Search

Calibration datasets can be extended by using artificial neural networks to transform data collected on different LIBS systems. Hence, regression performance can be improved.

Samples with different mechanical and physical properties were measured by LIBS under diverse experimental conditions. The results were used to train a neural network. By means of the neural network, the optimisation process was significantly reduced.

Microplastics found in the environment are often covered with a biofilm, which makes their analysis difficult. Therefore, the biofilm is usually removed before analysis, which may affect the microplastic particles or lead to their loss during the procedure. In this work, we used laser-based analytical techniques and evaluated their performance in detecting, characterizing, and classifying pristine and aged microplastics with a developed biofilm. Five types of microplastics from different polymers were selected (polyamide, polyethylene, polyethylene terephthalate, polypropylene, and polyvinyl chloride) and aged under controlled conditions in freshwater and wastewater. The development of biofilm and the changes in the properties of the microplastic were evaluated. The pristine and aged microplastics were characterized by standard methods (e.g., optical and scanning electron microscopy, and Raman spectroscopy), and then laser-induced breakdown spectroscopy (LIBS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were used. The results show that LIBS could identify different types of plastics regardless of the ageing and major biotic elements of the biofilm layer. LA-ICP-MS showed a high sensitivity to metals, which can be used as markers for various plastics. In addition, LA-ICP-MS can be employed in studies to monitor the adsorption and desorption (leaching) of metals during the ageing of microplastics. The use of these laser-based analytical techniques was found to be beneficial in the study of environmentally relevant microplastics.

Emission spectra yielded by laser-induced breakdown spectroscopy (LIBS) exhibit high dimensionality, redundancy, and sparsity. The high dimensionality is often addressed by principal component analysis (PCA) which creates a low dimensional embedding of the spectra by projecting them into the score space. However, PCA does not effectively deal with the sparsity of the analysed data, including LIBS spectra. Consequently, sparse PCA (SPCA) was proposed for the analysis of high-dimensional sparse data. Nevertheless, SPCA remains underutilized for LIBS applications. Thus, in this work, we show that SPCA combined with genetic algorithms offers marginal improvements in clustering and quantification using multivariate calibration. More importantly, we show that SPCA significantly improves the interpretability of loading spectra. In addition, we show that the loading spectra yielded by SPCA differ from those yielded by sparse partial least squares regression. Finally, by using the randomized SPCA (RSPCA) algorithm for carrying out SPCA, we indirectly demonstrate that the analysis of LIBS data can greatly benefit from the tools developed by randomized linear algebra: RSPCA offers a 20-fold increase in computation speed compared to PCA based on singular value decomposition.

Laser-induced breakdown spectroscopy (LIBS) data obtained from the elemental imaging of heterogenous samples were processed with various chemometric algorithms. The intention was to cluster obtained characteristic spectra and to provide additional information about the sample surface composition and distribution of individual matrices. However, there is a gray zone on the boundary of two matrices and the consequent clustering of the spectra obtained on this boundary is ambiguous. This paper focuses on the transition between two well-defined matrices in a simplified case for a better transparency in data visualization. Steel and aluminum samples that are represented by characteristic spectra with significantly distinct structures (e.g., different number of spectral lines). Using a carefully designed experiment, several Fe:Al ratios were ablated and analyzed by principal component analysis (PCA), self-organizing maps (SOM), and standard data metrics. This paper shows the strategy for the discrimination of unrecognized spectra and possibilities in their clustering.

The changes in aerosol physical properties were studied by applying Nanoparticle-Enhanced Laser Ablation Inductively Coupled Plasma Mass Spectrometry (NE-LA-ICP-MS). This approach was compared to conventional LA-ICP-MS method. Analyte signal enhancement was related to the particle number concentration of aerosol for three sizes of Au nanoparticles (NPs) applied on the metal sample surface in the form of droplets. The dependence of the number of generated particles on the laser fluence in the range from 0.5 to 10 J cm−2 was studied. A different shape of the particle size distribution (PSD) of NE-LA-ICP-MS and LA-ICP-MS aerosol was determined. The aerosol structure was additionally studied on filters using scanning electron microscopy (SEM). Compared to LA-ICP-MS, NE-LA-ICP-MS produced a larger proportion of small particles (

