Designing effective catalysts is a key process for optimizing catalytic reactions, however, existing generative approaches are often limited to specific reaction classes and predefined fragment categories. Here, the authors present CatDRX, a catalyst discovery framework powered by a reaction-conditioned variational autoencoder to generate potential catalysts and predict their activities, integrating optimization and validation based on reaction mechanisms and chemical knowledge.

Sodium-ion batteries are a commercially viable option for sustainable energy storage, but their performance at low temperatures remains underexplored. Here, the authors present a sodium-ion battery pouch cell designed for ultra-low temperatures, demonstrating its performance in laboratory conditions at -25 °C and -50 °C, in the presence of wind and snow, and in combination with a solar cell at -100 °C.

Aggregation-induced emission (AIE) luminogens can enhance the photonic response of luminescent solar concentrators (LSCs), but their controlled polymerization to produce macromolecular networks for LSC structures remains underexplored. Here, the authors develop a series of copolymers based on methyl methacrylate and an AIE-active monomer (tetraphenyl ethylene methacrylate) through free radical and reversible addition-fragmentation chain transfer (RAFT) polymerization, showing that RAFT-based LSCs exhibit more consistent device performance.

Lipid nanoparticles (LNPs) are essential carriers for mRNA delivery, but lipid peroxide impurities in ionizable lipids can degrade into reactive aldehyde species, leading to loss of mRNA’s translation efficiency in vitro. Here, the authors underscore the importance of rigorous lipid stability and purity analysis to ensure the quality and stability of mRNA encapsulated in the LNPs.

Dietary fibre (DF) analysis is crucial for understanding its health benefits, yet traditional methods lack molecular detail. Here, the authors develop a 1H NMR spectroscopy method to directly analyze hydrolysed DF fractions with high recovery and fast preparation while revealing detailed structural information including monosaccharide composition, carbohydrate modifications and the degradation products.

C^α-tetrasubstituted amides and esters are biologically important but are typically constructed through several-step transformations. Here, the authors report the synthesis of α,ε-difunctionalized γ,δ-unsaturated amides and esters via a photocatalyzed three-component remote 1,5-difunctionalization of alkenes using a tandem CF3 radical addition/radical clock cleavage approach with Umemoto reagents.

Electrocatalytic nitrate reduction is widely studied, but under a range of conditions, with implications on catalyst performance. Here, the authors highlight potential pitfalls in comparing across (and within) existing literature and suggest approaches to assess electrocatalysts under common conditions.

Communications Chemistry is pleased to present a Collection of articles on triplet-harvesting materials. Here, the Guest Editors introduce the topic and look towards the future of the field.

Plastic waste management is challenged by the inefficiencies and environmental impact of traditional chemical recycling methods. Here, the authors explore the chemoenzymatic cascade depolymerization approach, which offers a promising and sustainable solution for transforming plastic waste into valuable products.

Communications Chemistry is pleased to introduce a Collection of research works focused on recent developments within the interdisciplinary field of Covalent chemical probes. Here, the Guest Editors highlight key themes and look towards the future of this research field.

Communications Chemistry is delighted to introduce a Collection of articles on the intricate subject of electrochemical interfaces. Here, the Guest Editors introduce the topic, outline some of the themes covered by the Collection content, and look to the future of the field.

The construction industry faces mounting pressure to reduce its carbon footprint, with cement production alone contributing over 6% of global greenhouse gas emissions. In recent work, machine learning models screened over 14,000 materials from scientific literature and 1 million rock samples, identifying a diverse set of secondary and natural materials that could partially replace global cement production.