultaneous Observation of Carrier-Specific Redistribution and Coherent Lattice Dynamics in 2H-MoTe 2 with Femtosecond Core-Level Spectroscopy

In this study, we employ few-femtosecond extreme ultraviolet (XUV) transient absorption spectroscopy to reveal simultaneously the intra- and interband carrier relaxation and the light-induced structural dynamics in nanoscale thin films of layered 2H-MoTe 2 semiconductor. By interrogating the valence electronic structure via localized Te 4 d (39-46 eV) and Mo 4 p (35-38 eV) core levels, the relaxation of the photoexcited hole distribution is directly observed in real time. We obtain hole thermalization and cooling times of 15 ± 5 fs and 380 ± 90 fs, respectively, and an electron-hole recombination time of 1.5 ± 0.1 ps. Furthermore, excitations of coherent out-of-plane A 1g (5.1 THz) and in-plane E 1g (3.7 THz) lattice vibrations are visualized through oscillations in the XUV absorption spectra. By comparison to Bethe-Salpeter equation simulations, the spectral changes are mapped to real-space excited-state displacements of the lattice along the dominant A 1g coordinate. By directly and simultaneously probing the excited carrier distribution dynamics and accompanying femtosecond lattice displacement in 2H-MoTe 2 within a single experiment, our work provides a benchmark for understanding the interplay between electronic and structural dynamics in photoexcited nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Role of Exciton Diffusion in the Efficiency of Mn Dopant Emission in Two-Dimensional Perovskites

Two-dimensional (2D) metal-halide perovskites have promising characteristics for optoelectronic applications. By incorporating Mn 2+ ions into the perovskite structure, improved photoluminescence quantum yield can be achieved. This has been attributed to the formation of defect states that act as efficient recombination centers. Here, we make use of transient photoluminescence microscopy to characterize important material parameters of Mn 2+ -doped 2D perovskites with different doping levels. From these measurements, we visualize the importance of exciton transport as an intermediate step in the excitation of Mn 2+ . We model the spatiotemporal dynamics of the excited states to extract the diffusion constant and the transfer rate of the excitations to the Mn dopant sites. Interestingly, from these models, we find that the average distance an exciton needs to travel before transferring to a Mn site is significantly larger than expected from the Mn concentration obtained from elemental analysis. These insights are critical from a device design perspective.

2D perovskites↗
NASA NTRS1973

Flow in a porous nozzle with massive wall injection

An analytical and experimental investigation has been conducted to determine the effect of massive wall injection on the flow characteristics in a nozzle. The experiments were performed on a water table with a porous-nozzle test section. This had 45 deg and 15 deg half angles of convergence and divergence, respectively, throat radius of 2.5 inches, and throat width of 3 inches. The hydraulic analogy was employed to qualitatively extend the results to a compressible gas flow through the nozzle. An analysis of the water table flow was made using a one-dimensional flow assumption in the continuity and momentum equations. An analysis of a compressible flow in a nozzle was made in a manner analogous to that for the water flow. It is shown that the effect of blowing is to move the sonic position downstream of the geometric throat. Similar results were determined for the incompressible water table flow. Limited photographic results are presented for an injection of air, CO2, and Freon-12 into a main-stream air flow in a convergent-divergent nozzle. Schlieren photographs were used to visualize the flow.

