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Kidder, Michelle K.

Publications and source records attributed to Kidder, Michelle K..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO 2 and lignin dissolution

The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π*). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R 2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO 2 ) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO 2 into valuable chemicals.

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

Ionic Liquid Functionalizes the Metal Organic Framework for Microwave-Assisted Direct Air Capture of CO 2

Sorbents for direct air capture (DAC) of CO 2 typically employ a chemisorbing material that requires regeneration at elevated temperatures, which is an energy-intensive step. Here, we developed composites of metal organic framework (MOF) with a functional ionic liquid (IL) at 5, 20, and 35 wt % loading, capable of CO 2 chemisorption and regeneration by moisture-swing coupled microwave (MW) irradiation. In dynamic breakthrough measurements, CO 2 from synthetic air (500 ppm of CO 2 ) was selectively absorbed (0.5 mmol/g at 30 °C) and then rapidly released (2–4 min) by dielectric heating at 60 °C. The developed IL/MOF composites demonstrate (1) targeted energy transfer to the IL domains where CO 2 is released due to dielectric heating upon MW irradiation and (2) a fast desorption rate due to large surface area offered by the MOF architecture. Furthermore, this study establishes carbon capture through surface enhancement of a porous substrate with ILs for future DAC processes that can be powered by sustainable energy sources.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

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

Tuning Surface, Phase, and Magnetization of Superparamagnetic Magnetite by Ionic Liquids: Single-Step Microwave-Assisted Synthesis

Achieving colloidal and chemical stability in ferrofluids by surface modification requires multiple steps, including purification, ex situ modification steps, and operation at high temperatures. In this study, a single-step microwave-assisted methodology is developed for iron oxide nanoparticle (IONP) synthesis utilizing a series of imidazolium-based ionic liquids (ILs) with chloride, bis(trifluoromethylsulfonyl)imide, and pyrrolide anions as the reaction media, thus eliminating the use of volatile organics while enabling rapid synthesis at 80 °C as well as in situ surface functionalization. The characterized surface functionality, hydrodynamic particle size, magnetization, and colloidal stability of the IONPs demonstrate a strong dependence on the IL structure, ion coordination strength, reactivity, and hydrophilicity. The IONPs present primarily a magnetite (Fe 3 O 4 ) phase with superparamagnetism with the highest saturation magnetization at 81 and 73 emu/g at 10 and 300 K, respectively. Depending on the IL coating, magnetization and exchange anisotropy decrease by 20 and 2–3 emu/g (at 35 wt % IL), respectively, but still represent the highest magnetization achieved for coated IONPs by a coprecipitation method. Further, the surface-functionalized superparamagnetic magnetite nanoparticles show good dispersibility and colloidal stability in water and dimethyl sulfoxide at 0.1 mg/mL concentration over the examined 3 month period. This study reports on the intermolecular and chemical interactions between the particle surface and the ILs under synthetic conditions as they relate to the magnetic and thermal properties of the resulting IONPs that are well suited for a variety of applications, including separation and catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

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

Techno-economic and environmental life cycle assessment of next-generation fiber-encapsulated nanoscale hybrid materials for direct air carbon capture

A techno-economic and environmental life cycle assessment was conducted for manufacturing two novel types of solid sorbents used for direct air CO 2 capture (DAC). One type of solid sorbent comprised Polyacrylonitrile (PAN)/Organopolysilizane (OPSZ)/nanoscale organic hybrid materials (NOHM)(, and the other comprised polymers of intrinsic microporosity (PIM)/NOHM. These solid sorbents were assumed to be manufactured via an unconventional route: electrospinning as fibers. Economic analyses revealed that manufacturing costs for a 0.73 MT/year capacity plant was $\$$831 and $\$$1398/kg of electrospun fiber in the case ofPAN/OPSZ/NOHM and PIM/NOHM, respectively. The costs were higher for PIM/NOHM owing to high raw material prices (Spirobisindane and tetrafluoroterephthalonitrile), that are used only in manufacturing PIM. Scaling up the plant capacity from 0.73 to 30 MT/year plant decreased the manufacturing cost by nearly half from $\$$831 to $\$$379 in the case of PAN/OPSZ/NOHM, depicting the economies of scale of effect. An LCA of both types of solid sorbent was performed using OpenLCA software, the Ecoinvent v 3.5 database, and the TRACI LCI assessment method. The Global Warming Potential (GWP) ofPAN/OPSZ/NOHM and PIM/NOHM was observed to be 151 × 10 -3 and 166 × 10 -3 kg CO 2 per kg CO 2 captured in the DAC plant, respectively. In conclusion, these GWP values were nearly five times as high as those observed for other solid sorbents existing in the literature such as PEI on silica gel, carbonate on silica, and carbonate on activated carbon owing to large amounts of electricity used in electrospinning unit operation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Vibrations and Phase Stability in Mixed Valence Antimony Oxide