The goal of this work is to examine the effect of a third additional laser pulse on orthogonal double-pulse laser-induced breakdown spectroscopy (DP LIBS). Namely, a pre-ablation laser pulse and re-heating laser pulse were combined into triple-pulse LIBS (3P LIBS) to achieve plasma emission enhancement. The experiment was designed with emphasis on causing minimal sample damage by utilizing nanosecond laser pulses. It means that the energy of ablation laser pulses was set to the minimal value for which the intensities of selected spectral lines were above the limits of detection. This energy as well as the energies of the pre-ablation and the re-heating laser pulses were kept constant for all experimental arrangements. The interpulse delays for both DP LIBS and for 3P LIBS configurations were optimized for signal enhancements. The 3P LIBS results showed up to 5-fold improvement of the signal to background ratio compared with both DP LIBS arrangements and up to 228-fold improvement in comparison to conventional SP LIBS. In addition, it has been shown that the spectral lines with a higher value of excitation energy prove higher enhancement in both DP LIBS and 3P LIBS configurations. To explain this effect, the dimensions of ablation craters were measured for each configuration using a 3D optical microscope and the temperature was determined from the slope of the Boltzmann plot. It was shown that the pre-ablation causes an increase of ablation crater dimensions and the re-heating increases the plasma temperature.

The depth-resolved analysis by means of the laser-induced breakdown spectroscopy (LIBS) is a useful tool in the investigation of multi-layered structures of paintings. The LIBS technique is considered micro-destructive, as it is associated with the formation of the ablation crater. It is important to optimize the laser pulse parameters to minimize the crater size and also to avoid some possible side effects of the laser radiation, such as the material redeposition and the light- or heat-induced pigment discoloration. In the present work, mock-up painting samples were used to investigate the influence of laser radiation characteristics on the ablation process. The first LIBS set-up contains a Nd:YAG laser at the second harmonic frequency (laser wavelength 532 nm, pulse duration ~ 10 ns) and the second set-up is comprised of a modified laser-ablation system equipped with a Nd:YAG laser at the fourth harmonic frequency (laser wavelength 266 nm, pulse duration ~ 5 ns). The influence of different laser wavelengths and different laser energies on the properties of the craters was examined. The effects caused by the laser-ablation process were described.

The analysis of laser-induced breakdown spectroscopy (LIBS) data heavily relies on statistical approaches. Recently, the application of another well-established statistical tool has started gaining prominence in LIBS analysis: bootstrapping. In the present work, we investigate the impact of bootstrapping on LIBS data collected by various data acquisition strategies and provide some insights into the application of this powerful statistical tool. We show that bootstrapping primarily affects the standard deviation of the recorded spectra, while barely affecting the mean values. This limits the applicability of bootstrapping to specific cases which are briefly discussed.

Nowadays, laser-based techniques play a significant role in medicine, mainly in the ophthalmology, dermatology, and surgical fields. So far, they have presented mostly therapeutic applications, although they have considerable potential for diagnostic approaches. In our study, we focused on the application of laser-based spectroscopy in skin cancer assessment. Recently, lengthy and demanding pathological investigation has been improved with modern techniques of machine learning and analytical chemistry where elemental analysis provides further insight into the investigated phenomenon. This article deals with the complementarity of Laser-Induced Breakdown Spectroscopy (LIBS) with standard histopathology. This includes discussion on sample preparation and feasibility to perform 3D imaging of a tumor. Typical skin tumors were selected for LIBS analysis, namely cutaneous malignant melanoma, squamous cell carcinoma and the most common skin tumor basal cell carcinoma, and a benign tumor was represented by hemangioma. The imaging of biotic elements (Mg, Ca, Na, and K) provides the elemental distribution within the tissue. The elemental images were correlated with the tumor progression and its margins, as well as with the difference between healthy and tumorous tissues and the results were compared with other studies covering this topic of interest. Finally, self-organizing maps were trained and used with a k-means algorithm to cluster various matrices within the tumorous tissue and to demonstrate the potential of machine learning for processing of LIBS data.

