Nancy M. Washton

Highly alkaline brines comprising mixtures of alkali metal cations are an important component of safe, aqueous chemistries for (long-duration) energy storage, electrowinning for the direct reduction of metal oxides to metal, industrial electrolysis, and many other technological applications. Physicochemical studies of the ion association, solvation dynamics, and transport in this highly concentrated regime are sparse, particularly with LiOH as a mixture component approaching its saturation limit, and could provide key inputs for physics-based modeling efforts and defining operational limits. To this end, this study maps the composition space for KOH/LiOH and KOH/NaOH ranging from total ionic strengths of 1 to 9 M, with a series of alkali cation mixtures at each ionic strength and an emphasis on contrasting the solvation dynamics and transport of brines with Li⁺ cocations versus Na⁺. Combining NMR chemical shift, diffusivity, ionic conductivity, density, viscosity, and NMR relaxation measurements yields a detailed understanding of the hydroxide affinity of Li⁺ compared to K⁺ and Na⁺ as well as the evolution of the solution structure as the Li⁺ saturation limit is approached, culminating in the elucidation of a distinct solvation regime in these mixed cation electrolytes at ionic strengths above 6 M for both KOH/LiOH and KOH/NaOH.

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

This project aimed to advance the capabilities of high-field Dynamic Nuclear Polarization (DNP) Nuclear Magnetic Resonance (NMR) spectroscopy at 800 MHz through the synthesis and evaluation of novel biradicals. While the targeted HyTEK biradical synthesis faced significant challenges, alternative efforts focused on synthesizing intermediate compounds and exploring alternative biradicals. A portion of the BDPA precursor was successfully converted into BDPA-TEMPO, though further DNP testing could not be completed within the project timeline. Benchmarking experiments with existing biradicals, including TEKPol and M-TinyPol, were conducted to evaluate their performance under high-field conditions. These efforts provided valuable insights into DNP enhancements, refined operational protocols for high-field experiments, and established benchmarks for future polarizing agent development

Metal carbonates, which are ubiquitous in the near-surface mineral record, are a major product of biomineralizing organisms and serve as important targets for capturing anthropogenic CO₂ emissions. However, pathways of carbonate mineralization typically diverge from classical predictions due to the involvement of disordered precursors, such as the dense liquid phase (DLP), yet little is known about DLP formation or solidification processes. Using in situ methods we report that a highly hydrated bicarbonate DLP forms via liquid–liquid phase separation and transforms into hollow hydrated amorphous CaCO₃ particles. Acidic proteins and polymers extend DLP lifetimes while leaving the pathway and chemistry unchanged. Molecular simulations suggest that the DLP forms via direct condensation of solvated Ca²⁺⋅(HCO₃⁻)₂ complexes that react due to proximity effects in the confined DLP droplets. Our findings provide insight into CaCO₃ nucleation that is mediated by liquid–liquid phase separation, advancing the ability to direct carbonate mineralization and elucidating an often-proposed complex pathway of biomineralization.

The rapid expansion of generative artificial intelligence (GenAI) has generated excitement regarding its potential benefits and concern over its ethical implications. Governments, corporations, and standards organizations have described ethical principles to direct GenAI's development and use; however, practical guidance for implementing these principles is limited. Addressing this gap is critical, especially considering the array of risks associated with GenAI, such as legal liabilities, privacy concerns, security threats, and potential misuse. Robust policies and procedures are critical to support responsible deployment of GenAI. This report examines Pacific Northwest National Laboratory (PNNL)’s approach to promoting responsible GenAI use. Proposed initiatives include developing policies based on ethical principles, creating a governance process to review projects relative to those principles, and implementing onboarding processes for training staff. The governance framework described in this report adapts the structure and principles of Institutional Review Boards (IRBs), traditionally used in human subjects research, for GenAI ethical review, providing oversight. Ethical principles guiding responsible GenAI usage include transparency and accountability, privacy, fairness, safety, security, and validity and reliability. To operationalize these principles, we propose forming a GenAI Assurance Council (GAC) that mirrors the IRB's structure. The GAC will evaluate GenAI projects across privacy, accountability, transparency, safety, security, fairness, and validity dimensions. Complementing policy and governance is AI literacy training to support staff understanding of GenAI's ethical implications. An initial training effort for AI Incubator Chat—a GenAI tool deployed at PNNL—showed promising results, underscoring the importance of clear guidelines and user accountability. Collaborative efforts and the dissemination of best practices are also discussed. The proposed GAC model and AI literacy training provide a blueprint for establishing ethical GenAI use and governance, offering practical tools to bridge the gap between ethical principles and real-world applications. The responsible integration of GenAI at PNNL entails a multifaceted approach involving policy development, ethical governance, and AI literacy training. The positive initial feedback and collaborative opportunities position PNNL to lead by example in GenAI's responsible use, reflecting a proactive stance in addressing the ethical, legal, and societal challenges associated with this emerging technology. PNNL's systematic and ethical approach to GenAI offers a model for other institutions to emulate, promoting safe and responsible technological advancements in the AI domain.

