Paper
Designing porous molecularly imprinted polymers via simulation of pre-polymerisation mixtures: a case study with trinitrotoluene
Pre-polymerization MD with SASA/FFV analysis predicts solvent-driven porosity in MIPs, matching experiments and SEM. This approach enables rational tuning of polymer surface area by solvent choice.
Paper
Computational investigation of the impact of metal–organic framework topology on hydrogen storage capacity
A topologically diverse MOF dataset, NU-topoMOF-2025, was constructed and screened to identify topology-based design rules for materials with enhanced hydrogen storage performance.
Paper
Quantum chemical screening of eutectic solvent components for insights into CO2 complexation mechanisms
Different eutectic solvent components are simulated to understand the thermodynamics associated with the different, potential CO2 complexation pathways.
Stable n-type organic small-molecule conductor enabled by chemically doped ternary components
The ternary blends of 2DQTT-o, N-DMBI, and PEO exhibit significant enhancements in the n-type doping efficiency and stability.
Paper
Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks
Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors.
Paper
Nanostructured liquid-crystalline ion conductors based on linear carbonate moieties: effects of oligooxyethylene and alkylene spacers on self-assembled properties and ionic conductivities
Ion-conductive 2D nanostructured liquid crystals containing linear carbonate moieties are developed. The complexes of these materials with lithium salts may have potential as self-assembled electrolytes in lithium-ion batteries.
Paper
Investigating the design of macromolecular-based inks for two-photon 3D laser printing
The relationship between the design of macromolecular inks and material properties of two-photon 3D laser printed structures is investigated.
Paper
Accelerating multicomponent phase-coexistence calculations with physics-informed neural networks
We develop a physics-informed machine learning workflow that accelerates multicomponent phase-coexistence calculations on the number, composition, and abundance of phases. The workflow is demonstrated for systems described by Flory–Huggins theory.
About this collection
Welcome to our online rolling collection of the hottest work published in MSDE. Here we feature recent MSDE articles highlighted as HOT by the handling editor or our referees.
Congratulations to all the authors whose articles are featured.