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Allec, Sarah I.

Publications and source records attributed to Allec, Sarah I..

16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗
DOE OSTI2026

Boron Coordination in Multicomponent Glasses: Analytical Models and Machine Learning With Uncertainty

Borosilicate glasses are extensively used in a variety of applications from kitchenware to nuclear waste immobilization due to the strong network formed by the Si-O-B bond that makes it resistant to chemical corrosion and gives it a low thermal expansion. Boron, however, exists in both trigonal BO3 and tetrahedral BO4 bonds in glass systems, which impacts the chemical durability and thermal resistance of the glass, amongst other properties. Boron coordination (N4), or the ratio of the amount of BO4 to BO3 within a glass, may aid in predicting these properties but is difficult to derive without experimental data due to the complexity of impacts from varied glass compositions and processing factors. For this reason, compositional models have been developed to predict boron coordination, but the models typically include a limited number of glass components. To help fill this gap in the models, in this work, a diverse multicomponent glass dataset of 809 glasses is compiled from a literature search, and then a number of analytical and machine learning (ML) models are trained on the dataset. Previously developed modified Bernstein and modified Du Stebbins analytical models were fitted to update parameters with the new dataset. Then, partially Bayesian neural networks, Gaussian process regressor, and heteroskedastic deterministic neural networks were evaluated. The ML models examined all have different strategies to overcome the potential for overfitting as a result of a limited training dataset, and return results that account for model uncertainty, which can be valuable for understanding model reliability. For the first time, cooling rate is introduced as an input parameter for ML models, showing consistent improvements in performance and solidifying the importance of including parameters outside of composition alone for N4 prediction. The machine learning models examined here show promise in accurate predictions of boron coordination in borosilicate glasses, all achieving R2 values of 0.91.

boron coordination↗
DOE OSTI2026

Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation

We present a multi-agentic workflow for critical materials recovery that deploys a series of AI agents and automated instruments to recover critical materials from produced water and magnet leachates. This approach achieves selective precipitation from real-world feedstocks using simple chemicals, accelerating the development of efficient, adaptable, and scalable separations to a timeline of days, rather than months and years.

Ritchhart, Andrew J.↗
DOE OSTI2024

Towards informatics-driven design of nuclear waste forms

Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Molecular Understanding of Nitrogen Oxide Fixation of Water-Lean Carbon Capture Solvents by Atomistic Modeling

Nitrogen oxides, present in flue gas, can cause negative impacts on amine carbon capture solvents by the formation of heat-stable salts and suspected carcinogens. Thus, to maximize the performance of water-lean solvents, a better understanding of this process in these systems is necessary. Here, a computational study for the fixation of the CO 2 capture solvent N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (EEMPA) to nitramine/nitrosamine was conducted. The first step involves the dissociation of the NH bond of EEMPA, in which the homolytic mechanism is energetically more favorable than the heterolytic mechanism. The second step involves radical recombination to form N–N bonds. While NO 2 directly reacts with EEMPA, NO has almost no effect. However, in the presence of O 2 , fixation of EEMPA by NO is enhanced via the formation of N 2 O 4 species. Finally, low reaction energies indicate that the formation of nitramine/nitrosamine may be a reversible process, suggesting that EEMPA could be recovered under thermal stripping conditions.

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

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Enhancing CO 2 Transport Across a PEEK‐Ionene Membrane and Water‐Lean Solvent Interface

Abstract Efficient direct air capture (DAC) of CO 2 will require strategies to deal with the relatively low concentration in the atmosphere. One such strategy is to employ the combination of a CO 2 ‐selective membrane coupled with a CO 2 capture solvent acting as a draw solution. Here, the interactions between a leading water‐lean carbon‐capture solvent, a polyether ether ketone (PEEK)‐ionene membrane, CO 2 , and combinations were probed using advanced NMR techniques coupled with advanced simulations. We identify the speciation and dynamics of the solvent, membrane, and CO 2 , presenting spectroscopic evidence of CO 2 diffusion through benzylic regions within the PEEK‐ionene membrane, not spaces in the ionic lattice as expected. Our results demonstrate that water‐lean capture solvents provide a thermodynamic and kinetic funnel to draw CO 2 from the air through the membrane and into the bulk solvent, thus enhancing the performance of the membrane. The reaction between the carbon‐capture solvent and CO 2 produces carbamic acid, disrupting interactions between the imidazolium (Im + ) cations and the bistriflimide anions within the PEEK‐ionene membrane, thereby creating structural changes through which CO 2 can diffuse more readily. Consequently, this restructuring results in CO 2 diffusion at the interface that is faster than CO 2 diffusion in the bulk carbon‐capture solvent.

