3,297 Search Results

The development of efficient CO₂ reduction technologies hinges upon a thorough understanding of the intricate interplay between solution cations and the characteristics of the electrode surface. Recently, ionic liquids (ILs) have emerged as promising electrolytes for the CO₂ reduction reaction. However, the effect of alkali cations on the electrochemical CO₂ reduction (CO₂R) reaction remains unclear in ILs. Here, in this report, we studied alkali cation effects by assessing the electrocatalytic CO₂R activity with the IL 1-ethyl-3-methylimidazolium tetrafluoroborate, [EMIM][BF₄], in water with alkali metal co-cations (i.e., Li⁺, Na⁺, and K⁺) using a polycrystalline Sn catalyst. Contrary to previous findings in purelymore » aqueous media with inorganic cations, where alkali cations strongly enhance CO₂R via pH modulation and strengthening of interfacial electric fields, alkali cations in electrolytes containing the IL [EMIM][BF4] negatively impact CO₂R activity on Sn electrodes. These results were attributed to the larger radius and higher concentration of the IL organic cation [EMIM]⁺ that mitigates the impact of alkali cations. These findings highlight the complex interplay between IL cations and alkali metals in shaping CO₂R performance.« less

Controlling water structure and dynamics at silica interfaces are central to a wide range of technologies, including protective oxide layers for solar water splitting and nanoporous membranes. In this work, we develop a machine learning interatomic potential, trained via active learning, to achieve ab initio accuracy for water confined between hydroxylated silica surfaces over a range of silanol coverages and slit widths. We find that partially hydroxylated surfaces (50 and 75% OH) support stronger water−surface hydrogen bonding and more extended interfacial density profiles than fully hydroxylated (100% OH) surfaces, indicating that increasing OH coverage does not necessarily strengthen interfacial hydrogenbondmore » networks. Translational diffusion decreases approximately linearly with slit width and OH coverage, whereas rotational dynamics respond nonlinearly. In particular, at the smallest slit width of 5 Å, 75% OH coverage produces an enhanced local tetrahedral ordered interfacial network that strongly suppresses reorientation, while 100% coverage yields a crowded, disordered interfacial layer that also hinders rotation. In contrast, the 50% OH coverage is sufficiently sparse that it does not markedly alter water structure or dynamics under confinement. These results show that coupled control of pore size and surface chemistry enables nonlinear tuning of interfacial water structure and transport, providing a design strategy for optimizing porous silica for either enhanced interfacial stability and controlled reactivity or rapid and selective transport.« less

Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNPmore » method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.« less

Reliable transport and thermodynamic data for multivalent ions in complex molten salts are scarce, limiting model fidelity for electrorefining and impurity control. Here, we report a comprehensive electrochemical characterization of SnCl₂ in LiCl–KCl–CaCl₂ (50.5–44.2–5.3 mol%) at 685 K, including the effects of Ni²⁺ and Cu⁺ impurities. Using cyclic voltammetry (CV), chronoamperometry (CA), and chronopotentiometry (CP), we quantified Sn²⁺ and Ni²⁺ diffusion with exceptional agreement across methods: Sn²⁺ averaged (1.03 ± 0.10) × 10⁻⁵ cm² s⁻¹, and Ni²⁺ averaged (0.75 ± 0.19) × 10⁻⁵ cm² s⁻¹. The tight confidence-interval overlap across CV, CA, and CP strengthens confidence in these values andmore » is uncommon in molten chloride studies. Open-circuit-potential measurements provided standard apparent reduction potentials that closely match LiCl–KCl literature, indicating minimal shift with CaCl₂ present. The Sn²⁺/Sn couple behaves as a reversible two-electron soluble–insoluble process at 685 K; the Sn⁴⁺/Sn²⁺ couple transitions to soluble–soluble behavior near 788 K, which may correlate with the decomposition of surface bound chlorostannates, though direct characterization remains to be established. In mixed systems, Cu+/Cu overlaps Sn²⁺/Sn, limiting Cusingle bondSn electroseparation, whereas the larger potential gap between Ni²⁺/Ni and Sn²⁺/Sn supports selective Ni removal. These internally consistent transport and thermodynamic data establish a validated basis for process modeling and optimization of Sn electrorefining and impurity management in LiCl–KCl–CaCl₂.« less

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a systemmore » in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Furthermore, those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.« less

