Accounts of Chemical Research最新文献

Background and purpose: During epidemics with an increased prevalence of pulmonary infections, extending stroke CTA examinations of acute stroke work up to the whole chest may allow for the identification of pulmonary findings that would have been missed on standard CTA examinations.

Materials and methods: Our analysis comprised 216 patients with suspicion of stroke who received extended full-chest cerebrovascular CTA examinations from January 27th 2020 - date of the first confirmed Covid-19 case in Germany - until April 30th 2020.

Results: Consolidations and ground-glass opacifications were found in 73 of all 216 patients (34%). Opacifications were found in the upper chest in 51/216 patients (23%). There were lower-chest opacifications in 22 of 165 patients (13%) with unsuspicious upper-chest scans. In these 22 patients, there were consolidations in 10 cases (45%), ground-glass opacifications in 10 cases (45%), and both in 2 cases (10%).

Conclusion: Our study showed that extending the scan volume of an emergency stroke CTA to the whole chest reveals a considerable number of opacifications that would have been missed on a standard CTA. Even though these findings were rarely indicative of COVID-19, a large number of opacifications warranted further investigation.

The spin-promotion effect in the oxygen evolution reaction (OER) in ferromagnetic (FM) materials has recently received increasing interest. However, the mechanistic picture has not been clarified. Here, we present a comprehensive overview of the mechanisms behind the spin-promotion effect in the OER, bridging the atomic-scale spin interaction with the macroscopic role of the magnetic domains. As a conclusion, FM catalysts have a spin-promotion effect on the OER at a high pH electrolyte. The high pH enables the formation of M–O• oxyl radicals through surface deprotonation. The spins of the single electrons on two neighboring M–O• oxyl radicals are aligned on a magnetic domain but not aligned on a magnetic domain wall. Thus, the triplet O₂ formation is faster on a magnetic domain than on a domain wall. Magnetization leads to converting domain-wall regions to domain ones, and thus the promotion effect can be observed. Such an increment is determined by the domain structure of the magnetic catalysts, i.e., nonsignificant domain wall occupation cannot lead to a remarkable increment by spin-promotion effect. However, one should be reminded that any catalysts can give remarkable increments at a high current density when applied with an external magnetic field, which is due to the promoted bubble removal.

Nanoscale confinement profoundly reshapes the physical and chemical behavior of water and gases: transition conditions, phase stability, and kinetics can deviate dramatically from bulk expectations, yet many “macroscopic” relations hold for strikingly small systems. These effects pervade porous materials, atmospheric aerosols, membranes, and electrochemical interfaces. This Account asks when classical capillary laws remain predictive at molecular scales and why they fail. Using molecular dynamics and grand canonical Monte Carlo simulations, we examine phenomena where curvature and interfaces dominate─capillary condensation and evaporation in nanopores, nanodroplet and nanobubble formation and stability, wetting on chemically patterned surfaces, and electrochemically generated bubbles at solid–liquid interfaces. We organize these systems using three descriptors─confinement, surface chemical heterogeneity, and observational time scale─which together determine whether fluctuations self-average into continuum-like behavior. A central conclusion emerges: relations such as Kelvin, Young–Laplace, Henry’s Law, and Cassie–Baxter retain predictive power down to aggregates of ∼30 molecules provided features are large enough and observations long enough for interfacial fluctuations to equilibrate. Departures arise as confinement intensifies or measurements probe short windows: line and boundary energies, hydrogen-bond microrugosity, and contact-line pinning introduce terms neglected by the macroscopic approximation. A recurring crossover at 1 to 2 nm delineates the regimes of the behavior: above it, additivity and capillary relations are recovered; below it, mixtures can exceed Cassie additivity, nucleation barriers and hysteresis shrink and merge, and metastable nanobubbles give way to transient, oscillating gas clusters. Within nanopores, hysteresis narrows with confinement and can be minimized by deliberate chemical patterning that partitions a single nucleation barrier into staged steps, sharpening reversibility without shifting the equilibrium condensation pressure. On chemically patterned surfaces, Cassie–Baxter additivity fails when heterogeneity is molecular-sized and recovers as features coarsen toward the crossover scale. For surface nanobubbles, hydrophobic binding patches larger than ∼2 nm sustain metastable states whose growth and dissolution follow macroscopic relations, whereas smaller or more curved sites erase the metastable minimum. Under electrochemical driving that produces gases, electrode-bound bubbles reach stationary nonequilibrium states and can transition to nonstationary cycling of nucleation–growth–release when gas generation outpaces dissolution; the onset and bounds of these regimes are captured by simple capillary balances. Together, these results delineate the boundary of predictiveness of capillary thermodynamics and sharpen a picture in which length scale, surface heterogeneity, and observational time scale jointly gover

This Account discusses recent progress and challenges in binding free energy computations, focusing on two classes of enhanced sampling techniques: alchemical transformations and path-based methods. Binding free energy is a crucial metric in drug discovery, as it measures the affinity of a ligand for its target receptor. Free energy and affinity guide the ranking and selection of potential drug candidates. The theoretical foundations of free energy calculations were established several decades ago, but their efficient application to drug-target binding remains a grand challenge in computational drug design. The main obstacles stem from sampling issues (as binding is a rare event), force field accuracy limitations, and simulation convergence. Alchemical transformations are now the most used methods for computing binding free energies in the pharmaceutical industry. However, while they efficiently calculate energy differences, the application of these methods is often limited to relative binding free energy calculations. Absolute and accurate (error < 1 kcal/mol) binding free energy predictions remain one of the great challenges for computational chemists and physicists. Another limitation of alchemical methods is that they lack the ability to provide mechanistic or kinetic insights into the binding process, crucial for optimizing lead compounds and designing novel therapies. Path-based methods offer, in principle, the possibility to accurately estimate absolute binding free energy while also providing insights into binding pathways and interactions.

This Account explores recent advances in binding free energy methods for drug-target recognition and binding. In particular, we discuss the similarities and differences between alchemical and path-based approaches, highlighting recent innovations in both families of methods and providing perspectives from our group’s contributions. We examine the foundational role of alchemical methods, which have been employed since the inception of free energy calculations, in both equilibrium and nonequilibrium contexts. We also emphasize the growing importance of path-based methods in drug discovery and their ability to predict binding and unbinding pathways, free energy profiles, and binding free energy estimates. In particular, the combination of path methods with machine learning has proven to be a powerful means for accurate path generation and free energy estimations. Building on our recent research, we discuss several path-based applications for drug discovery. Moreover, we focus on two semiautomatic protocols representing our group’s state-of-the-art in free energy calculations. The first protocol is based on MetaDynamics simulation. From this, a recent innovation is instead based on nonequilibrium simulations combined with nonequilibrium estimators. We discuss in depth the advantages and drawbacks of equilibrium and nonequilibrium approaches to drug-target binding free energy predictions.