ABSTRACT
In recent years, extensive research efforts have been dedicated to the investigation of CdSe/CdS-based quantum-confined nanostructures, driven by their distinctive properties. The morphologies of these nanostructures have been shown to directly affect their properties, an area which has proven to be an important field of study. Herein, we report a new morphology of CdSe/CdS core-shell heterostructures in the form of a 'nanonail' - a modified nanorod-like morphology, in which a distinctive triangular head can be observed at one end of the structure. In-depth studies of this morphology reveal a material with tuneable rod length and width, as well as exceptional photoluminescent properties. Following this, we have demonstrated the ability to induce chiroptical activity via ligand exchange, revealing the important role of the specific morphology, shell thickness and chiral ligand concentration in the effect of ligand induced chirality. In addition, the cellular uptake and cytotoxicity of obtained chiral nanostructures were evaluated on human lung-derived A549 cancer cells, revealing a significant enantioselectivity in biological activity. Finally, analysis on monolayers of the material demonstrate the complete absence of FRET processes. Overall, this CdSe/CdS heterostructure is another tuneable morphology of a very important nanomaterial, one which shows great advantages and a range of potential applications.
ABSTRACT
Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.
ABSTRACT
Boron nitride (BN) based 2D nanomaterials are an emerging class of materials for the development of new membranes for nanofiltration applications. Here, we report the preparation, characterisation and testing of highly promising nanofiltration membranes produced from partially oxidised BN (BNOx) 2D nanosheets. In our work, the partial oxidation of BN was successfully achieved by heating the bulk h-BN powder in air at 1000 °C, resulting in BNOx product. The characterisation of the sample showed the presence of B-OH groups corresponding to the partial oxidisation of the BN. The BNOx material was then exfoliated in water and used to produce membranes, using vacuum filtration. These membranes were characterised using electron microscopy, BET and mercury porosimetry techniques. The membranes have also been tested in water purification and removal of several typical water-soluble dyes, demonstrating outstanding retention values close to 100%. We believe that this research opens up new opportunities for further production, as well as chemical functionalisation and modification of membranes for nanofiltration and separation technologies.
