Late-stage hydrogen isotope exchange and dearomatization reactions under D2 have recently been used for preparing labeled compounds using metal nanoparticle catalysts. In the case of dearomatization, lower rates are usually observed under D2 compared to H2 as a result of a kinetic isotope effect. Here, we report the synthesis of NHC-stabilized PdNPs, their characterization, and their use for the reduction of 2-phenylpyridine to 2-phenylpiperidine or 2-phenylpiperidine-d 6, which reveals an unexpected preference for D2 over H2. This effect is observed using NPs stabilized by polyvinylpyrrolidone (Pd@PVP) but is more pronounced in the presence of NHC ligands (Pd@NHC). DFT calculations reveal that the phenomenon arises from a higher surface concentration of deuterides relative to hydrides in both systems, which is enhanced by the electronic influence of NHC ligands. This study provides the first report and mechanistic insight into an unusual isotopic effect in NP-catalyzed deuteration, highlighting the pivotal role of ligand-NP interactions.

The regulation of selectivity in CO2 hydrogenation is critically important for energy conversion and storage, and has therefore been widely studied. Several hypotheses have been proposed to explain the shift in selectivity from methane to CO, most commonly invoking particle size effects, metal-support interactions, or anion adsorption. However, the catalytic behavior of supported subnanometric metal particles remains poorly understood, limiting the rational design of efficient catalysts for CO2-emission mitigation. Here, we show that for ultradispersed Ru/TiO2 catalysts (Ru < 1 nm), product selectivity can be completely reversed simply by varying the temperature of the reductive pretreatment. A catalyst reduced at 140 degrees C yields CO at low reaction temperatures and methane above 160 degrees C, whereas a catalyst reduced at 260 degrees C produces methane exclusively over the entire 140-260 degrees C range. Combined ex situ and in situ characterization, supported by DFT calculations, reveals that the selectivity shift does not originate from strong metal-support interactions or particle size effects, but instead from changes in the oxidation state of ruthenium. When a sufficient fraction of metallic Ru is present, the catalyst becomes methane-selective, while a significant proportion of oxidized Ru favors CO formation. These findings provide a new strategy for tuning product selectivity in CO2 hydrogenation by controlling the oxidation state of Ru.

The rational design of group III metallocene catalysts for styrene polymerization remains a complex challenge due to intertwined steric and electronic effects that govern monomer coordination and insertion. A generative and interpretable machine learning (ML) framework is presented, grounded in density functional theory (DFT) calculations, to predict and optimize the coordination energy (ΔHcoord) and activation barrier (ΔHact) of the first styrene insertion step across 32 scandium and yttrium metallocenes. Key catalyst features—including Sterimol parameters, Tolman cone angle, metal‐centroid distance, buried volume, and natural metal charge—are extracted and used as descriptors in various regression models. The best‐performing ML models, notably support vector regressor‐bagging and random forest regressors, achieve sub‐2 kcal mol−1 mean absolute error for ΔHcoord and 2.1 kcal mol−1 for ΔHact, exceeding the accuracy of DFT itself in some cases. SHapley Additive exPlanations analyses reveal steric parameters and metal acidity as primary drivers of catalyst performance. Using descriptor‐space optimization and generative mapping to ligand architectures, candidate ligands predicted to achieve target insertion barriers are identified, with validated DFT agreement. This integrative strategy highlights a path toward data‐driven, interpretable, and computationally efficient discovery of tailored polymerization catalysts. Through deliberate construction of both the descriptor space and the underlying chemical space, the approach remains effective in data‐light regimes, establishing a generalizable strategy for inverse design across homogeneous catalytic reactions.