Our objective is to apply machine learning (ML) algorithms for the prediction of molecular catalysis descriptors from geometric properties derived from experimental crystallographic databases. Catalysis is often considered a “low-data” discipline that is poorly suited for ML methods. An exception is the extensive structural information that is available for molecular catalysts through the Cambridge Structural Database (CSD), which contains atomically precise molecular structures from X-ray diffraction analysis for >600K metal complexes. As a proof-of-principle, we targeted the prediction of hydricity, a thermodynamic property that provides understanding and control of catalytic hydride transfer. We built a training set composed of ~100 molecular complexes with a known hydricity and structural information from the CSD. This data set was converted into a machine-readable format using the smooth overlap of atomic positions (SOAP) representation and further labeled with simple electronic descriptors for the metal centers. Multiple different neural networks were trained on this data set, and the accuracy of the hydricity predictions ranged from < 2 kcal/mol to 20 kcal/mol. The accuracy of each model was highly sensitive to which compounds were in the train versus test set, underscoring the challenges associated with small and chemically diverse data sets. Finally, to further augment themore » data set, we attempted to experimentally measure several new hydricity values, however these experiments were unsuccessful due to undesired chemical reactivity of the selected complexes. « less

The present study investigates barriers and facilitators to the implementation of Findable, Accessible, Interoperable, and Reusable (FAIR) data processes within the Physical Sciences Division of the Computational Sciences Directorate (PCSD). Employing a dual-method approach consisting of surveys and focus group discussions, the study aims to illuminate the complex interplay between technical and psychosocial factors that influence FAIR data adoption. Key Findings: • Surveys indicated that while staff generally understood the merits of FAIR data, the implementation was hampered chiefly due to concerns of accuracy, trust, and resource constraints. • Focus group discussions further elucidated the nature and extent of these barriers, revealing issues ranging from career risk to administrative burdens. • Despite general apprehensions, there was a common acknowledgment of the positive potential of FAIR data, such as streamlining research processes and fostering a community of shared insights and failures. Recommendations: • Convene a cross-disciplinary working group to facilitate implementation strategies and serve as FAIR data ambassadors. • Implement NEMO, an open-source software for streamlined data handling and robust cost-benefit analyses. • Engage in meta-data identification congruent with community practices. • Foster dialogues with Principal Investigators and Project Managers regarding DataHub costs. • Employ a dedicated “Data Librarian” to manage and curate data repositories. The report underscores the necessity of a nuanced approach that considers both technical and psychosocial variables to accelerate FAIR data integration into the PCSD's research ecosystem. The detailed insights and recommendations aim to provide a roadmap for cultivating a data culture that is both rigorous and collaborative, thereby potentially expediting scientific discovery.

We report a new sodium fast‐ion conductor, Na₃B₅S₉, that exhibits a high Na ion total conductivity of 0.80 mS cm⁻¹ (sintered pellet; cold‐pressed pellet=0.21 mS cm⁻¹). The structure consists of corner‐sharing B₁₀S₂₀ supertetrahedral clusters, which create a framework that supports 3D Na ion diffusion channels. The Na ions are well‐distributed in the channels and form a disordered sublattice spanning five Na crystallographic sites. The combination of structural elucidation via single crystal X‐ray diffraction and powder synchrotron X‐ray diffraction at variable temperatures, solid‐state nuclear magnetic resonance spectra and ab initio molecular dynamics simulations reveal high Na‐ion mobility (predicted conductivity: 0.96 mS cm⁻¹) and the nature of the 3D diffusion pathways. Notably, the Na ion sublattice orders at low temperatures, resulting in isolated Na polyhedra and thus much lower ionic conductivity. This highlights the importance of a disordered Na ion sublattice—and existence of well‐connected Na ion migration pathways formed via face‐sharing polyhedra—in dictating Na ion diffusion.