Immunohistochemistry (IHC) and immunocytochemistry (ICC) are widely used to identify cancerous cells within tissues and cell cultures. Even though the optical microscopy evaluation is considered the gold standard, the limited range of useful labels and narrow multiplexing capabilities create an imminent need for alternative readout techniques. Laser-induced breakdown spectroscopy (LIBS) enables large-scale multi-elemental analysis of the surface of biological samples, e.g., thin section or cell pellet. It is, therefore, a potential alternative for IHC and ICC readout of various labels or tags (Tag-LIBS approach). Here, we introduce Tag-LIBS as a method for the specific determination of HER2 biomarker. The cell pellets were labeled with streptavidin-conjugated upconversion nanoparticles (UCNP) through a primary anti-HER2 antibody and a biotinylated secondary antibody. The LIBS scanning enabled detecting the characteristic elemental signature of yttrium as a principal constituent of UCNP, thus indirectly providing a reliable way to differentiate between HER2-positive BT-474 cells and HER2-negative MDA-MB-231 cells. The comparison of results with upconversion optical microscopy and luminescence intensity scanning confirmed that LIBS is a promising alternative for the IHC and ICC readout.

The improving performance of the laser-induced breakdown spectroscopy (LIBS) triggered its utilization in the challenging topic of soft tissue analysis. Alterations of elemental content within soft tissues are commonly assessed and provide further insights in biological research. However, the laser ablation of soft tissues is a complex issue and demands a priori optimization, which is not straightforward in respect to a typical LIBS experiment. Here, we focus on implementing an internal standard into the LIBS elemental analysis of soft tissue samples. We achieve this by extending routine methodology for optimization of soft tissues analysis with a standard spiking method. This step enables a robust optimization procedure of LIBS experimental settings. Considering the implementation of LIBS analysis to the histological routine, we avoid further alterations of the tissue structure. Therefore, we propose a unique methodology of sample preparation, analysis, and subsequent data treatment, which enables the comparison of signal response from heterogenous matrix for different LIBS parameters. Additionally, a brief step-by-step process of optimization to achieve the highest signal-to-noise ratio (SNR) is described. The quality of laser–tissue interaction is investigated on the basis of the zinc signal response, while selected experimental parameters (e.g., defocus, gate delay, laser energy, and ambient atmosphere) are systematically modified.

Immunohistochemistry (IHC) and immunocytochemistry (ICC) are widely used to identify cancerous cells within tissues and cell cultures. Even though the optical microscopy evaluation is considered the gold standard, the limited range of useful labels and narrow multiplexing capabilities create an imminent need for alternative readout techniques. Laser-induced breakdown spectroscopy (LIBS) enables large-scale multi-elemental analysis of the surface of biological samples, e.g., thin section or cell pellet. It is, therefore, a potential alternative for IHC and ICC readout of various labels or tags (Tag-LIBS approach). Here, we introduce Tag-LIBS as a method for the specific determination of HER2 biomarker. The cell pellets were labeled with streptavidin-conjugated upconversion nanoparticles (UCNP) through a primary anti-HER2 antibody and a biotinylated secondary antibody. The LIBS scanning enabled detecting the characteristic elemental signature of yttrium as a principal constituent of UCNP, thus indirectly providing a reliable way to differentiate between HER2-positive BT-474 cells and HER2-negative MDA-MB-231 cells. The comparison of results with upconversion optical microscopy and luminescence intensity scanning confirmed that LIBS is a promising alternative for the IHC and ICC readout.