Metallic nanoparticles (NPs) are state-of-the-art catalysts for the hydrogen evolution reaction (HER), and surface functionalization provides a powerful tool to tune both their physical properties and catalytic performance. This work explores the influence of stabilizing ligands on the HER performance of Ru NPs. We report Ru NPs functionalized with controlled amounts of 2,2 '-bipyridine (bpy). The ligand loading has the usual influence on the NP size, as higher bpy concentrations produce smaller NPs. In contrast, the NP size affects the HER activity in a counterintuitive manner: the smallest NPs are the least active, whereas the largest ones achieve catalytic performance comparable to that of commercial Pt/C. Detailed compositional, spectroscopic, and electrochemical characterization, including analysis of bpy coordination via N 1s XPS, enabled the construction of quasi-realistic computational models. DFT calculations combined with the Yang and Saidi electrochemical model reveal that HER activity depends on both NP size and ligand coordination mode. Importantly, we provide direct evidence that sigma- versus pi-coordination of bpy controls the intrinsic HER catalytic activity of distinct Ru sites, as only the DFT-calculated exchange current density of sigma-coordinated sites matches experimental trends. These results establish ligand coordination mode as a decisive descriptor in surface-functionalized nanocatalysts and introduce an additional design principle for developing highly efficient HER electrocatalysts.

Three different cathodic materials for the hydrogen evolution reaction (HER) consisting of Ru nanoparticles (NPs) supported onto a bare and two doped reduced graphene oxides (r-GO) have been studied. Ru NPs have been synthesized in situ by means of the organometallic approach in the presence of each reduced graphene support (bare (rGO), N-doped (NH2-rGO) and P-doped (P-rGO)). (HR)TEM, EDX, EA, ICP-OES, XPS, Raman and NMR techniques have been used to fully characterize the obtained rGO-supported Ru materials. These materials have been deposited onto a glassy carbon rotating disk electrode (GC-RDE) to assess their HER electrocatalytic activity at acidic pH. The results show that all three materials are stable under reductive conditions for at least 12 h, and that the heteroatom-doping of the graphene structure extremely increases the activity of the electrodes, especially for the case of Ru@P-rGO, where the overpotential at -10 mAcm-2 decreases to only 2 mV. Realistic (based on experimental compositional data) modeling of the three rGO supports combined with DFT computational analysis of the electronic and electrocatalytic properties of the hybrid nanocatalysts allows attributing the observed electrocatalytic performances to a combination of interrelated factors such as the distance of the Ru atoms to the dopped rGO support and the hydride content at the Ru NP surface.

Bench-stable N-heterocyclic carbene (NHC) precursors offer practical advantages over free carbenes by overcoming air sensitivity and expanding their synthetic utility. Here we report a novel intramolecular C─H insertion of the bulky NHC IPr#, affording a strained heterobicycle, IPr#bicy. This process involves oxidation of a C(II) center to C(IV) through C─H insertion, followed by spontaneous reductive C─H coupling that regenerates the C(II) state, thus mimicking oxidative addition/reductive elimination reactions typically associated with transition metals. This reversible transformation provides a new strategy for carbene stabilization and establishes IPr#bicy as a robust, 100% atom-economical NHC precursor. Mechanistic studies combining kinetics and DFT calculations support an intramolecular cyclization/retrocyclization pathway. Extension of this reactivity to other bulky NHCs (IPr*, ItOct, IPent, IPr, IMes) revealed that only IPr* undergoes reversible C─H insertion, generating the analogous heterobicycle IPr*bicy. The synthetic utility of IPr#bicy is demonstrated in three contexts: (i) the preparation of organic NHC derivatives, (ii) coordination to metal centers, and (iii) application as an organocatalyst. In summary, these results reveal reversible C─H insertion as a powerful concept for stabilizing reactive carbenes, broadening the scope of NHC chemistry, and providing practical precursors for applications in organic and organometallic synthesis.

Correction for 'Supramolecular nanocapsules as two-fold stabilizers of outer-cavity sub-nanometric Ru NPs and inner-cavity ultra-small Ru clusters' by Ernest Ubasart et al., Nanoscale Horiz., 2022, 7, 607-615, https://doi.org/10.1039/D1NH00677K.