α-Sb 2 O 4 (cervantite) and β-Sb 2 O 4 (clinocervantite) are mixed valence compounds with equal proportions of Sb III and Sb V as represented in the formula Sb III Sb V O 4 . Their structure and properties can be difficult to calculate owing to the Sb III lone-pair electrons. Here, we present a study of the lattice dynamics and vibrational properties using a combination of inelastic neutron scattering, Mössbauer spectroscopy, nuclear inelastic scattering, and density functional theory (DFT) calculations. DFT calculations that account for lone-pair electrons match the experimental densities of phonon states. Mössbauer spectroscopy reveals the β phase to be significantly harder than the α phase. Calculations with O vacancies reveal the possibility for nonstoichiometric proportions of Sb III and Sb V in both phases. An open question is what drives the stability of the α phase over the β phase, as the latter shows pronounced kinetic stability and lower symmetry despite being in the high-temperature phase. Since the vibrational entropy difference is small, it is unlikely to stabilize the α phase. In conclusion, our results suggest that the α phase is more stable only because the material is not fully stoichiometric.

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

Impact of Mg on Pd-based Methane Oxidation Catalysts for Lean-burn Natural Gas Emissions Control

More efficient lean-burn, natural gas engines are limited by greenhouse gas emissions due to methane oxidation catalysts (MOC) that suffer from water inhibition and high temperature activation. Herein, we report that the addition of Mg to supported 1 wt.% Pd MOCs improved hydrothermal stability even after severe hydrothermal aging. The superior methane oxidation activity compared to the corresponding Mg-free catalyst was attributed to (1) influence of Mg during surface roughening and restructuring at 700 °C on metal-support interaction, (2) reducibility of PdOx sites and (3) preferential stabilization of active Pd (1 0 0) facets in the sample as was evidenced by H 2 TPR and CO TPD characterization experiments. Methane conversion under synthetic exhaust conditions relevant to natural gas, lean-burn engines were investigated. In conclusion, BET, TPR, CO pulse chemisorption followed by TPD provided valuable insights into the surface area, pore volume, reducibility, Pd dispersion and Pd particle size of the selected catalyst samples.

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

Direct air capture of CO2: from insights into the current and emerging approaches to future opportunities

The rapid development of direct air capture (DAC) technologies has become critical in order to remove CO 2 from the atmosphere and limit global warming to a maximum of 1.5°C. In this perspective, we provide a mini review of the current research on the emerging liquid- and solid-based sorbent materials to capture CO 2 , summarize the existing challenges of DAC technologies, and suggest future research directions to accelerate the development of DAC systems. In particular, the desired properties for a breakthrough sorbent that efficiently captures CO 2 from the air and releases it for sequestration are described.

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

Photochemically‐Driven CO 2 Release Using a Metastable‐State Photoacid for Energy Efficient Direct Air Capture

Abstract One of the grand challenges underlying current direct air capture (DAC) technologies relates to the intensive energy cost for sorbent regeneration and CO 2 release, making the massive scale (GtCO 2 /year) deployment required to have a positive impact on climate change economically unfeasible. This challenge underscores the critical need to develop new DAC processes with substantially reduced regeneration energies. Here, we report a photochemically‐driven approach for CO 2 release by exploiting the unique properties of an indazole metastable‐state photoacid (mPAH). Our measurements on simulated and amino acid‐based DAC systems revealed the potential of mPAH to be used for CO 2 release cycles by regulating pH changes and associated isomers driven by light. Upon irradiating with moderate intensity light, a ≈55 % and ≈68 % to ≈78 % conversion of total inorganic carbon to CO 2 was found for the simulated and amino acid‐based DAC systems, respectively. Our results confirm the feasibility of on‐demand CO 2 release under ambient conditions using light instead of heat, thereby providing an energy efficient pathway for the regeneration of DAC sorbents.

