Livia Giordano

Oxidative degradation of carbonate solvents via C–H activation is observed on late transition metal oxide positive electrodes in Li-ion batteries. However, analogous reactivity of other classes of organic molecules on oxides is not well understood. Here, we used density functional theory calculations to show how C–H activation changes from deprotonation to oxidative hydrogen abstraction as the lattice oxygen activity increases by moving the Fermi level closer to the oxygen 2p-band center. By using a set of 23 small molecules with 50 nonequivalent C–H bonds, spanning a broad range of functional groups and substitutions, we show how relevant energies of the isolated molecules impact the driving force for degradation reactions at the surface of LiNiO2, an oxide with high lattice oxygen activity. We demonstrated that C–H bond oxidation free energy, measured as the free energy to substitute a C–H bond with a C–OH bond for the isolated molecule, is the descriptor with the most predictive power for the C–H activation driving force on an oxide with highly reactive lattice oxygen. Using this descriptor, we identified promising S- and F-containing compounds with enhanced stability toward C–H activation. These results pave the way for a rational design of novel electrolytes that minimize degradation and impedance growth of Ni-rich LiNixMnyCo1–x–yO2 (NMC) electrodes.

Computational modeling plays a central role in advancing our understanding of electrochemical reactions and may thus guide the rational development of sustainable energy technologies. Despite significant methodological progress, achieving predictive and quantitatively reliable simulations of electrified solid–liquid interfaces under operando conditions remains a major challenge. Recently, the European COST Action EU-CONCERT (European Collaborative Network on Electrocatalysis for Efficient Renewable Technologies) was launched with the aim of advancing atomistic modeling of electrochemical reactions through systematic benchmarking and cross-validation. This position paper outlines the consortium’s view of the current state of the art, identifies the priority methodological challenges to be addressed, and defines the benchmark tests that are urgently needed. This paper should serve as a guide to the community and a roadmap for the EU-CONCERT COST Action.

Background: Immigrant populations demonstrate lower attendance in organized breast cancer screening programs than non-immigrants. Evidence on interventions to increase attendance, and their effectiveness, remains limited. We aimed to map interventions implemented in nationally or regionally organized screening programs to enhance immigrants' attendance and to assess the extent of their systematic evaluation. Methods: We conducted a survey using an electronic questionnaire distributed through the International Cancer Screening Network mailing list and snowball sampling to identify interventions improving breast cancer screening attendance among immigrant populations. Twenty-seven screening programs participated. As the number of eligible programs reached was unknown, response rate could not be calculated. Findings should be considered descriptive rather than representative of international practice. Data were analyzed using descriptive statistics. Results: Most programs (70%, 19/27) lacked routine systems to monitor immigrants' attendance. Across 10 predefined categories, 111 interventions were reported. Of these, 23% (25/111) had been evaluated, and 9% (10/111) were reported by respondents to have increased attendance. The most frequently implemented interventions were translated materials (70%, 19/27), public advertisements (52%, 14/27), and collaboration with voluntary organizations (52%, 14/27). Evaluation rates varied: 16% (3/19) for translated materials, 21% (3/14) for public advertisements, and 50% (7/14) for voluntary sector partnerships. Conclusions: Considerable variation exists in data availability on immigrants' attendance. Few interventions have been rigorously evaluated. Strengthening monitoring systems and generating evidence are essential to promote equitable screening attendance, although current findings are partly based on self-reported data and should be interpreted with caution.

Layering two-dimensional (2D) materials into van der Waals bilayers provides an effective method to achieve innovative quantum states and tunable electronic properties. Exploring the extensive configurational space resulting from various layer combinations, twist angles, and stacking patterns presents a significant computing challenge. As the volume of accessible datasets expands, training machine learning models on conventional hardware may ultimately become excessively expensive, prompting the advancement of quantum machine learning (QML) methodologies for materials discovery. This study presents a quantum-enhanced machine learning framework for predicting the bandgaps of van der Waals bilayers utilizing quantum simulators. We start with conventional feature selection to ascertain the most important descriptors affecting electronic properties. The properties are subsequently encoded into quantum states utilizing parameterized quantum circuits. We evaluated three quantum feature maps, namely ZFeatureMap, ZZFeatureMap, and PauliFeatureMap, and observed task-dependent performance. For the classification task, ZFeatureMap with two repetitions gave the strongest quantum-classifier performance, whereas for Quantum Support Vector Regressor (QSVR) bandgap regression, PauliFeatureMap with one repetition achieved the lowest average root-mean-square error (RMSE). These results indicate that feature-map choice should be optimized separately for classification and regression rather than being interpreted as the universal superiority of a single feature map. Using these feature-map comparisons, we employ a QSVR and a variational quantum regressor to predict the bandgaps of 1850 semiconducting vdW bilayers, illustrating that QML may attain significant predictive accuracy despite limited training data. Overall, the results demonstrate the feasibility of a simulator-based QML workflow for materials-property prediction. The quantum classifiers are competitive with the classical support vector classifier (SVC) baseline for zero-gap/non-zero-gap classification, whereas the classical Support Vector Regression (SVR) achieves the lowest RMSE in the regression task. Therefore, the present study should be interpreted as an exploratory benchmark of hybrid classical–quantum models for van der Waals (vdW) bilayers rather than as evidence of a definitive quantum performance advantage.