Ablation geometry significantly affects the plasma parameters and the consequent spectroscopic observations in laser-induced breakdown spectroscopy. Nevertheless, plasmas induced by laser ablation under inclined incidence angles are studied to a significantly lesser extent compared to plasmas induced by standard orthogonal ablation. However, inclined ablation is prominent in stand-off applications, such as the Curiosity Mars rover, where the orthogonality of the ablation laser pulse cannot be always secured. Thus, in this work, we characterize non-orthogonal ablation plasmas by applying plasma imaging, tomography, and spectral measurements. We confirm earlier observations according to which non-orthogonal ablation leads to a laser-induced plasma that consists of two distinct parts: one expanding primarily along the incident laser pulse and one expanding along the normal of the sample surface. Moreover, we confirm that the former emits mainly continuum radiation, while the latter emits mainly sample-specific characteristic radiation. We further investigate and compare the homogeneity of the plasmas and report that inclined ablation affects principally the ionic emissivity of laser-induced plasmas. Overall, our results imply that the decreased fluence resulting from inclined angle ablation and the resulting inhomogeneities of the plasmas must be considered for quantitative LIBS employing non- orthogonal ablation.

In the last few years, LIBS has become an established technique for the assessment of elemental concentrations in various sample types. However, for many applications knowledge about the overall elemental composition is not sufficient. In addition, detailed information about the elemental distribution within a heterogeneous sample is needed. LIBS has become of great interest in elemental imaging studies, since this technique allows to associate the obtained elemental composition information with the spatial coordinates of the investigated sample. The possibility of simultaneous multi-elemental analysis of major, minor, and trace constituents in almost all types of solid materials with no or negligible sample preparation combined with a high speed of analysis are benefits which make LIBS especially attractive when compared to other elemental imaging techniques. The first part of this review is aimed at providing information about the instrumental requirements necessary for successful LIBS imaging measurements and points out and discusses state-of-the-art LIBS instrumentation and upcoming developments. The second part is dedicated to data processing and evaluation of LIBS imaging data. This chapter is focused on different approaches of multivariate data evaluation and chemometrics which can be used e.g. for classification but also for the quantification of obtained LIBS imaging data. In the final part, current literature of different LIBS imaging applications ranging from bioimaging, geoscientific and cultural heritage studies to the field of materials science is summarized and reviewed. 2020 The Authors. Published by Elsevier B.V.

International audience; We present results of the classification contest organized for the EMSLIBS 2019 conference. For this publication, we chose only the five best approaches and discussed their algorithm in detail. The main focus of the contest reflected both recent and long-term challenges of Laser-Induced Breakdown Spectroscopy (LIBS) data processing. The contest was designed with a purpose to raise a challenge in handling and processing a very large dataset, containing high-dimensional elemental spectra. For the contest, 138 samples were measured using a lab-based LIBS system. In total, the data set consisted of 70,000 spectra, separated into 12 classes according to their elemental composition. Due to its extensivity and complexity, the data set is unique within the LIBS community. The central idea was to simulate the so-called “out-of-sample” classification (i.e. according to similar elemental composition), implying various real-world applications. Even more, it reflects the current level of expertise in the LIBS community and the capability of the LIBS method itself.

The temporal evolution of laser-induced plasmas is studied in the orthogonal double-pulse arrangement. Both the pre-ablation mode (an air spark is induced above the sample surface prior to the ablation pulse) and the re-heating mode (additional energy is delivered into the plasma created by the ablation pulse) is considered. The plasmas are investigated in terms of the temporal evolution of their electron density, temperature, and volume. The plasma volumes are determined using a time-resolved tomography technique based on the Radon transformation. The reconstruction is carried out for both white-light and band-pass filtered emissivities. The white-light reconstruction corresponds to the overall size of the plasmas. On the other hand, the band-pass emissivity reconstruction shows the distribution of the atomic sample species (Cu I). Moreover, through spectrally resolved tomographic reconstruction, the spatial homogeneity of the electron density and temperature of the plasmas is also investigated at various horizontal slices of the plasmas. Our results show that the pre-ablation geometry yields a more temporally stable and spatially uniform plasma, which could be beneficial for calibration-free laser-induced breakdown spectroscopy (LIBS) approaches. On the contrary, the plasma generated in the re-heating geometry exhibits significant variations in electron density and temperature along its vertical axis. Overall, our results shed further light on the mechanisms involved in the LIBS signal enhancement using double-pulse ablation.

