The precise regulation of the spatial distribution of active phases in bifunctional catalysts remains a significant challenge in heavy oil upgrading. This study highlights the critical role of zeolite silanol groups in directing the anchoring of Mo species and their impact on the metal–acid synergy during tetralin hydrocracking. Three Beta zeolites with sponge-like (S-Beta) and nano-structured (Nano-Beta) morphologies were investigated. Spectroscopic investigations (OH-FTIR and Py-IR) revealed that the distinct morphological features of these zeolites were accompanied by significant variations in silanol species distribution. Specifically, Nano-Beta-2 exhibits a high density of external isolated silanol groups (3745 cm−1), which serve as preferential anchoring sites for Mo species. This external-dominant localization effectively preserves the internal bridging hydroxyl groups (Si–OH–Al, 3610 cm−1) representing the Brønsted acid sites, thereby avoiding site neutralization during the impregnation process. Combined with the high mesoporosity (Vmeso/Vtotal = 0.63) that facilitates mass transfer, the Mo/Nano-Beta-2 catalyst achieved a remarkable tetralin conversion of 96.2% and a monocyclic aromatic hydrocarbons (MAHs, C6-C10) yield of 48.4% at 400 °C. The results demonstrate that leveraging the surface chemical environment (silanol types) of morphologically distinct zeolites enables the design of site-selective catalysts, thereby optimizing the selective ring-opening reaction pathway.

Chemists in search of structure-property relationships face great challenges due to limited high quality, concordant datasets. Machine learning (ML) has significantly advanced predictive capabilities in chemical sciences, but these modern data-driven approaches have increased the demand for data. In response to the growing demand for explainable AI (XAI) and to bridge the gap between predictive accuracy and human comprehensibility, we introduce LAMeL-a Linear Algorithm for Meta-Learning that preserves interpretability while improving the prediction accuracy across multiple properties. While most approaches treat each chemical prediction task in isolation, LAMeL leverages a meta-learning framework to identify shared model parameters across related tasks, even if those tasks do not share data, allowing it to learn a common functional manifold that serves as a more informed starting point for new unseen tasks. Our method delivers up to 60-96% reduction in MAE over standard ridge regression, depending on the domain of the dataset. While the degree of performance enhancement varies across tasks, LAMeL consistently outperforms or matches traditional linear methods, making it a reliable tool for chemical property prediction where both accuracy and interpretability are critical.

Redox chemistry governs the energetic and electronic landscape of chemical transformations central to catalysis, energy conversion, and separations. Predicting the redox behavior in complex metal coordination systems requires a systematic sampling of the configurational space to capture the subtle interplay between molecular geometry, solvation, and electronic structure. This challenge is particularly relevant in actinide chemistry, where redox processes dictate the efficiency of nuclear fuel recycling and waste separations. In this work, we integrate automated structure generation with high-throughput density functional theory to probe the AnO2 2+/+ (An = U, Np, Pu) redox couple in aqueous nitric acid media with representative chelating ligands. By explicitly accounting for diverse coordination environments and ligand-binding configurations, the role of chelator charge, donor-atom identity, and molecular configuration is established. Systematic configurational exploration exposes shifts exceeding 0.300 V arising from ligand substitution and structural isomerization—effects often overlooked in conventional single-structure studies. The observed correlations between coordination number, local electrostatic environment, and frontier orbital energetics establish design principles for tuning redox potentials across f-element systems for a wide range spanning over 1.5 V. These findings highlight the essential role of configurational sampling in quantitative redox prediction and offer molecular-level strategies not only for controlling redox chemistry in advanced nuclear separation workflows, but also towards metal coordination chemistry used in a variety of applications.

The synthesis, characterization, and electronic structure of Cp*2Am(tBu2bipy•-) (1-Am) (Cp* = pentamethylcyclopentadienide; tBu2bipy = 4,4'-di-tert-butyl-2,2'-bipyridine), the first isolated and structurally verified americium complex containing a radical ligand, is reported. Theoretical calculations (DFT and multiconfigurational methods) alongside experimental comparisons of M-N distances reveal a greater degree of metal-ligand covalency in 1-Am compared to near-isoradial lanthanide counterparts 1-Nd and 1-Sm. Electrochemical studies suggest that 1-Am may behave as an "Am(II) synthon", and a preliminary attempt to probe the chemical reactivity of 1-Am is briefly discussed.

