Real-time sensing of Chemical Warfare Agents (CWAs) is today a crucial topic to prevent lethal effects of a chemical terroristic attack. For this reason, the development of efficient, selective, sensitive and reversible sensoristic devices, able to detect by optical response ppm levels of these compounds, both in water and in air, is strongly required. Here we report the design and synthesis of a fluorescent nanosensor, based on carbon nanoparticles covalently functionalized with ethanolamine arms, which exploits the multitopic supramolecular interaction with nerve agents, ensuring highly efficient (logK 6.46) and selective molecular recognition. Moreover, given the aqueous dispersibility of carbon nanoparticles, these nanosensors ensure even higher sensitivity, detecting sub-ppt concentration of nerve agents in water, and sub-ppm level in air by using a common digital camera or a smartphone. Our results pave the way to an innovative class of low-cost reusable CWA sensors prompting, for the first time, the simultaneous detection of nerve agents through gaseous and aqueous media, thus extending the protection range to public water supplies.

We have resolved the molecular structure of a bulk oil/water interface that contains amphiphilic ligand molecules using a combination of X-ray and neutron reflectivity measurements for the first time. This new capability can greatly impact future work in the field of ion separation by phase transfer, i.e. liquid/liquid extraction.

Fluorescent carbon quantum dots (CDs) are synthesized and employed as fluorescent nanochemosensors for selective detection of amino acids. A detailed investigation of excitation–emission maps revealed that the fluorescence properties of CDs are intensely and strongly influenced by the interaction at the surface with different amino acids. The discrimination capability was demonstrated by tensor rank decomposition of the differences induced by the surface reaction in the excitation–emission maps and by means of a common machine learning approach based on artificial neural networks.