Shaoqi Zhan

We report a porphyrin-bridged Ru(bda) coordination polymer integrated with carbon nanotubes, constructed from the well-defined homogeneous water oxidation catalyst in acid. The porphyrin linker simultaneously provides redox activity and proton-buffering functionality, enabling acidic water oxidation at an ultralow Ru loading (0.021 mgRu·cm–2). The resulting composite reaches current densities up to 200 mA·cm–2 in acid, exceeding previously reported molecular water oxidation catalyst-based anodes by one to two orders of magnitude. At identical metal loadings, it also outperforms both RuO2 and IrO2 benchmarks in both activity and stability. Electrochemical, spectroscopic, and theoretical results support porphyrin protonation and reversible oxidation, suggesting that ligand redox activity, together with proton management, contributes to the enhanced catalytic performance. These findings underscore the transformative potential of ligand engineering in molecular integration strategies for advancing artificial photosynthetic energy conversion systems.

Perovskite photovoltaics are limited by structural instabilities initiated during crystallization and amplified under operation. We report that a lattice-matched Prussian blue scaffold directs heterogeneous nucleation to produce highly oriented, strain-relaxed films. Its redox-active Fe–C≡N–Fe network mediates the conversion of Pb ⁰ and I ⁰ defects, and its rigid open framework suppresses A-site cation redistribution and the resulting electronic inhomogeneity under bias. This strategy yielded champion power conversion efficiencies of 26.1% (n-i-p) and 26.9% (p-i-n; 26.2% certified), scaling to 23.4% in 6-centimeter–by–6-centimeter minimodules and a certified 22.9% in 30-centimeter–by–30-centimeter submodules. Submodules with initial power conversion efficiencies ranging from 20.2 to 21.0% showed robust durability under accelerated aging and no discernible decline relative to a silicon reference over 5 months of outdoor testing.

The development of efficient catalysts for nitrogen conversion to ammonia is critical for a sustainable alternative to the energy‐intensive Haber–Bosch process. Yet, rational catalyst design remains highly challenging, compounded by complex structure–function relationships within realistic conditions. Herein, we present an integrated computational framework combining quantum chemical calculations with 27 machine learning models to predict experimental catalytic metrics in metal–ligand complexes. The models are trained and validated on a large experimental database and demonstrate high predictive accuracy across multiple tasks. For classification, family 1 and family 2 catalysts achieved test accuracies up to 1. Regression models yield test R ² values of 0.91 and 0.88 for turnover frequency (TOF) and turnover number (TON) predictions in family 1, and 0.96 and 0.99 in family 2. Notably, the models accurately capture time‐dependent variability of TOF and TON for new complexes, with predicted values closely matching experimental results. Moreover, strong transfer learning capability is observed for structurally distinct coordination architectures. Feature interpretation reveals clear design principles for optimal catalysts involving metal spin state, ligand geometry, charge distribution, and experimental conditions. Together, this study established an efficient and practical framework for discovery and inverse design of high‐performance catalysts under realistic conditions, with broader relevance to electrocatalysis.

Density functional theory and microkinetic modeling show that an Au@NiO core–shell nanostructure enhances the oxidative esterification of aldehydes by electron transfer from NiO to Au, stabilizing intermediates and lowering activation energies.

Metal ions play a central functional and structural role in many molecular structures, from small catalysts to metal-organic frameworks (MOFs) and proteins. Computational studies of these systems typically employ classical or quantum mechanical approaches or a combination of both. Among classical models, only the covalent metal model reproduces both geometries and charge transfer effects but requires time-consuming parametrization, especially for supramolecular systems containing repetitive units. To streamline this process, we introduce metallicious, a Python tool designed for efficient force-field parametrization of supramolecular structures. metallicious has been tested on diverse systems, including supramolecular cages, knots, and MOFs. Our benchmarks demonstrate that parameters obtained from metallicious accurately reproduce the reference properties obtained from quantum calculations and crystal structures. MD simulations of the generated structures consistently yield stable simulations in explicit solvent, in contrast to similar simulations performed with non-bonded and cationic dummy models. Overall, metallicious facilitates the setup of molecular dynamics (MD) of supramolecular systems simulations, providing insights into their dynamic properties and host-guest interactions. The tool is freely available on GitHub (https://github.com/duartegroup/metallicious)

Fenton-like radical processes are widely utilized to explain catalytic mechanisms of peroxidase-like nanozymes, which exhibit remarkable catalytic activity, cost-effectiveness, and stability. However, there is still a need for a comprehensive understanding of the formation, stabilization, and transformation of such radicals. Herein, a copper formate-based nanozyme (Cuf-TMB) was fabricated via a pre-catalytic strategy under ambient conditions. The as-prepared nanozyme shows comparable catalytic activity (K_m, 1.02 × 10^-5 mM^-1; K_cat, 3.09 × 10^-2 s^-1) and kinetics to those of natural peroxidase toward H₂O₂ decomposition. This is attributed to the feasible oxidation by *OH species via the *O intermediate, as indicated by density functional theory calculations. The key ·OH radicals were detected to be stable for over 52 days and can be released in a controlled manner during the catalytic process via in situ electron spin-resonance spectroscopy measurements. Based on the understanding, an ultrasensitive biosensing platform was constructed for the sensitive monitoring of biochemical indicators in clinic settings.

The ERK pathway is one of the most important signaling cascades involved in tumorigenesis. So far, eight noncovalent inhibitors of RAF and MEK kinases in the ERK pathway have been approved by the FDA for the treatment of cancers; however, their efficacies are limited due to various resistance mechanisms. There is an urgent need to develop novel targeted covalent inhibitors. Here we report a systematic study of the covalent ligandabilities of the ERK pathway kinases (ARAF, BRAF, CRAF, KSR1, KSR2, MEK1, MEK2, ERK1, and ERK2) using constant pH molecular dynamics titration and pocket analysis. Our data revealed that the hinge GK (gate keeper)+3 cysteine in RAF family kinases (ARAF, BRAF, CRAF, KSR1, and KSR2) and the back loop cysteine in MEK1 and MEK2 are reactive and ligandable. Structure analysis suggests that the type II inhibitors belvarafenib and GW5074 may be used as scaffolds for designing pan-RAF or CRAF-selective covalent inhibitors directed at the GK+3 cysteine, while the type III inhibitor cobimetinib may be modified to label the back loop cysteine in MEK1/2. The reactivities and ligandabilities of the remote cysteine in MEK1/2 and the DFG-1 cysteine in MEK1/2 and ERK1/2 are also discussed. Our work provides a starting point for medicinal chemists to design novel covalent inhibitors of the ERK pathway kinases. The computational protocol is general and can be applied to the systematic evaluation of covalent ligandabilities of the human cysteinome.