Plutonium dioxide (PuO$_2$) is a key nuclear fuel material whose performance and durability are strongly governed by intrinsic point defects, particularly under extreme reactor environments. However, the charge-state stability and electronic effects of native defects---especially at surfaces---remain poorly understood. Here, we use first-principles DFT+U calculations to investigate the formation energetics, electronic structure, and migration mechanisms of oxygen and plutonium defects in bulk PuO$_2$ and at the oxygen-terminated (111) surface. We find that intrinsic defects can adopt multiple charge states within the band gap, with stability strongly dependent on the oxygen chemical potential and defect-induced redistribution of Pu 5$f$ electrons. Defects introduce significant local lattice distortions, renormalize magnetic moments, and generate deep electronic trap states. Migration calculations show that oxygen vacancy diffusion proceeds via sequential nearest-neighbor hops, while oxygen interstitial atoms migrate preferentially through a lattice-mediated exchange mechanism. At the (111) surface, oxygen vacancies are thermodynamically more stable than in the bulk, are predominantly stabilized in $\pm2$ charge states, and preferentially form in the topmost surface layer for a wide range of the electron chemical potential. These results provide mechanistic insight into defect-driven transport and surface effects in PuO$_2$, with direct relevance to modeling stoichiometry control, oxygen diffusion, and degradation processes in advanced nuclear energy systems.

Chemical process optimization is a critical and challenging task relevant to a broad range of industrial applications. Bayesian optimization (BO) has emerged as a powerful framework for navigating complex solution spaces, often with substantially greater efficiency than conventional experimental design approaches. While BO is commonly implemented using domain-agnostic Gaussian processes, its performance can potentially be further improved by incorporating domain knowledge. In this work, we propose a model-based BO framework that combines data-driven kinetic reaction networks with the pointwise optimal parameter sets (POPS) approach for improving uncertainty quantification. We apply this framework to the optimization of liquid-liquid uranium extraction using two monoamide ligands, N,N -dihexyloctanamide (DHOA) and N,N -di(2-ethylhexyl)-isobutyramide (DEHiBA). We evaluate the performance of the kinetic-model-based BO framework relative to a traditional Gaussian process counterpart, with particular emphasis on the scarce-data regime. We find that the kinetic reaction networks provide consistent and reliable predictions and that the POPS approach yields well-calibrated uncertainty estimates that can be effectively leveraged to guide the Bayesian optimization process. These features improve optimization efficiency, particularly in the scarce-data regime. Additional performance gains are obtained by regularizing the kinetic reaction networks using quantum mechanical calculations.

Developing new separation technologies for rare-earth elements is essential for sustaining the critical materials supply chain. Toward this end, we developed a pH-controlled solvent extraction strategy employing an aqueous-phase holdback agent to enable selective lanthanide separations, using an integrated computational, machine learning, and automated experimental high-throughput workflow. Database screening, density functional theory (DFT) calculations, and initial experimental evaluation identified oxaloacetic acid as a promising holdback agent that enhanced selective extraction of four lanthanides (Nd, Eu, Dy, Ho) when paired with the di(2-ethylhexyl)phosphoric acid (HDEHP or D2EHPA) extractant. Within this framework, the dependence of lanthanide solvent extraction was determined across a multidimensional chemical matrix (pH, extractant concentration, holdback agent concentration, and salt concentration) using automated, high-throughput experiments coupled with multi-objective Bayesian Optimization. Through efficient exploration of a large experimental space, we discovered Eu, Dy, and Ho could be selectively extracted over Nd in acidic media at pH ∼2.0, while a modest decrease in pH to ∼0.5 shifted the selectivity to enable Eu separation from Dy and Ho. The use of multi-objective Bayesian Optimization quickly yielded a 4-fold increase in separation factors compared to our HDEHP-only system, eliminating the need for a complete grid-based, exhaustive sampling approach. Overall, this work establishes a hierarchical and data-driven framework to identify selective separation conditions and provides a foundation for accelerating separations discovery and design.

Automating scientific discovery has been a long-standing objective in the scientific community, and the recent advances in artificial intelligence, particularly large language models and agentic workflows, have made this goal increasingly feasible. In chemistry, however, many problems remain limited by sparse data and fragmentary prior knowledge, leading standard data-driven approaches to struggle. In these low-data settings, progress should instead be accelerated by autonomous systems that strategically generate new informative data. Here, we introduce Chelatron, an agentic platform for autonomous metal-ligand design that leverages large language model-based reasoning to coordinate an iterative, hypothesis-driven framework of molecular discovery via effective chemical space exploration. The platform emulates key elements of the scientific method by proposing chemically meaningful hypotheses, generating candidate molecules, evaluating molecular properties, and using accumulated evidence to guide subsequent exploration. As an initial demonstration, we apply Chelatron to the design of chelators for actinium, an element whose scarcity and radioactivity have left its coordination chemistry underexplored. Across two campaigns, Chelatron proposed 119 hypotheses for improving chelator design and screened nearly 65,000 candidate molecules. 3D metal-chelator structures were constructed and evaluated at the xTB-level of theory for around 2,500 chelators, of which 679 were predicted to bind actinium comparable to or more favorable than the reference chelator, macropa, and are under further investigation. These results demonstrate Chelatron as an operational framework to accelerate hypothesis-driven discovery in data-limited metal-ligand chemistry.