In this work we discuss how sample surface topography can significantly influence the laser ablation (LA) process and, in turn, the analytical response of the LA Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) method. Six different surface topographies were prepared on a certified aluminium alloy sample BAM 311 and SRM NIST 610 to investigate the phenomenon. All the samples were repetitively measured by LA-ICP-MS using a spot by spot analysis. The effect of laser fluence in the range of 1–13 J/cm2 was studied. For majority of measured isotopes, the ICP-MS signal was amplified with roughening of the sample surface. A stronger effect was observed on the Al alloy sample, where the more than sixty-time enhancement was achieved in comparison to the polished surface of the sample. Since the effect of surface topography is different for each analyte, it can be stated that surface properties affect not only the ICP-MS response, but also elemental fractionation in LA. The presented results show that different surface topographies may lead to misleading data interpretation because even when applying ablation preshots, the signal of individual elements changes. The utmost care must be taken when preparing the surface for single shot analysis or chemical mapping. On the other hand, by roughening the sample surface, it is possible to significantly increase the sensitivity of the method for individual analytes and supress a matrix effect.

Laser-induced breakdown spectroscopy is often combined with a multivariate black box model-such as support vector machines (SVMs)-to obtain desirable quantitative or qualitative results. This approach carries obvious risks when practiced in high-stakes applications. Moreover, the lack of understanding of a black-box model limits the user's ability to fine-tune the model. Thus, here we present four approaches to interpret SVMs through investigating which features the models consider important in the classification task of 19 algal and cyanobacterial species. The four feature importance metrics are compared with popular approaches to feature selection for optimal SVM performance. We report that the distinct feature importance metrics yield complementary and often comparable information. In addition, we identify our SVM model's bias towards features with a large variance, even though these features exhibit a significant overlap between classes. We also show that the linear and radial basis kernel SVMs weight the same features to the same degree.

In the present work, we introduce a possibility to improve the rapid prototyping process of selective laser melting (SLM) using laser-induced breakdown spectroscopy (LIBS) which provides a material analysis. SLM uses many disparate materials for manufacturing of parts. The elemental composition of raw materials and constructed parts is obtained from a characteristic spectrum, which is a result of LIBS measurement. We compared a high-end LIBS instrumentation with a low-cost one; the latter could be easily implemented to a SLM device. The measured data were processed using multivariate data analysis algorithms. First, the principal component analysis was employed for a visualization and dimensionality reduction. Second, the reduced data set was classified using support vector machines. Moreover, we have suggested a procedure for an automatized classification of materials and parts during the SLM process without any supervision of a spectroscopy-specialist.

An implementation of a fast, robust, and effective algorithm is inevitable in modern multivariate data analysis (MVDA). The principal component analysis (PCA) algorithm is becoming popular not only in the spectroscopic community because it complies with the qualities mentioned above. PCA is, therefore, often used for the processing of detected multivariate signal (characteristic spectra). Over the past decade, PCA has been adopted by the Laser-Induced Breakdown Spectroscopy (LIBS) community and the number of scientific articles referring to PCA steadily increases. The interest in PCA is not caused only by the basic need to obtain a fast data visualization on a lower dimensional scale and to inspect the most prominent variables. Most recently, PCA has also been applied to yield unconventional data analyses, i.e. processing of large scale LIBS maps. However, a rapid development of LIBS-related instrumentation and applications has led to some non-uniform methodologies in the implementation and utilization of MVDA, including PCA. Thus, in this work, we critically assess and elaborate on the approaches to utilize PCA in LIBS data processing. The aim of this article is also to derive some implications and to suggest advice in data preprocessing, visualization, dimensionality reduction, model building, classification, quantification and non-conventional multivariate mapping. This review reflects also other MVDA algorithms than PCA and consequently, presented conclusions and recommendations can be generalized.