Walter, Eric D.↗
DOE OSTI2023

Dynamic Evolution of Palladium Single Atoms on Anatase Titania Support Determines the Reverse Water–Gas Shift Activity

Research interest in single-atom catalysts (SACs) has been continuously rising. However, the lack of understanding of the dynamic behaviors of SACs during applications hinder catalyst development and mechanistic understanding. Herein, we report on the evolution of active sites over Pd/TiO 2 -anatase SAC (Pd 1 /TiO 2 ) in the reverse water-gas shift (rWGS) reaction. Combining kinetics, in-situ characterization, and theory, we show that at T ≥ 350 °C, the reduction of TiO 2 by H 2 alters the coordination environment of Pd, creating Pd sites with partially cleaved Pd-O interfacial bonds and a unique electronic structure that exhibit high intrinsic rWGS activity through the carboxyl pathway. The activation by H 2 is accompanied by the partial sintering of single Pd atoms (Pd 1 ) into disordered, flat, ~1 nm diameter clusters (Pd n ). The highly active Pd sites in the new coordination environment under H 2 are eliminated by oxidation, which, when performed at high temperature, also re-disperses Pd n and facilitates the reduction of TiO 2 . In contrast, Pd 1 sinters into crystalline, ~5 nm particles (Pd NP ) during CO treatment, deactivating Pd 1 /TiO 2 . During the rWGS reaction, the two Pd evolution pathways co-exist. The activation by H 2 dominates, leading to the increasing rate with time-on-stream, and steady-state Pd active sites similar with the ones formed under H 2 . Finally, this work demonstrates how the coordination environment and nuclearity of metal sites on a SAC evolve during catalysis and pre-treatments, and how their activity is modulated by these behaviors. These insights on SAC dynamics and structure-function relationship are valuable to mechanistic understanding and catalyst design.

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

The Role of Surface Hydroxyls in the Mobility of Carboxylates on Surfaces: Dynamics of Acetate on Anatase TiO 2 (101)

The dynamics of reactive intermediates are important in catalysis for understanding transient species, which can drive reactivity and the transport of species to reaction centers. In particular, the interplay between surface-bound carboxylic acids and carboxylates is important for numerous chemical transformations, including CO 2 hydrogenation and ketonization. Here, we investigate the dynamics of acetic acid on anatase TiO 2 (101) using scanning tunneling microscopy experiments and density functional theory calculations. We demonstrate the concomitant diffusion of bidentate acetate and a bridging hydroxyl and provide evidence for the transient formation of molecular monodentate acetic acid. The diffusion rate is strongly dependent on the position of hydroxyl and adjacent acetate(s). A facile three-step diffusion process is proposed consisting of acetate and hydroxyl recombination, acetic acid rotation, and acetic acid dissociation. Here this study clearly demonstrates that the dynamics of bidentate acetate could be important in forming monodentate species, which are proposed to drive selective ketonization.

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

Stable Catalysts for Combined Dry and Steam Reforming of Methane and Carbon Dioxide

The specific aim of this project is to develop a comprehensive understanding of cheaper and more stable methane and carbon dioxide reforming catalysts with an optimal introduction of steam to reduce carbon deposits that typically prevent the catalytic systems from transferring to commercial implementation. The work was proposed as a two-year project with the end goal of to show a reliable catalyst system run for at least 400 hours at temperatures less than 800 °C with less than 5% loss of activity of the catalyst and a carbon monoxide to hydrogen product ratio of at least 1:1. The project went for one-year and met the end of year milestone by developing two strategies (use of a cerium co-catalyst and introducing 5% water) that reduced the carbon deposition rates by at least 20% relative to the baseline 10% NiMgAlO methane reforming of CO 2 at < 800 °C in order to shown technical feasibility of the concept.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Assessing entropy for catalytic processes at complex reactive interfaces