A deeper understanding of the electronic structure of the lanthanides (Ln) is essential to harness their technological applications and enable responsible recovery/separation. Here, an isostructural series of tris(3,5-dimethyl-1-pyrazolyl)borate (Tp*)-supported LnIII adducts of a redox noninnocent 3,5-di-tert-butyl-o-semiquinone (DTBSQ) ligand, (Tp*)2Ln(DTBSQ), is characterized experimentally and computationally for all the early lanthanides La-Gd, except Pm. Computational simulations, together with spectroscopic measurements on selected systems, show the metals retain the trivalent oxidation state despite semiquinone binding with weak metal-radical coupling. While LnIII-DTBSQ bond distances decrease with decreasing LnIII ionic radii, theory suggests increased 4f-orbital mixing driven by the energetic accessibility of the f-shell, with significant radical contributions to the bonding, occurs for Sm/Eu. Orbital analyses and multireference calculations confirm these effects originate from metal-ligand orbital energy matching rather than through-space overlap. The LnIII 4f interactions are more covalent with the radical ligand than the Tp*, showing remarkably low 6s character. This contrasts with other common ligand-Ln studies and suggests that the DTBSQ-based orbitals have the appropriate symmetry and energy to preferentially interact with the 4f orbitals over the 6s orbital. This work provides new insights into Ln-radical interactions, revealing bonding characteristics for potential selective f-element chelators and extension into actinide chemistry.

Two-dimensional metal-organic frameworks (2D MOFs) hold considerable potential as active electrode materials for next-generation high-performance supercapacitors, owing to their highly tailorable crystal structures, abundant porous networks, and superior electron and ion transport capabilities. This review systematically summarizes the construction strategies, crystal structural features, and electron/ion transport mechanisms of 2D MOFs and their derivatives, with a focus on four categories: non-pillared 2D MOFs, pillar-supported 2D MOFs, hydrogen-bonded MOFs (HMOFs), and Metallo‑hydrogen-bonded MOFs (MHOFs). Non-pillared 2D MOFs exhibit high intrinsic conductivity due to continuous in-plane π-d conjugated coordination networks and van der Waals stacking, whereas pillar-supported 2D MOFs utilize pillar ligands to generate layer-pillar topologies and out-of-plane ion channels. Their high intrinsic conductivity arises from continuous in-plane π-d conjugated coordination networks, rather than merely the absence of pillars. Hydrogen bonds and flexible pores enable synergistic migration of electrons and protons in HMOFs and MHOFs. We then comprehensively evaluate their electrochemical performances in both supercapacitors and secondary batteries (lithium-ion, aqueous zinc-ion, and sodium-ion batteries). The application differences between these two energy storage systems are comparatively analyzed. Lastly, the key challenges facing 2D MOFs, including structural integrity maintenance, electrical conductivity improvement, and device integration capability, are expounded, and their future development opportunities in high-energy-density and multifunctional energy storage systems are discussed. Collectively, we provides a valuable reference for the research and engineering application of 2D MOFs in the field of electrochemical energy storage.

Solubility quantifies the concentration of a molecule that can dissolve in a given solvent.

Divalent f-block chemistry has undergone rapid expansion in recent years, driven by advances in the stabilization of low-valent lanthanide and actinide complexes. Although f-elements were historically accessed in the +3 or higher oxidation states, the isolation of +2 species has revealed unusual spectroscopic signatures, distinct bonding motifs, and multiple accessible electronic configurations that influence their chemical and physical properties. These developments have generated opportunities in areas including quantum information science, molecular magnetism, catalysis, and luminescence.Computational chemistry has played a pivotal role in interpreting the electronic structure and reactivity of these systems. However, accurately modeling divalent f-block complexes remains challenging because of strong electronic correlation, multiconfigurational character, and the presence of close-in-energy competing electronic states. As experimental capabilities and theoretical methodologies continue to advance, a comprehensive assessment of divalent chemistry is both timely and needed.In this Review, we integrate experimental and theoretical perspectives to provide a systematic analysis of divalent lanthanide and actinide complexes across the f-block series. We critically assess the role of ligands in determining competing ground-state configurations (fnd1 vs. fn+1) resulting in their distinct spectroscopic, magnetic, and bonding properties. Finally, we discuss emerging strategies for predictive modeling and rational design of low-valent f-block molecular systems, with potential applications ranging from dinitrogen reduction to quantum technologies.

