Development of porous materials combining stability and high performance has remained a challenge. This is particularly true for proton-transporting materials essential for applications in sensing, catalysis and energy conversion and storage. Here we report the topology guided synthesis of an imine-bonded (C=N) dually stable covalent organic framework to construct dense yet aligned one-dimensional nanochannels, in which the linkers induce hyperconjugation and inductive effects to stabilize the pore structure and the nitrogen sites on pore walls confine and stabilize the H₃PO₄ network in the channels via hydrogen-bonding interactions. The resulting materials enable proton super flow to enhance rates by 2–8 orders of magnitude compared to other analogues. Temperature profile and molecular dynamics reveal proton hopping at low activation and reorganization energies with greatly enhanced mobility.

Large language models (LLMs) have gained widespread interest owing to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capabilities of LLMs, which would be required to improve models and mitigate potential harm. Here we introduce ChemBench, an automated framework for evaluating the chemical knowledge and reasoning abilities of state-of-the-art LLMs against the expertise of chemists. We curated more than 2,700 question–answer pairs, evaluated leading open- and closed-source LLMs and found that the best models, on average, outperformed the best human chemists in our study. However, the models struggle with some basic tasks and provide overconfident predictions. These findings reveal LLMs’ impressive chemical capabilities while emphasizing the need for further research to improve their safety and usefulness. They also suggest adapting chemistry education and show the value of benchmarking frameworks for evaluating LLMs in specific domains.

The advantages of two dimensional covalent organic framework membranes to achieve high flux have been demonstrated, but the capability of easy structural modification to manipulate the pore size has not been fully explored yet. Here we report the use of the Langmuir–Blodgett method to synthesize two ultrathin covalent organic framework membranes (TFP–DPF and TFP–DNF) that have a similar framework structure to our previously reported covalent organic framework membrane (TFP–DHF) but different lengths of carbon chains aiming to rationally control the pore size. The membrane permeation results in the applications of organic solvent nanofiltration and molecular sieving of organic dyes showed a systematic shift of the membrane flux and molecular weight cut-off correlated to the pore size change. These results enhanced our fundamental understanding of transport through uniform channels at nanometer scales. Pore engineering of the covalent organic framework membranes was demonstrated for the first time.

Periodic energy decomposition analysis shows the accuracy of GFN-xTB vs. PBE-D3/TZ2P for the PES of empty and guest-containing COF-1.

A silica-bound C-butylpyrogallol[4]arene chromatographic stationary phase was prepared and characterised by thermogravimetric analysis, scanning electron microscopy, NMR and mass spectrometry. The chromatographic performance was investigated by using C₆₀ and C₇₀ fullerenes in reverse phase mode via flash column and high-pressure liquid chromatography (HPLC). The resulting new stationary phase was observed to demonstrate size-selective molecular recognition as postulated from our in-silico studies. The silica-bound C-butylpyrogallol[4]arene flash and HPLC stationary phases were able to separate a C₆₀- and C₇₀-fullerene mixture more effectively than an RP-C₁₈ stationary phase. The presence of toluene in the mobile phase plays a significant role in achieving symmetrical peaks in flash column chromatography.

Metal-organic frameworks (MOFs) are a versatile class of materials with applications in gas storage, separations, and catalysis. Despite extensive research, one of the key factors hindering their broader deployment is the absence of reproducible and scalable synthetic protocols. To address this, we introduce FAIR-MOFs, a database designed to be Findable, Accessible, Interoperable, and Reusable (FAIR), comprising 45{,}700 curated experimental structures, 33{,}361 geometry-optimised structures, and 4{,}161 entries linked to reported synthesis conditions. Analysis of the dataset showed that the propensity of open-metal sites in MOFs is statistically associated with reaction temperature, metal salts, ligands, topology, and metal secondary building unit. Furthermore, we developed a retrosynthetic recommender that captures literature co-usage patterns among solvents, metal salts, and ligands. For any given component, the system suggests compatible reagents and retrieves MOFs prepared under similar conditions. Finally, we trained a graph-based neural network integrated with our MOF deconstruction module to predict most probable metal salts, ligands and solvents directly from 3D structures of experimental or hypothetical MOFs. Using this model, we successfully synthesised MOFs randomly selected from hypothetical MOF databases illustrating the potential of FAIR-MOFs to accelerate the discovery and enable data-driven synthesis of MOFs.