Themed collection MSDE Recent HOT Articles

The versatility of metal–organic frameworks (MOF) towards hydrolysis of a range of biological molecules is explored in detail to underline specific challenges and exciting possibilities of developing MOFs as nanozymes.

This review summarized the fundamentals and recent developments of organic semiconductor morphology dependent organic-transistor-based chemical sensors, which also gives corresponding strategies toward high-performance device construction.

Flexible/stretchable electrets based on organic molecules and polymers, in particular, highly deformable liquid electrets are a promising candidate for future implantable/wearable soft electronics.

Ion beam engineering of surfaces is a promising way to tailor the surface properties. It provides control over depth and concentration of nanoparticles for SERS applications.

Depiction of red, green and blue emitters along with energy level diagram.

Graphene and its derivatives are used as metal-free photocatalysts because of their fascinating optoelectronic properties which can further be improved by chemical doping for advanced functional applications in the field of energy and environment.

This study presents a concept that focuses on the structure design of preceramic resin precursors by the sol–gel method for fabricating carbon–ceramic molecular sieving membranes.

Different eutectic solvent components are simulated to understand the thermodynamics associated with the different, potential CO₂ complexation pathways.

The ternary blends of 2DQTT-o, N-DMBI, and PEO exhibit significant enhancements in the n-type doping efficiency and stability.

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.

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.

The relationship between the design of macromolecular inks and material properties of two-photon 3D laser printed structures is investigated.

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.

This work uses automated network generation, specifically the Python-based tool Pickaxe, for retrosynthetic planning towards making potential corrosion inhibitors from a pool of candidate bioprivileged molecules.

Polyurethane-based polymers are designed with tailored thermo-responsive swelling-release behaviours, and the roles of different segments in the swelling process are clarified.

An in-depth understanding of corrosion inhibitor behaviour(s) at the metal–solution interface governed by unique molecular features is the key premise to realising molecular tailoring for pronounced metal protection.

We used machine learning to predict the self-assembly structures of amphiphilic molecules and analyzed the physical factors affecting their morphologies.

The regulation of the polymerization process of moleculars from liquid to solid is crucial for the ultimate mechanical properties of composite materials.

A new class of fluorophores based on a twisted tetraazaperylene core has been employed in the fabrication of emissive organic 3D microstructures using two-photon laser printing.

Understanding how sequence modification, self-assembly, stereochemistry and affinity for zinc ions contribute to catalytic function of histidine-rich metal-dependent peptides.

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage.

This work demonstrates a rational precursor design concept for simple and scalable synthesis of ordered mesoporous carbon materials.

Experimental and computational study of the mechanism of conversion, via pyrolysis, of pV4D4 films into silica ceramics and of the impact of preparation conditions on their final structure.

A new representation for intrinsically disordered protein sequences is developed for usage in both classical and deep learning machine learning models.

Enabling the photochemical redox process of organic chromophores in water.

Halogenated paracyclophanetetraenes (PCTs) for the integration of the PCT motif into larger conjugated molecules by cross-coupling reactions are introduced.

PolyMOFs prepared from mixtures of multivalent polymer linkers and free linkers display tunable surface areas and improved CO₂ uptake compared to native MOF-5 and polyMOF-5.

Neglecting linker rotation in molecular simulations of methane in UiO-66 can have a significant impact on methane diffusion and moderate impact on methane uptake.

Radical-based molecules are employed as functional materials in organic electronic devices.

The smooth overlap of atomic positions (SOAP) descriptor represents an increasingly common approach to encode local atomic environments in a form readily digestible to machine learning algorithms.

Charged nanoparticles can significantly increase rectification in bipolar nanopores but this effect is largely dependent on the pore's geometry, its charge distribution, and the sign of the nanoparticles' charge.

A critical balance between high solvent self-diffusivity in short-chain solvents and low solvation free energy in long-chain solvents leads to an optimal solvent size for achieving high ionic conductivity for fluoroether solvents.

In organic π-conjugated materials, side chains play important roles that impact far beyond solubility.

High-throughput screening based on molecular simulation, machine learning and black-box optimization identifies promising candidates for forming self-assembled peptide vesicles.

The power conversion efficiency of the ternary PSCs (16.32%) is higher than that for binary counterparts, i.e., 13.16% and 12.62% for P(DTB-BDD):DBTBT-IC and P(DTB-BDD):Y6, respectively.

A method for the effective calculation of transmission probabilities for processes of molecular sieving is presented and tested against Eyring theory by comparison to Molecular Dynamics simulations.

Machine learning (ML) has emerged as a promising technology to accelerate materials discovery.

Non-conservative and permutationally-invariant Markov state models inform understanding and control of self-assembling optical matter systems.

We report the electrical conductivity enhancement of a perylene-based MOF upon partial ligand oxidation. The conductivity enhancement is rationalised by quantum-chemical calculations, supporting a through-space hopping transport.

The knowledge leveraged from adsorption property can be used to induce enhancements in the prediction of diffusion property within metal–organic frameworks.

We show how the tribofilm growth rate of zinc dialkyldithiophosphate (ZDDP) changes for different alkyl substitutents under carefully controlled temperature and stress conditions.

Segregation reduces the chemical diversity but increases the number of catalytically active sites of the dominant element at the surface.

Movable cross-network elastomer with knitting polymers (KP elastomer) were obtained by bulk polymerization of main chain monomers in presence of host polymer. The two design strategies successfully improve the toughness and stiffness of the KP elastomers.