Magnesium oxide (MgO) is a prototypical model for oxide–water interfaces and a technologically relevant material in catalysis, environmental remediation, and corrosion-resistant coatings. Here, the dissolution behavior and morphology evolution of MgO smoke nanocubes are investigated in aqueous media while simultaneously monitoring the self-adjusting solution pH under three initial conditions: distilled water (pH ≈ 6.4), mildly acidic HCl solution (pH = 6), and alkaline NaOH solution (pH = 11). Time-resolved TEM reveals that dissolution initiates preferentially at (110) edges, drives a progressive transition from cubic to truncated cubes and finally octahedral morphologies exposing (111) facets, and is accompanied by brucite [Mg(OH)2] formation whose extent depends on particle size and pH. Upon immersion in distilled water or a mildly acidic solution, the suspension pH rapidly rises to ≈11–12 within minutes, whereas this abrupt pH change is not observed when MgO is introduced directly into an already alkaline medium (pH 11). A subsequent quasi-stationary regime with pH maintained at 11–12 persists for days, showing that the vast majority of the observed morphological transformations in these systems actually proceed under strongly alkaline rather than nominally neutral or mildly acidic conditions. To rationalize these observations, density functional theory calculations are performed for ideal and defect-containing low-index MgO surfaces under H+-rich conditions, including protonated Mg-vacancy (VMgH2) defects relevant to nonoxidative dissolution. The calculations reveal a strong pH dependence of defective surface energies, pronounced anisotropy in the thermodynamic driving force for dissolution, and higher Mg2+ extraction barriers for compact (100) than for more open (110) and (111) terminations, consistent with the experimentally observed facet-dependent kinetics. These results present a unified, pH-dependent picture of MgO dissolution and morphology evolution in water, clarify that earlier “neutral” dissolution studies actually probed alkaline conditions, and offer guidelines for controlling MgO stability and Mg(OH)2 formation in aqueous media. They highlight how self-adjusted local pH and defect chemistry regulate dissolution, shape transformation, and passivation of oxide nanomaterials at solid–liquid interfaces relevant to environmental and energy-related (electro)chemical systems.

Lithium dendrite formation during cycling of solid-state lithium batteries (SSLB) is known for critically affecting the duration of these devices, which limits their application, despite their promising overall performances. In this study, we performed a first-principles computational analysis of lithium interaction with anatase TiO2 surfaces grafted with polyethylendioxide (PEO) – aiming at understanding the mechanism behind the self-healing behavior observed in batteries with composite electrolytes comprising nanoparticles of functionalized titania. We report a strong stabilization of Li species at the PEO/TiO₂ interface, which exposes several oxygen sites with good coordinative capability toward Li⁺ cations, whose formation is enabled by the reducible nature of TiO₂, prone to host extra electrons in the empty Ti(3d) states. The role of PEO/TiO₂ nanostructures in dissolving dendrites, thus, may go also beyond a bare mechanical effect, as previously proposed, and also involve a chemical process of Li coordination and oxidation at the polymer/oxide interface.

The NaFeNb­(PO₄)₃, NFNP, material has been designed as a candidate anode material for sodium-ion batteries, as in its pristine form it combines the presence of Fe­(III) and Nb­(V)available for possible reduction upon Na insertionallowing for the formal introduction of 3 Na ions at reasonable potentials, and the robust NASICON structure with open channels for Na migration. The NFNP material has been successfully obtained by the solid-state route and fully characterized in terms of structure and transport properties by means of diffraction, XAS, and DFT analysis. Although promising, the electrochemical testing reveals that the initially satisfactory results in terms of capacity and Coulombic efficiencies fade upon cycling. The in-depth operando investigation, with the implementation of in situ XRD and XAS, unveiled a phase transition upon cycling; this involves the formation and accumulation of a low-symmetry secondary phase delivering lower capacity related to the Nb redox couples.

An interpretable machine learning model was developed using DFT data to predict hydrogen adsorption energies, enabling accurate and transparent screening of HER catalysts with performance comparable to the intrinsic accuracy of DFT methods.

The underlying reaction mechanism in lithium-ion batteries remains poorly understood. We provide experimental and theoretical evidence that lithium intercalation occurs by coupled ion-electron transfer, where ion transfer across the electrode-electrolyte interface is facilitated by electron transfer to a neighboring redox site. Electrochemical measurements for a variety of common electrode and electrolyte materials reveal a universal dependence of the (de-)intercalation rate on Li ⁺ vacancy fraction, as well as temperature and electrolyte effects consistent with the theory, which could be used to guide the molecular design of lithium-ion battery interfaces.