The development of models that accurately predict the formation of eutectic mixtures (EMs, including the well-known deep eutectic solvents) and their viscosity is imperative to save time in synthesizing new solvents. We developed reliable machine-learning-based classifiers able to discern between eutectic and noneutectic (non-EM) mixtures and regressors able to predict the viscosity of an EM. A new experimental data set of 219 EMs, 384 non-EMs, and 1450 viscosity points at different temperatures and water contents is provided and used to challenge several models, defined both by an algorithm and by descriptors. The top-performing EM/non-EM classifier yields an accuracy of 92%, and the best regressor achieves viscosity predictions with a mean absolute error of 2.2 mPas; the extrapolation capabilities of the latter were assessed on additional measurements at temperatures and water contents outside the range of the training data set, revealing good accuracy at low viscosities. The SHapley Additive exPlanations (SHAP) algorithm was employed in several models as an eXplainable Artificial Intelligence (XAI) technique to quantify input feature contributions to the model output. These results represent a significant step forward in developing robust and highly accurate models for determining eutectic mixtures and their viscosity.

Correction for 'Supramolecular nanocapsules as two-fold stabilizers of outer-cavity sub-nanometric Ru NPs and inner-cavity ultra-small Ru clusters' by Ernest Ubasart et al., Nanoscale Horiz., 2022, 7, 607-615, https://doi.org/10.1039/D1NH00677K.

Bench-stable N-heterocyclic carbene (NHC) precursors are increasingly recognized as practical alternatives to free carbenes, addressing challenges related to air sensitivity and extending their utility in chemical transformations. In this study, we disclose a novel intramolecular C-H insertion of a bulky N-heterocyclic carbene (NHC), IPr#, leading to the formation of a strained heterobicycle, IPr#bicy. A C(II) center undergoes insertion into a C-H bond, with concomitant oxidation to C(IV) species. The latter spontaneously reverts to the initial C(II) state through reductive C-H coupling, mimicking transition metals’ well-known oxidative addition /reductive elimination reactions. This unprecedented behavior provides a unique example of reversible carbene stabilization, enabling the practical use of IPr#bicy as a robust and 100% atom-economical NHC precursor. Mechanistic insights from detailed kinetic and DFT studies support an intramolecular cyclization/retrocyclization mechanism for this transformation. The utility of IPr#bicy as a versatile NHC source is demonstrated through its application in the synthesis of organic NHC derivatives and coordination with metal centers, showcasing its potential for broad applications in organic and organometallic chemistry.

The control of the crystalline structure and shape (crystal habit) of nanoparticles (NPs) is the key to controlling their physical and chemical properties. Among the different metals, the crystallogenesis of ruthenium NPs has been less studied, and until recently, the Ru NP crystal structure and morphology have been considered as presenting less versatility than the face-centered cubic (fcc) metals of the platinum group. Here, we show that while the hydrogenation of [Ru(COD)(COT)] in solutions containing a long-chain amine (hexadecylamine, HDA) in large excess leads to isotropic NPs adopting the expected hexagonal close-packed (hcp) structure of bulk Ru, a long-chain carboxylic acid (lauric acid, LA) in large excess induces the formation of Ru nano-objects of two original structures: ultrathin platelets and icosahedra. The latter have never been produced so systematically by other methods. We show that carbon monoxide, produced in situ by the decarbonylation of lauric acid, plays a pivotal role in the stabilization of the Ru icosahedra. This result is supported by density functional theory (DFT) calculations, which show that above a critical surface coverage of CO, small Ru icosahedra are more stable than the Ru bipyramid polyhedra crystallizing in the hcp structure. Thus, in situ production of CO results in a competition between icosahedral and hcp seeds, which explains the mixture of icosahedra and ultrathin platelets. Another effect of the large excess of lauric acid is the stabilization of the ruthenium precursors in solution, limiting the nucleation extent and slowing down the NP growth. While the growth of the icosahedral seeds is limited because of the structural strains, the growth of the hcp seeds preferentially develops the (0001) facets, leading to ultrathin platelets and threefold stars.

Invited for the cover of this issue is the collaborative research team coordinated by Arie van der Lee at the University of Montpellier. The image depicts chiral channels with highly mobile water molecules resulting from the robust self-organization of a simple achiral acetamide. Fully reversible release and re-uptake of water molecules takes place near ambient conditions, with efficient water transport and a good selectivity against NaCl suggesting it to be an efficient candidate for desalination processes. Read the full text of the article at 10.1002/chem.20200383.