Paintings represent composed materials arranged in successive layers. Development of a suitable method for a depth-profiling analysis is essential for acquiring the information on the stratigraphy as well as on the chemical composition of individual layers, revealing the pigments which had been used. In this study, a depth-resolved analysis of multi-layered model samples of historical easel paintings was performed by means of laser-induced breakdown spectroscopy (LIBS) in combination with non-invasive X-ray fluorescence (XRF). The LIBS analysis was carried out using modified laser ablation system, UP-266 MACRO, equipped with a Czerny-Turner spectrometer. The XRF analysis was performed by handheld spectrometer, Delta Premium. In LIBS experiments, a set of six spots was examined with 5, 10, 15, 20, 25 and 30 pulses respectively, for each of the studied paint samples. Digital and 3D optical microscopy was employed to measure individual layer thickness and to obtain the information on average ablation rates of each sample. The chemical composition of the model samples with each layer partly uncovered was known, and this enabled to directly compare the results obtained by LIBS depth profiling with a fast analysis carried out with the handheld XRF spectrometer. The LIBS depth profiling proved to be a suitable method to distinguish layers of a different material composition and estimate their thickness. The combined use of LIBS and XRF analyses offered essential complementary information on the elemental composition of analysed multi-layered samples.

In this work, we present an extensive dataset of laser-induced breakdown spectroscopy (LIBS) spectra for the pre-training and evaluation of LIBS classification models. LIBS is a well-established spectroscopic method for in-situ and industrial applications, where LIBS is primarily applied for clustering and classification tasks. As such, our dataset is aimed at helping with the development and testing of classification and clustering methodologies. Moreover, the dataset could be used to pre-train classification models for applications where the amount of available data is limited. The dataset consists of LIBS spectra of 138 soil samples belonging to 12 distinct classes. The spectra were acquired with a state-of-the-art LIBS system. Lastly, the composition of each sample is also provided, including estimated uncertainties., Measurement(s)emission spectrumTechnology Type(s)laser-induced breakdown spectroscopyFactor Type(s)classSample Characteristic - Environmentsoil Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11799207

A LIBS equipment operating at 532 nm was optimized and used for sulfur determination in concrete samples. The influence of He atmosphere in a gas-tight chamber (1000–200 mbar) on S I 921.29 nm line sensitivity, signal-to-background and signal-to-noise ratio was studied at gate delays 100–2000 ns. Wide range of gate delays from 500 to about 1000 ns and pressures from several hundreds of mbar to the atmospheric pressure can be used for the desired detection of sulfur. The LIBS quantification was done using a simple calibration method. A synthetic limestone enriched by defined amounts of sodium sulfate was newly employed for direct quantification of S in concrete. This powder material was pressed into pellets and ablated with the LIBS system. The average content of sulfur as SO3 in the samples was 0.41–0.70 wt% by LIBS and 0.43–0.61 wt% by a reference standard procedure employing gravimetry and Inductively Coupled Plasma Triple Quad Mass Spectrometry (ICP-QQQMS). The uncertainty of the yielded LIBS results covers also the dispersion of the points in the calibration line and ranges from 16 to 28% at the probability level of 95%. The uncertainty of the ICP-QQQMS results was almost 10%. No correction on different signal response on the limestone and on the concrete was necessary.

The purpose of this study is to present a novel algorithm to combine high-resolution X-ray Computed Tomography (HRXCT) and Double-Pulse Laser-Induced Breakdown Spectroscopy (DP LIBS) for geological samples' analysis. The ability to reveal the elemental composition of the internal structure is demonstrated on a rare geological sample from a unique Talnakh PGE–Cu–Ni sulfide deposit. The main point of the suggested approach is to assign the DP LIBS elemental maps to the different densities of the HRXCT data. This assignment is based on the coordinates of the HRXCT data (top-cross section) and DP LIBS elemental maps of the surface. The resolution and orientation of these two images are aligned in order to enable a comparison. Moreover, the volumetric information about individual materials within the sample was estimated.

Spectra acquired by laser-induced breakdown spectroscopy comprise both a valuable analyte signal and an undesired background signal originating from various sources. The latter is commonly suppressed, at least partially, by gating of the signal acquisition, or treated numerically during the data processing. The numerical treatment might lead to loss of information or introduction of spectral artefacts, depending on the applied methodology. Consequently, background subtraction might significantly influence both univariate and multivariate analysis of the LIBS data. While various baseline correction methods have recently been studied and compared for multivariate LIBS analysis, their influence on LIBS data remains unexplored. Therefore, the present work aims to elucidate the effects of numerical background estimation and subtraction on LIBS data in terms of limits of detection estimated by the signal-to-noise ratio method, considering several fundamentally different background estimation algorithms: polynomial fitting, heuristic estimation, wavelet smoothing, and non-parametric modelling. A threshold gate delay value was observed above which the numerical treatment of spectral background has to be done cautiously. In addition, it was found that the optimal measurement parameters and selection of the emission line yielding the best results depend on the planned spectral processing.