Premadasa, Uvinduni I.↗
DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

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

Photochemically-Driven CO 2 Release Using a Metastable-State Photoacid for Energy Efficient Direct Air Capture

One of the grand challenges underlying current direct air capture (DAC) technologies relates to the intensive energy cost for sorbent regeneration and CO 2 release, making the massive scale (GtCO 2 /year) deployment required to have a positive impact on climate change economically unfeasible. This challenge underscores the critical need to develop new DAC processes with substantially reduced regeneration energies. Here, in this work, we report a photochemically-driven approach for CO 2 release by exploiting the unique properties of an indazole metastable-state photoacid (mPAH). Our measurements on simulated and amino acid-based DAC systems revealed the potential of mPAH to be used for CO 2 release cycles by regulating pH changes and associated isomers driven by light. Upon irradiating with moderate intensity light, a ≈55 % and ≈68 % to ≈78 % conversion of total inorganic carbon to CO 2 was found for the simulated and amino acid-based DAC systems, respectively. Our results confirm the feasibility of on-demand CO 2 release under ambient conditions using light instead of heat, thereby providing an energy efficient pathway for the regeneration of DAC sorbents.

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

Encapsulation of Nanoparticle Organic Hybrid Materials within Electrospun Hydrophobic Polymer/Ceramic Fibers for Enhanced CO 2 Capture

Liquid-like nanoparticle organic hybrid materials (NOHMs) consisting of a silica core with ionically grafted branched polyethyleneimine chains (referred to as NIPEI) are encapsulated within submicron-scale polyacrylonitrile (PAN)/polymer-derived-ceramic electrospun fibers. The addition of a room-temperature curable, liquid-phase organopolysilazane (OPSZ) ceramic precursor to the PAN/NOHM solution enhances the internal dispersion of NOHMs and forms a thin ceramic sheath layer on the fiber exterior, shielding the hydrophilic NIPEI to produce near-superhydrophobic non-woven fiber mats with contact angles exceeding 140°. 60:40 loadings of NOHMs to PAN/OPSZ can be reliably achieved with low OPSZ percentages required, and up to 4:1 NOHM:polymer loadings are possible before losing hydrophobicity. These fibers demonstrate up to ≈2 mmol CO 2 g -1 fiber capture capacities in a pure CO 2 atmosphere through the nonwoven fibrous networks and the permeability of the OPSZ shell. In conclusion, the hybrid fibers additionally show enhanced capture kinetics under pure CO 2 and 400 ppm CO 2 conditions, indicating their promising application as a direct air capture platform.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. Here in the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

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

Microwave Regeneration and Thermal and Oxidative Stability of Imidazolium Cyanopyrrolide Ionic Liquid for Direct Air Capture of Carbon Dioxide

Abstract Understanding the oxidative and thermal degradation of CO 2 sorbents is essential for assessing long‐term sorbent stability in direct air capture (DAC). The potential degradation pathway of imidazolium cyanopyrrolide, an ionic liquid (IL) functionalized for superior CO 2 capacity and selectivity, is evaluated under accelerated degradation conditions to elucidate the secondary reactions that can occur during repetitive absorption‐desorption thermal‐swing cycles. The combined analysis from various spectroscopic, chromatographic, and thermal gravimetric measurements indicated that radical and S N 2 mechanisms in degradation are encouraged by the nucleophilicity of the anion. Thickening of the liquid and gas evolution are accompanied by 50 % reduction in CO 2 capacity after a 7‐day exposure to O 2 under 80 °C. To prevent long exposure to conventional thermal heating, microwave (MW) regeneration of the CO 2 ‐reactive IL is used, where dielectric heating at 80 and 100 °C rapidly desorbs CO 2 and regenerates the IL without any measurable degradation.

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

Impact of Hydrogen Bonds on CO 2 Binding in Eutectic Solvents: An Experimental and Computational Study toward Sorbent Design for CO 2 Capture

Choline-based amino acid ionic liquids with anions glycinate, β-alaninate, phenylalaninate, and prolinate were synthesized and mixed with ethylene glycol to form lower-viscosity benign eutectic solvents for CO 2 capture. The highest capacity measured was 0.7 moles of CO 2 per mole of ionic liquid (2 moles CO 2 per kg solvent) for a 1 to 2 mole ratio mixture of choline prolinate to ethylene glycol at 1 bar of CO 2 and 25 °C. Under 5000 ppm of CO 2 , half of this capacity was realized. Here, through a combined study of quantitative 13 C NMR spectroscopy, molecular dynamics simulations and density functional theory calculations, we show that hydrogen bonding in the eutectic solvent prevents proton-transfer between prolinate anions upon CO 2 absorption, which occurs in the absence of ethylene glycol and deactivates binding sites. Blocking this proton transfer leads to a higher binding capacity compared to neat choline prolinate. This work demonstrates the impact of hydrogen bonding on the CO 2 binding mechanism and energetics, as well as physical and thermal properties in eutectic solvents, thus addressing an unmet need and informing future studies on the development of benign sorbents for capturing CO 2 from dilute streams.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