Analytical applications of single particle inductively coupled plasma mass spectrometry: a comprehensive and critical review

Single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) refers to the use of ICP-MS as a particle counting technique. When ICP-MS measurements are performed at very high data acquisition frequencies, information about (nano)particles containing specific elements and their dissolved forms can be obtained (element mass per particle, size and number and mass concentrations). As a result of its outstanding performance, SP-ICP-MS has become a relevant technique for the analysis of complex samples containing inorganic nanoparticles. This review discusses the maturity level achieved by the technique through the methods developed for the detection, characterisation and quantification of engineered and natural (nano)particles. The application of these methods in different analytical scenarios is comprehensively reviewed and critically discussed, with special attention to their current technical and metrological limitations. The emergent applications of SP-ICP-MS in the field of nanoparticle-tagged immunoassay and hybridization methods are also reviewed.

Classification of ancient Roman glazed ceramics using the neural network of Self-Organizing Maps

Artificial neural networks with unsupervised learning strategy known as Self-Organizing Maps were applied to classify ancient Roman glazed ceramics. Their clay ceramic bodies were analyzed by Inductively Coupled Plasma-Atomic Emission Spectroscopy and the chemical composition obtained was processed by this neural algorithm. The results obtained provide two types of information: firstly, classification of ceramic samples with identification of several groups and secondly, differentiation between the elemental chemical information. It was found that there are certain chemical elements which can be considered as principal and which can serve to differentiate between ceramics, whereas other elements give redundant information and do not contribute to sample differentiation. Seven chemical elements were considered principal and provide the necessary information. Two types of element were identified: 1- a group formed by common elements, such as: Ca, Fe, Mg, Mn and 2- another formed by optional elements: K or Na and Ba or Sr and Al or Ti.