Humans are widely exposed to naphthalene. Once inhaled or ingested, naphthalene is metabolized by cytochrome P450 and other enzymes to form toxic metabolites known to harm lung epithelial cells. Naphthalene metabolites circulate in the blood. Chronic naphthalene inhalation promotes lesions in the epithelium of the mouse lung and rat nose. Oral naphthalene exposure leads to DNA adduct formation in mouse lung, but the contributions of different enzymatic pathways and the metabolites they generate are not fully understood. This study explores the influence of naphthalene metabolites on DNA adduct formation in the lungs of two species (mice and primates). To isolatemore » the lung response, conducting airway explants containing Club cells, a target for pulmonary naphthalene toxicity, were microdissected from live lung tissue and incubated with ¹⁴C-naphthalene or its metabolites: ¹⁴C-1,2-naphthoquinone or ¹⁴C-naphthalene-1,2-dihydrodiol. Explants were incubated for 1 h, then processed immediately (T1), or were transferred to clean media for the remainder of the 24 h (T24), to monitor ¹⁴C in DNA over time. Accelerator mass spectrometry analysis revealed the formation of DNA adducts by all three radiolabeled compounds by T24. Our results support the notion that P450 enzymes of the Cyp2abfgs subfamily contribute to naphthalene-induced DNA adduct formation (approximately 4-fold reduction in male mice lacking the Cyp2abfgs genes, P < 0.01). The finding that naphthalene-1,2-dihydrodiol, a stable metabolite, formed DNA adducts (102–117 adducts/10⁸ nucleotides) at 24 h following addition to the culture media validates the concern that circulating naphthalene metabolites can contribute to DNA adduct formation in the lung. DNA adducts persisted to 24 h after exposure in both mouse and primate airways and at comparable levels between species (77.8 vs 129 adducts/10⁸ nucleotides, respectively). Together, these results support the importance of a potential genotoxic mechanism of naphthalene and its metabolites in vivo in both mice and nonhuman primates, and possibly also in humans.« less

O-protected primary cyanohydrins (glycolonitriles) are important building blocks for many difunctionalized compounds and precursors to known bioactive molecules. Their synthesis, however, utilizes toxic cyanide, which raises significant safety concerns for industrial synthesis. Here, in this study, we present a cyanide-free enzymatic synthesis of an o-benzyl protected primary cyanohydrin from an (E)- or (Z)-α-oxygen protected aldoxime using an engineered aldoxime dehydratase enzyme from Bacillus sp. OxB-1 (OxdB). In contrast to many evolved enzymes that tend to “specialize” as their activity increases, we used directed evolution to engineer OxdB for efficient dehydration of both isomers in a mixture of (E)- or (Z)-α-oxygenmore » aldoximes with high activity and substrate loading to achieve near quantitative yield. Using this enzyme, we further demonstrate a cyanide-free chemoenzymatic pathway to an o-protected primary cyanohydrin starting from a readily available aldehyde, where the aldehyde is first condensed with hydroxylamine, followed by dehydration using our evolved enzyme. This pathway was readily scaled up to 1 g scale with high substrate loading, demonstrating its utility in industrial synthesis of these important building block functional groups.« less

Lithium metal is a sought after battery material for its high energy density due to the low electrochemical potential and density. However, lithium metal is also highly reactive, which results in a strong propensity for dendrite formation. The Sand’s time has previously been used to predict the time of dendrite initiation on metals that do not form a solid-electrolyte interphase (SEI), but it has been shown that the Sand’s time is not accurate for lithium electrodes when using transport parameters associated with the electrolyte. Thus, we built a numerical model to simulate lithium ion transport through a growing SEI tomore » predict the Sand’s time. The numerical model is shown to be more accurate than previous analytical solutions, especially for low current densities. We then analyze the sensitivity of the Sand’s time to different SEI properties and the chemical potential gradients present in the SEI, driving lithium transport. The results showed that high lithium concentration has a greater impact at high current density, while fast diffusivity is more important at low current density. Lastly, we modeled the influence of surface roughness on the plating evolution and chemical potential gradients when an SEI is present in comparison to the electrolyte. As a result, we demonstrate that the SEI plays a critical role in lithium electrode stability, and that improved characterization techniques are needed to better understand transport through the SEI and increase lithium metal utilization in energy storage devices.« less

Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but it is challenging due to the large number of reactions occurring at various time scales. Here, in this study, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic material 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under detonation. We discuss a strategy for constructing diverse training data needed to capture the complex chemistry of TATB. Our potential demonstrates strong transferability across a wide range of thermodynamic conditions and other explosives, enabling accurate and reliable chemical modeling of organic materials under extreme conditions. Themore » efficiency of our approach allows for simulations over several nanoseconds and for large system sizes, providing detailed insights into the chemistry of shocked TATB. The model accurately reproduces experimental Hugoniot equation of state data, and our simulations reveal the rapid formation of nitrogen-rich carbon clusters following shock. The methods and datasets developed here offer a robust framework for accurate chemical modeling of other shocked organic energetic materials.« less

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up tomore » 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.« less