Kinney, R. B.↗
DOE OSTI2019

Distributions of the Mean for Lognormally Distributed Data - 19427

In environmental sciences, a distribution of the mean is often needed for right-skewed data. For example, risk assessments are often based on distributions of the mean to obtain upper confidence limits (UCLs) for analyte concentrations, and, in probabilistic performance assessment (PPA) models, parameters are given distributions rather than deterministic values, and they are usually given distributions of their mean rather than the population distributions to account for the large spatial and temporal domains to which they are applied. How this distribution of the mean is developed depends on the characteristics of the parameter's full distribution and how the parameter relates to its use. In this presentation, I will discuss the implications of a right skew in distribution development. Parameters may vary over orders of magnitude and/or may be right-skewed. The material property of saturated hydraulic conductivity, Ks, is a prime example of such a parameter. Right-skewed parameters are often assumed to follow lognormal distributions. If X follows a lognormal distribution, then log(X) follows a normal distribution. For some right-skewed parameters, practitioners go back and forth between the two forms of the data depending on their needs. For example, it may be preferred to use the untransformed version of X in a model or equation, but it is easier to visualize the parameter values on a logarithmic scale. However, distribution development for the average value of the parameter will differ depending on whether X or log(X) is modeled. This difference is due to Jensen's Inequality, a theorem which states that the expected value of a concave function of a variable is less than or equal to the same function of the expected value of the variable; a logarithm is a concave function. In other words, the mean of log(X) is always less than or equal to the log of the mean of X and therefore the mean estimated from a distribution based on log(X) is less than or equal to the mean estimated from a distribution based on X. If a parameter is naturally considered on a log scale by subject matter experts, then distributions developed for the mean of log(X) may be more consistent with the use of the parameters in the literature than distributions developed for X. The statistical decisions of distribution development will be discussed within the context of right-skewed, lognormal data. These issues include method-of-moments versus maximum likelihood estimation of the distribution parameters and the use of bootstrapping to aid in the visualization of a distribution of the mean. The details surrounding Jensen's Inequality will be explained and visualized within the context of distribution development of a right-skewed parameter such as saturated hydraulic conductivity. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Materials informatics: From the atomic-level to the continuum

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE

Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. In this work, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI datasets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus facilitating applications of MSI in glycobiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Intact, Commercial Lithium-Polymer Batteries: Spatially Resolved Grating-Based Interferometry Imaging, Bragg Edge Imaging, and Neutron Diffraction

We survey several neutron imaging and diffraction methods for non-destructive testing and evaluation of intact, commercial lithium-ion batteries. Specifically, far-field interferometry was explored as an option to probe a wide range of autocorrelation lengths within the batteries via neutron imaging. The dark-field interferometry images change remarkably from fresh to worn batteries, and from charged to discharged batteries. When attempting to search for visual evidence of battery degradation, neutron Talbot-Lau grating interferometry exposed battery layering and particle scattering through dark-field imaging. Bragg edge imaging also reveals battery wear and state of charge. Neutron diffraction observed chemical changes between fresh and worn, charged and discharged batteries. However, the utility of these methods, for commercial batteries, is dependent upon battery size and shape, with 19 to 43 mAh prismatic batteries proving most convenient for these experimental methods. This study reports some of the first spatially resolved, small angle scattering (dark-field) images showing battery degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Facile Background Discrimination in Femtosecond Stimulated Raman Spectroscopy Using a Dual-Frequency Raman Pump Technique

Femtosecond stimulated Raman spectroscopy (FSRS) is a useful technique for uncovering chemical reaction dynamics by acquiring high-resolution Raman spectra with ultrafast time resolution. However, in FSRS, it can be challenging to discern Raman features from signals arising from transient absorption and other four-wave mixing pathways. To overcome this difficulty, we combine the principles of shifted excitation Raman difference spectroscopy with a simple fixed frequency comb to perform dual-frequency Raman pump FSRS. Through the addition of only two mirrors and a slit to the standard FSRS setup, this method provides Raman spectra at two different excitation wavelengths that can be processed by an automated algorithm to reconstruct the Raman spectrum. In this study, we demonstrate the utility of dual-frequency Raman pump FSRS to easily identify Raman signatures by visual inspection for excited-state and ground-state spectra, both on- and off-resonance. We show that previously assigned short-lived vibrations of photoexcited β-carotene are actually not vibrational in nature. We also use crystalline betaine-30 as a challenging test case for this method, as the FSRS spectra contain a number of narrow transient vibronic and non-SRS features. By reliably reducing interference from background signals, the interpretation is substantially more quantitative and enables the future study of new systems, particularly those with small Raman cross-sections or solid-state samples with narrow vibronic features.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Effect of Formation Heterogeneity on CO 2 Dissolution in Subsurface Porous Media

