Determining the hydronium pK[Formula: see text] at platinum surfaces and the effect on pH-dependent hydrogen evolution reaction kinetics.

Electrocatalytic hydrogen evolution reaction (HER) is critical for green hydrogen generation and exhibits distinct pH-dependent kinetics that have been elusive to understand. A molecular-level understanding of the electrochemical interfaces is essential for developing more efficient electrochemical processes. Here we exploit an exclusively surface-specific electrical transport spectroscopy (ETS) approach to probe the Pt-surface water protonation status and experimentally determine the surface hydronium pKa [Formula: see text] 4.3. Quantum mechanics (QM) and reactive dynamics using a reactive force field (ReaxFF) molecular dynamics (RMD) calculations confirm the enrichment of hydroniums (H3O[Formula: see text]) near Pt surface and predict a surface hydronium pKa of 2.5 to 4.4, corroborating the experimental results. Importantly, the observed Pt-surface hydronium pKa correlates well with the pH-dependent HER kinetics, with the protonated surface state at lower pH favoring fast Tafel kinetics with a Tafel slope of 30 mV per decade and the deprotonated surface state at higher pH following Volmer-step limited kinetics with a much higher Tafel slope of 120 mV per decade, offering a robust and precise interpretation of the pH-dependent HER kinetics. These insights may help design improved electrocatalysts for renewable energy conversion.

Turning the structure of the Aß42 peptide by different functionalized carbon nanotubes: a molecular dynamics simulation study.

Functionalized carbon nanotubes (CNTs) can inhibit the self-assembly of amyloid-beta (Aß) peptides. Under abnormal conditions, the structure of the Aß peptides undergoes a fundamental transformation, and this transformation will induce conformational conversions of other polymerized Aß peptides. Here, we explore the interactions between different functionalized CNTs and Aß42 peptides by molecular dynamics simulations. Our results show that compared to the original CNTs, the highly functionalized CNTs induce different adsorption patterns of the peptides. This adsorption pattern destroys the α-helix structure and increases the ß-turn and random coil content significantly. The hydrogen bonds formed by the peptide and water molecules or CNTs further reveal the reasons for the structural transformation of the peptide. Due to electrostatic interactions and π-π stacking interactions, some amino acids (such as Phe4, Lys16, Phe20, and Lys28) are tightly fixed on the surfaces, and other amino acids move around these amino acids to accelerate the unfolding and denaturation of the peptide. Our research shows that functionalized CNTs have excellent potential to inhibit the abnormal aggregation of Aß42 peptides. Our research also provides theoretical guidance in the design and synthesis of carbon nanomedicines for protein conformation diseases.

The Inhibition of SARS-CoV-2 3CL Mpro by Graphene and Its Derivatives from Molecular Dynamics Simulations.

At present, the most powerful new drugs for COVID-19 are antibody proteins. In addition, there are some star small molecule drugs. However, there are few studies on nanomaterials. Here, we study the intact graphene (IG), defective graphene (DG), and graphene oxide (GO) interacting with COVID-19 protein. We find that they show progressive inhibition of COVID-19 protein. By using molecular dynamics simulations, we study the interactions between SARS-CoV-2 3CL Mpro and graphene-related materials (GRMs): IG, DG, and GO. The results show that Mpro can be absorbed onto the surfaces of investigated materials. DG and GO interacted with Mpro more intensely, causing the decisive part of Mpro to become more flexible. Further analysis shows that compared to IG and GO, DG can inactivate Mpro and inhibit its expression effectively by destroying the active pocket of Mpro. Our work not only provides detailed and reliable theoretical guidance for the application of GRMs in treating with SARS-CoV-2 but also helps in developing new graphene-based anti-COVID-19 materials.

Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery.

X-ray photoelectron spectroscopy (XPS) is a powerful surface analysis technique widely applied in characterizing the solid electrolyte interphase (SEI) of lithium metal batteries. However, experiment XPS measurements alone fail to provide atomic structures from a deeply buried SEI, leaving vital details missing. By combining hybrid ab initio and reactive molecular dynamics (HAIR) and machine learning (ML) models, we present an artificial intelligence ab initio (AI-ai) framework to predict the XPS of a SEI. A localized high-concentration electrolyte with a Li metal anode is simulated with a HAIR scheme for ∼3 ns. Taking the local many-body tensor representation as a descriptor, four ML models are utilized to predict the core level shifts. Overall, extreme gradient boosting exhibits the highest accuracy and lowest variance (with errors ≤ 0.05 eV). Such an AI-ai model enables the XPS predictions of ten thousand frames with marginal cost.