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods. (C) 2017 Elsevier B.V. All rights reserved.

The purpose of this study was to determine the toxicity of two different sources of cadmium, i.e. CdCl2 and Cd-based Quantum Dots (QDs), for freshwater model plant Lemna minor L. Cadmium telluride QDs were capped with two coating ligands: glutathione (GSH) or 3-mercaptopropionic acid (MPA). Growth rate inhibition and final biomass inhibition of L. minor after 168-h exposure were monitored as toxicity endpoints. Dose-response curves for Cd toxicity and EC50168h values were statistically evaluated for all sources of Cd to uncover possible differences among the toxicities of tested compounds. Total Cd content and its bioaccumulation factors (BAFs) in L. minor after the exposure period were also determined to distinguish Cd bioaccumulation patterns with respect to different test compounds. Laser-Induced Breakdown Spectroscopy (LIBS) with lateral resolution of 200µm was employed in order to obtain two-dimensional maps of Cd spatial distribution in L. minor fronds. Our results show that GSH- and MPA-capped Cd-based QDs have similar toxicity for L. minor, but are significantly less toxic than CdCl2. However, both sources of Cd lead to similar patterns of Cd bioaccumulation and distribution in L. minor fronds. Our results are in line with previous reports that the main mediators of Cd toxicity and bioaccumulation in aquatic plants are Cd2+ ions dissolved from Cd-based QDs.

Laser-induced plasma (LIP) is dependent on many experimental conditions, as well as on the material and type of the ablated sample. Typically, the spectral data from a LIP are used as an analytical tool. However, the problem is that the data suffer from instability of the plasma, matrix effect, shot-to-shot fluctuation, etc. One of the possibilities how to improve the performance of LIBS is a deep understanding of behaviour of the plasma at various times during its evolution. Therefore, we used shadowgraphy setup to observe the shockwave created by the explosion during ablation of the sample. Our goal was to observe differences in shadowgraphy data for four different samples, each of them with unique matrix. Then, we aimed to use the shadowgraphy data to calculate the initial energy of the expansion. For each sample, we used various laser energies between 7 and 50 mJ. Sedov-Taylor model was applied as it fit the measured data. The model accurately fitted the experimental data up to 1.3 μs from the ablation of the sample. For further time delays, a slightly different approach was proposed, using model's equation with a changed power exponent. Later, Sedov-Taylor and Jones models were employed to calculate the initial energy of the shockwave. The calculation revealed a similarity in the amount of energy that was needed to ablate steel and glass sample. As for the ablation of bronze and soft tissues, approximately half of that energy was needed. These results were then compared with thermal properties of the samples, where similar results can be seen.

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.

This paper presents a novel approach for processing the spectral information obtained from high-resolution elemental mapping performed by means of Laser-Induced Breakdown Spectroscopy. The proposed methodology is aimed at the description of possible elemental associations within a heterogeneous sample. High-resolution elemental mapping provides a large number of measurements. Moreover, typical laser-induced plasma spectrum consists of several thousands of spectral variables. Analysis of heterogeneous samples, where valuable information is hidden in a limited fraction of sample mass, requires special treatment. The sample under study is a sandstone-hosted uranium ore that shows irregular distribution of ore elements such as zirconium, titanium, uranium and niobium. Presented processing methodology shows the way to reduce the dimensionality of data and retain the spectral information by utilizing self-organizing maps (SOM). The spectral information from SOM is processed further to detect either simultaneous or isolated presence of elements. Conclusions suggested by SOM are in good agreement with geological studies of mineralization phases performed at the deposit. Even deeper investigation of the SOM results enables discrimination of interesting measurements and reveals new possibilities in the visualization of chemical mapping information. Suggested approach improves the description of elemental associations in mineral phases, which is crucial for the mining industry.