Dissolution trapping is one of the most dominant mechanisms for the secure storage of CO 2 injected in porous subsurface formations saturated with brine. This trapping mechanism is enhanced by convective mixing, which occurs due to the gravitational instability between the different fluid layers in the aquifer. The reservoir permeability also plays a crucial role in the dissolution rate and overall fluid flow dynamics during the density-driven convection in porous media. Herein this study investigates the role of complex heterogeneity, i.e., irregular permeability distribution in CO 2 dissolution, using a novel experimental approach to create medium permeability heterogeneity in Hele–Shaw cells. Complex subsurface transport phenomena such as a preferential dissolution path, CO 2 sweep efficiency, changes in finger morphology, and CO 2 concentration distribution are visualized by creating heterogeneous media. Experimental results showed that reservoir permeability heterogeneity causes significant channeling effects and poor sweep efficiency. A scaling relationship between average finger growth rate (Gr) and permeability (k) was obtained as Gr [m s –1 ] = 266.8k [m 2 ] + 1.20 × 10 –6 . Furthermore, the mass of CO 2 dissolved is calculated using the spectrophotometric method to characterize the convective instability. The convective flux was analyzed by comparing the experimental dissolution flux with the theoretical diffusion flux, calculating a maximum Sherwood number of 6.8. The study’s findings improve the current understanding of the CO 2 convection morphology in heterogeneous media, allowing better assessment of long-term CO 2 storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Oxidation induced strain and defects in magnetite crystals

Oxidation of magnetite (Fe 3 O 4 ) has broad implications in geochemistry, environmental science and materials science. Spatially resolving strain fields and defect evolution during oxidation of magnetite provides further insight into its reaction mechanisms. Here we show that the morphology and internal strain distributions within individual nano-sized (~400 nm) magnetite crystals can be visualized using Bragg coherent diffractive imaging (BCDI). Oxidative dissolution in acidic solutions leads to increases in the magnitude and heterogeneity of internal strains. This heterogeneous strain likely results from lattice distortion caused by Fe(II) diffusion that leads to the observed domains of increasing compressive and tensile strains. In contrast, strain evolution is less pronounced during magnetite oxidation at elevated temperature in air. Furthermore, these results demonstrate that oxidative dissolution of magnetite can induce a rich array of strain and defect structures, which could be an important factor that contributes to the high reactivity observed on magnetite particles in aqueous environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Electrostatic Barriers to Nanoparticle Accessibility of a Porous Matrix

Translocation from one cavity to another through a narrow constriction (i.e., a “hole”) represents the fundamental elementary process underlying hindered mass transport of nanoparticles and macromolecules within many natural and synthetic porous materials, including intracellular environments. This process is complex and may be influenced by long-range (e.g., electrostatic) particle–wall interactions, transient adsorption/desorption, surface diffusion, and hydrodynamic effects. In this study, we used a three-dimensional (3D) tracking method to explicitly visualize the process of nanoparticle diffusion within periodic porous nanostructures, where electrostatic interactions were mediated via ionic strength. The effects of electrostatic interactions on nanoparticle transport were surprisingly large. For example, an increase in the Debye length of only a few nanometers (in a material with a hole diameter of ~100 nm) increased the mean cavity escape time 3-fold. A combination of computational and experimental analyses indicated that this hindered cavity escape was due to an electrostatic energy barrier in the region of the hole, which was quantitatively explained using DLVO theory. These findings explicitly demonstrate that the cavity escape process was barrier-limited and dominated by electrostatic effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Small Molecule Inhibitors of the BfrB–Bfd Interaction Decrease Pseudomonas aeruginosa Fitness and Potentiate Fluoroquinolone Activity

The iron storage protein bacterioferritin (BfrB) is central to bacterial iron homeostasis. The mobilization of iron from BfrB, which requires binding by a cognate ferredoxin (Bfd), is essential to the regulation of cytosolic iron levels in P. aeruginosa . This paper describes the structure-guided development of small molecule inhibitors of the BfrB–Bfd protein–protein interaction. The process was initiated by screening a fragment library and followed by obtaining the structure of a fragment hit bound to BfrB. The structural insights were used to develop a series of 4-(benzylamino)- and 4-((3-phenylpropyl)amino)-isoindoline-1,3-dione analogs that selectively bind BfrB at the Bfd binding site. Challenging P. aeruginosa cells with the 4-substituted isoindoline analogs revealed a dose-dependent growth phenotype. Further investigation determined that the analogs elicit a pyoverdin hyperproduction phenotype that is consistent with blockade of the BfrB–Bfd interaction and ensuing irreversible accumulation of iron in BfrB, with concomitant depletion of iron in the cytosol. The irreversible accumulation of iron in BfrB prompted by the 4-substituted isoindoline analogs was confirmed by visualization of BfrB-iron in P. aeruginosa cell lysates separated on native PAGE gels and stained for iron with Ferene S. Challenging P. aeruginosa cultures with a combination of commercial fluoroquinolone and our isoindoline analogs results in significantly lower cell survival relative to treatment with either antibiotic or analog alone. Collectively, these findings furnish proof of concept for the usefulness of small molecule probes designed to dysregulate bacterial iron homeostasis by targeting a protein–protein interaction pivotal for iron storage in the bacterial cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Discovery of hybrid chemical synthesis pathways with DORAnet

