Quantum dots; Laser-induced breakdown spectroscopy; Chemical mapping; Filtration paper; Fluorescence microscopy;

In this study, the feasibility of Quantum dots (QDs) 2D distribution mapping on the substrate by Laser-Induced Breakdown Spectroscopy (LIBS) was examined. The major objective of this study was to describe phenomena occurring after applying aqueous solutions of QDs onto filtration paper. Especially, the influence of pH and presence of Cu2 + cations in QDs solutions on LIBS signal was investigated. Cadmium Telluride QDs (CdTe QDs) were prepared by formation of nanosized semiconductor particles in so called “one-pot” synthesis. CdTe QDs were capped by glutathione or by 3-mercaptopropionic acid. The technique described in this work allows detection of QDs injected on the selected substrate – filtration paper. Results obtained from LIBS experiments were collated with a comparative method, fluorescence microscopy, which showed variations in the distribution of QDs on the substrate surface and possibilities for quenching. Due to the immediate signal response, relatively simple instrumentation and automatization possibility, LIBS offers promising and fast alternative to other techniques, as it is able to detect also nanoparticles with no visible luminescence.

Multivariate data analysis (MVDA) is getting popular across the spectroscopic community. To assess accurate results, the obtained data should be preprocessed prior to utilization of any MVDA algorithm. The process of data normalization or “internal standardization” is widely used across a broad range of applications. In this manuscript we investigate the utilization of Laser-Induced Breakdown Spectroscopy (LIBS) coupled with MVDA. However, many articles regarding the use of MVDA on data from LIBS do not provide any information about the data pretreatment. This work describes the impact of LIBS data normalization approaches on MVDA classification accuracy. Also, the impact of classical data preprocessing (mean centering and scaling) exploiting the prior utilization of MVDA was studied. This issue was investigated exploiting simple soft independent modelling of class analogies algorithm. The findings were generalized for three sample matrices (steel, Al alloys, and sedimentary ores). Furthermore, the selection of an appropriate normalization algorithm is not trivial since the spectrum of each sample matrix is composed of a different number of elements and corresponding elemental lines.

Laser-Induced Breakdown Spectroscopy; Silver nanoparticles; Low pressure conditions; Sensitivity improvement;

In this work we studied the effect of vacuum (low pressure) conditions on the behavior of laser-induced plasma (LIP) created on a sample surface covered with silver nanoparticles (Ag-NPs), i.e. Nanoparticles-Enhanced Laser-Induced Breakdown Spectroscopy (NELIBS) experiment in a vacuum. The focus was put on the step by step optimization of the measurement parameters, such as energy of the laser pulse, temporally resolved detection, ambient pressure, and different content of Ag-NPs applied on the sample surface. The measurement parameters were optimized in order to achieve the greatest enhancement represented as the signal-to-noise ratio (SNR) of NELIBS signal to the SNR of LIBS signal. The presence of NPs involved in the ablation process enhances LIP intensity; hence the improvement in the analytical sensitivity was yielded. A leaded brass standard was analyzed with the emphasis on the signal enhancement of Pb traces. We gained enhancement by a factor of four. Although the low pressure had no significant influence on the LIP signal enhancement compared to that under ambient conditions, the SNR values were noticeably improved with the implementation of the NPs.