One of the most common oral infections that afflict people of all ages globally is dental caries. In order to prevent and treat oral infections as well as other bacterial infections, many medicinal plants and herbal nanoparticles have been used in various medical systems to combat dental caries and other oral infections. These are currently in demand for the development of innovative therapeutic drugs with few side effects. In terms of easy, green, and applicable chemical research, a bio-inspired method for producing copper nanoparticles employing Dendrobium officinalis as a natural reducing and stabilizing agent without the need for hazardous or toxic chemicals is being presented. Cu2O NPs/D. officinalis' capacity to suppress dental bacterial growth and anti-adherence in vitro was investigated in this work. Detailed analysis using methods like TEM, FE-SEM, EDX, ICP-OES, and XRD verified the creation of the Cu2O NPs/D. officinalis nanocomposite. TEM pictures showed spherical nanoparticles with a narrow size distribution, nearing 15-25 nm in dimension. The fabricated Cu2O NPs/D. officinalis showed good activity for N-arylation of imidazole through Ullmann-type C-N coupling. Different aryl halides were transformed into N-arylated imidazoles with good outcomes. Moreover, the Cu2O NPs/D. officinalis could be reused for 7 times with keeping their efficiency. Following that, an investigation was conducted into the biomolecules biological performance that were functionalized Cu2O NPs/D. officinalis. The Cu2O NPs/D. officinalis addition greatly decreased the in vitro adherence of Porphyromonas gingivalis and Streptococcus mutans (MIC = 8 mu g/mL). Additionally, P. gingivalis and S. mutans were eliminated by the Cu2O NPs/D. officinalis with an MBC of 8-16 mu g/ mL. According to the study's findings, Cu2O NPs/D. officinalis could provide an excellent oral hygiene product to prevent dental cavities and periodontal diseases.

Uranium dioxide (UO2) is the primary fuel used in nuclear reactors. Under the extreme heat and radiation inside a reactor, this material inevitably develops defects in its crystal structure. To investigate the nature and behavior of these defects, DFT+U calculations were employed to investigate charged point defects in both bulk UO2 and its most stable surface, the (111) plane. The formation of defects and their impact on the electronic structure were systematically examined. The results reveal that these defects introduce localized electronic states, alter magnetic behavior, and modify the structural properties. In general, such defects act as deep traps capable of capturing and retaining charge carriers. The stability of these defects depends strongly on the chemical environment and the position of the Fermi level. Surface defect calculations reveal that oxygen vacancies form more readily at the surface than in the bulk over a wide range of electron chemical potential, with subsurface oxygen vacancies being more stable than those in the top layer. Overall, the findings demonstrate how charged defects influence magnetism, transport, and stability in UO2, providing insights that may guide improvements in the safety and efficiency of nuclear fuel.

Understanding how metal–ligand bonding evolves across the actinide series is central to the rational development of separations, nuclear fuel cycle, and waste management strategies, yet systematic experimental studies remain scarce owing to the difficulty of handling transuranium elements. Here we report the synthesis and isolation of [NpIII(Cptet)3] and [PuIII(Cptet)3] (Cptet = {C5Me4H}), which complete a rare isostructural series of trivalent triscyclopentadienide complexes [MIII(Cptet)3] spanning five early actinides (M = Th, U, Np, Pu, Am). Metal–carbon distances from thorium to americium show remarkably little variation, despite the decrease in metal ionic radius across this series, and unlike previously reported lanthanide analogues with similar metal ionic radii, which rules out steric congestion as the cause. Density functional theory and multi-reference ab initio calculations reveal that the 5𝑓 orbitals stabilise and contract markedly across the series, crossing below the ligand π-dominant manifold by americium. Topological analysis of the electron density exposes a polar covalent metal–ligand interaction that peaks around uranium/neptunium. These factors are tensioned across the series, such that covalency-induced bond length shortening is key for early actinides, while for later examples, the ionic bonding precludes bond length contraction due to electron-electron repulsion between the ligands and the non-bonding metal 5𝑓 electrons. UV-Vis-NIR spectroscopy, supported by CASSCF/RASSCF calculations, tracks the spectroscopic consequences of this electronic reorganization. Together, these results provide a self-consistent experimental and computational benchmark for electronic structure evolution across the first half of the actinide series within a single, structurally invariant ligand platform.

Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental data sets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess the metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants.

We present a hybrid semiempirical density functional tight-binding (DFTB) model with a machine learning neural network potential as a correction to the repulsive term. This hybrid model, termed machine learning tight-binding (MLTB), employs the standard self-consistent charge (SCC) DFTB formalism as a baseline, enhanced by the HIP-NN potential as an effective many-body correction for short-range pairwise repulsive interactions. The MLTB model demonstrates significantly improved transferability and extensibility compared to the SCC-DFTB and HIP-NN models. This work provides a practical computational framework for developing reliable SCC-DFTB models with additional many-body corrections that more closely approach the DFT level of accuracy. We illustrate this method with the development of an accurate model for the thorium-oxygen system, applied to the study of its nanocluster structures (ThO2)n.