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems

The Dalton Project provides a uniform platform access to the underlying full-fledged quantum chemistry codes Dalton and LSDalton as well as the PyFraME package for automatized fragmentation and parameterization of complex molecular environments. The platform is written in Python and defines a means for library communication and interaction. Intermediate data such as integrals are exposed to the platform and made accessible to the user in the form of NumPy arrays, and the resulting data are extracted, analyzed, and visualized. Complex computational protocols that may, for instance, arise due to a need for environment fragmentation and configuration-space sampling of biochemical systems are readily assisted by the platform. The platform is designed to host additional software libraries and will serve as a hub for future modular software development efforts in the distributed Dalton community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Strong spin-orbit coupling and Dirac nodal lines in the three-dimensional electronic structure of metallic rutile IrO 2

Using high-resolution angle-resolved photoemission spectroscopy and ab initio calculation, we have studied the bulk and surface electronic structure of metallic rutile 5d transition metal oxide IrO 2 that harbors both edge and corner sharing Ir-O octahedrons. We observe strong modulation of the band structure by spin-orbit coupling (SOC). The measured band structure is well reproduced by our ab initio calculation without band renormalization, suggesting the absence of the SOC-enhanced correlation effect in IrO 2 . In accordance with the calculation, we visualize two types of Dirac nodal lines (DNLs) protected by mirror symmetry and nonsymmorphic crystal symmetry, respectively. SOC gaps the first type of DNLs, which may contribute largely to the strong spin Hall effect. Furthermore, the second type of DNLs at the edges of Brillouin zone, however, remain intact against SOC. Our results not only provide important insights into the exotic transport properties of IrO 2 , but also shed light on the understanding of the role of SOC in the iridate family.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1995

Applying AI tools to operational space environmental analysis

The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines events covering reports of natural phenomena such as solar flares, bursts, geomagnetic storms, and five others pertinent to space environmental analysis. With our preliminary event definitions we experimented with TAS's support for temporal pattern analysis using X-ray flare and geomagnetic storm forecasts as case studies. We are currently working on a framework for integrating advanced graphics and space environmental models into this analytical environment.

Krajnak, Mike↗
DOE OSTI2019

Revealing the atomic ordering of binary intermetallics using in situ heating techniques at multilength scales

Ordered intermetallic nanoparticles are promising electrocatalysts with enhanced activity and durability for the oxygen-reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs). The ordered phase is generally identified based on the existence of superlattice ordering peaks in powder X-ray diffraction (PXRD). However, after employing a widely used postsynthesis annealing treatment, we have found that claims of “ordered” catalysts were possibly/likely mixed phases of ordered intermetallics and disordered solid solutions. In this work, we employed in situ heating, synchrotron-based, X-ray diffraction to quantitatively investigate the impact of a variety of annealing conditions on the degree of ordering of large ensembles of Pt 3 Co nanoparticles. Monte Carlo simulations suggest that Pt 3 Co nanoparticles have a lower order–disorder phase transition (ODPT) temperature relative to the bulk counterpart. Furthermore, we employed microscopic-level in situ heating electron microscopy to directly visualize the morphological changes and the formation of both fully and partially ordered nanoparticles at the atomic scale. In general, a higher degree of ordering leads to more active and durable electrocatalysts. The annealed Pt 3 Co/C with an optimal degree of ordering exhibited significantly enhanced durability, relative to the disordered counterpart, in practical membrane electrode assembly (MEA) measurements. The results highlight the importance of understanding the annealing process to maximize the degree of ordering in intermetallics to optimize electrocatalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