Magnetic nanoparticles are a highly valuable substrate for the attachment of homogeneous inorganic and organic containing catalysts. This review deals with the very recent main advances in the development of various nanocatalytic systems by the immobilisation of homogeneous catalysts onto magnetic nanoparticles. We discuss magnetic core shell nanostructures (e.g., silica or polymer coated magnetic nanoparticles) as substrates for catalyst immobilisation. Then we consider magnetic nanoparticles bound to inorganic catalytic mesoporous structures as well as metal organic frameworks. Binding of catalytically active small organic molecules and polymers are also reviewed. After that we briefly deliberate on the binding of enzymes to magnetic nanocomposites and the corresponding enzymatic catalysis. Finally, we draw conclusions and present a future outlook for the further development of new catalytic systems which are immobilised onto magnetic nanoparticles.

This review presents recent advances in the development of various chiroptically active quantum nanostructures.

The utilisation of chiral penicillamine stabilisers allowed the preparation of new water soluble white emitting CdS nano-tetrapods, which demonstrated circular dichroism in the band-edge region of the spectrum.

Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.