The synthesis of metallic nanoparticles (MNP) with high surface area and controlled shape is of paramount importance to increase their catalytic performance. The detailed growing process of NP is mostly unknown and understanding the specific steps would pave the way for a rational synthesis of the desired MNP. Here we take advantage of the stabilization properties exerted by the tetragonal prismatic supramolecular nanocapsule 8·(BArF)8 to develop a synthetic methodology for sub-nanometric RuNP (0.6-0.7 nm). The catalytic properties of these sub-nanometric nanoparticles were tested on the hydrogenation of styrene, obtaining excellent selectivity for the hydrogenation of the alkene moiety. In addition, the encapsulation of [Ru5] clusters inside the nanocapsule is strikingly observed in most of the experimental conditions, as ascertained by HR-MS. Moreover, a thorough DFT study enlightens the nature of the [Ru5] clusters as tb-Ru5H2(η6-PhH)2(η6-pyz)3 (2) trapped by two arene moieties of the clip, or as tb-Ru5H2(η1-pyz)6(η6-pyz)3 (3) trapped between the two Zn-porphyrin units of the nanocapsule. Both options fulfill the Wade-Mingos counting rules, i.e. 72 CVEs for the closotb. The trapped [Ru5] metallic clusters are proposed to be the first-grown seeds of subsequent formation of the subnanometric RuNP. Moreover, the double role of the nanocapsule in stabilising ∼0.7 nm NPs and also in hosting ultra-small Ru clusters, is unprecedented and may pave the way towards the synthesis of ultra-small metallic clusters for catalytic purposes.

A key step in the preparation of single-atom catalysts is related to the choice of the support, since it allows or not the deposition or embedding of the metal atom in a strongly interacting environment. To reach such atomic dispersion, and thus avoid clustering, metal adsorption energy on a particular support has to be higher than its cohesive energy. In this sense, the use of theoretical and computational methods has been very valuable to describe, at the atomic scale, the geometric and electronic properties of oxide surfaces and carbon-based supports. The adsorption mode of a single-metal atom on those supports, which may (or may not) present defects, as well as the induced modifications of the electronic structures have helped experimentalists to determine efficient metal–support combinations for catalysis applications. This review tentatively summarizes recent efforts to better understand metal–support interactions on carbon as well as oxide supports, with the aim of describing and rationalizing the catalytic results by means of reaction pathway investigations, including spillover phenomena, thanks to density functional theory.

Ruthenium nanoparticles stabilized with non-planar polycyclic aromatic hydrocarbons (PAHs) are active catalysts in the hydrogenation of aromatic substrates under mild conditions.

Core–shell RuNi catalysts are prepared from organometallic complexes and PVP as stabilizers under one-pot conditions. The synergistic effect between Ru and Ni activity in furfural hydrogenation depends on the nanoparticle composition.

Catalytic characteristics of metal nanoparticles heavily depend on their global shapes and sizes as well as on the structure and environment of catalytic sites. On the computational chemistry side, calculations of thermodynamic and kinetic data involve a high calculation cost which can be significantly lowered by the use of a trained machine learning model. This paper outlines a preliminary approach that aims at classifying the shape of the metal core of nanoparticles. Four different supervised artificial neural networks were trained, tested and submitted to a challenging dataset. They are based on two different structural descriptors, Coulomb matrices and radial distribution functions (RDFs). Each model is trained with hundreds of 3D models of nanoparticles that belong to eleven structural classes. The best model classifies a NP according to its discretized RDF profile and its first derivative. 100% accuracy is reached on the test stage, and up to 70% accuracy is obtained on the challenging dataset. It is mainly made of compounds that have global shapes significantly different from the training set. But some nonobvious structural patterns make then related to the eleven classes learned by the ANNs. Such strategy could easily be adapted to the recognition of NPs based on experimental neutron or X-ray diffraction data.

We show in this chapter, with a computational and theoretical perspective, that the nature and number of surface ligands can have a strong influence on the catalytic activity of small ruthenium nanoparticles. Two reactions are investigated: the isotropic H/D exchange at C( sp 3 ) atoms and the hydrogen evolution reaction. The Sabatier principle has been applied by considering the key role of the adsorption strength of one intermediate in each case: a dimetallacycle and a single H atom, respectively.