When chemical reactions are accelerated by a catalyst, entropy differences between reactants and their transient intermediates can be the driving force behind the promotion or inhibition of desired and parasitic chemical pathways. Understanding and controlling catalytic processes therefore requires both a fundamental and practicable understanding of entropy in addition to enthalpy. In unstructured media such as the vapor phase equilibrated with sparsely covered surfaces, entropy can be adequately accounted for by well-established approaches based on translational, rotational, and harmonic vibrational partition functions. However, these approximations become inadequate in more complex condensed phase environments, e.g., solid liquid interfaces of confined reaction spaces. In this chapter, we provide an overview of the state-of-art in the computational quantification of entropy and its known ramifications on catalysis. The fundamental roles of thermodynamics and kinetics in catalysis are covered in enough detail to appreciate and contextualize the computational methods employed to compute chemically accurate estimates of entropy. These methods are discussed in appropriate detail and range from the ubiquitous harmonic oscillator approximation where entropy unrelated to high frequency oscillations is typically underestimated, to enhanced free energy sampling with molecular dynamics where the desired accuracy must be weighed against the associated computational cost of obtaining it. The rising importance of machine learning and artificial intelligence in accelerating methodological progress in this field is touched upon, as well. Finally, applications, successes, and pitfalls of using these methods are provided to showcase past and present accomplishments while clarifying where improvements in both understanding and methodology are still needed.

Kollias, Loukas↗
DOE OSTI2021

Amphilic Water-Lean Carbon Capture Solvent Wetting Behavior through Decomposition by Stainless-Steel Interfaces

In this work, we report on a combined experimental and theoretical study of the wetting and reactivity of water-lean carbon capture solvents on the surface of common column packing materials. Paradoxically, we find that these solvents are equally able to wet hydrophobic and hydrophilic surfaces. The solvents are amphiphilic and are able to adapt to any interfacial environment due to their inherent heterogeneous molecular structure. Ab initio molecular dynamics indicates that these structures enable the formation of a strong adlayer on the surface of hydrophilic surfaces like oxidized steel which promotes solvent decomposition akin to hydrolysis from surface oxides and hydroxides. This decomposition passivates the surface, making it effectively hydrophobic, and the decomposed solvent promotes leaching of the iron into the bulk fluid. This study links the wetting behavior to the observed corrosion of the steels via decomposition of solvent at steel interfaces. The overall affect is strongly dependent on the chemical composition of the solvent, in that amines are stable, whereas imines and alcohols are not. We also observed that plastic packing shows little to no solvent degradation, but an equal degree of wetting.

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

The role of sub-surface hydrogen on CO 2 reduction and dynamics on Ni(110): An ab initio molecular dynamics study

The catalytic reduction of carbon dioxide is a crucial step in many chemical industrial reactions, such as methanol synthesis, the reverse water-gas shift reaction (rWGS) and formic acid synthesis. Here, we investigate the role of bulk hydrogen, where hydrogen atoms are found deep inside a metal surface as opposed to subsurface ones, upon CO 2 reduction over a Ni(110) surface using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. While it has previously been shown that subsurface hydrogen stabilizes CO 2 and can aid in overcoming reaction barriers, the role of bulk hydrogen is less studied and thus unknown with regard to CO 2 reduction. Furthermore, we find that the presence of bulk hydrogen can significantly alter the electronic structure of the Ni(110) surface, particularly the work function and d-band center, such that CO 2 adsorbs more strongly to the surface and is more easily reduced. Our results show an enhanced CO 2 dissociation in the presence of bulk hydrogen, shedding light on a hitherto underappreciated mechanistic pathway for CO 2 reduction on metal surfaces.

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

The electrolyte comprising more robust water and superhalides transforms Zn-metal anode reversibly and dendrite-free

A great challenge for all aqueous batteries, including Zn-metal batteries, is the parasitic hydrogen evolution reaction on the low-potential anode. Herein, we report the formula of a highly concentrated aqueous electrolyte that mitigates hydrogen evolution by transforming water molecules more inert. The electrolyte comprises primarily ZnCl 2 and LiCl as an additive, both of which are inexpensive salts. The O–H covalent bonds in water get strengthened in a chemical environment that has fewer hydrogen bonding interactions and a greater number of Zn–Cl superhalides, as suggested by integrated characterization and simulation. As a result, the average Coulombic efficiency of zinc-metal anode is raised to an unprecedented >99.7% at 1 mA cm –2 . In the new electrolyte, the plating/stripping processes leave the zinc-metal anode dendrite-free, and the zinc-metal anode delivers stable plating/stripping cycles for 4000 hours with an areal capacity of 4 mAh cm –2 at 2 mA cm –2 . Furthermore, the high Coulombic efficiency of zinc-metal anode in the ZnCl 2 -LiCl mixture electrolyte is demonstrated in full cells with a limited anode. The V 2 O 5 ·H 2 O| |Zn full cell with an N/P mass ratio of 1.2 delivers a stable life of more than 2500 cycles, and the LiMn 2 O 4 | |Zn hybrid cell with an N/P mass ratio of 0.6 exhibits 1500 cycles in its stable life.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