Classification of explosive residues on organic substrates using laser induced breakdown spectroscopy

Classification of e-waste using machine learning-assisted laser-induced breakdown spectroscopy

Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapid and contactless classification of metals in electronic waste (e-waste) management. In the present research work, five types of aluminium alloys, because of their extensive use in structural, electrical and thermotechnical functions in the electronics industry, were taken. Laser-induced breakdown spectroscopy (LIBS), a spectral identifier technique, was employed in conjunction with machine learning (ML) classification models of AI. Principal component analysis (PCA), an unsupervised ML classifier, was found incapable to differentiate LIBS data of alloys. Supervised ML classifier was then trained (for 10-fold cross-validation) on randomly selected 80% and tested on 20% spectral data of each alloy to assess classification capacity of each. In most of the tested variants of K nearest neighbour (kNN) the resulting accuracy was lower than 30% but kNN ensembled with random subspace method showed improved accuracy up to 98%. This study revealed that an AI-based LIBS system can classify e-waste alloys rather effectively in a non-contactless mode and could potentially be connected with robotic systems, hence, minimizing manual labour.

Sensing with Femtosecond Laser Filamentation

Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 1013-1014 W/cm2 with a large distance ranging from meter to kilometers can effectively ionize, break, and excite the molecules and fragments, resulting in characteristic fingerprint emissions, which provide a great opportunity for investigating strong-field molecules interaction in complicated environments, especially remote sensing. Additionally, the ultrastrong intensity inside the filament can damage almost all the detectors and ignite various intricate higher order nonlinear optical effects. These extreme physical conditions and complicated phenomena make the sensing and controlling of filamentation challenging. This paper mainly focuses on recent research advances in sensing with femtosecond laser filamentation, including fundamental physics, sensing and manipulating methods, typical filament-based sensing techniques and application scenarios, opportunities, and challenges toward the filament-based remote sensing under different complicated conditions.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.

Chemical Classification of Explosives

This work comprehensively reviews some fundamental concepts about explosives and their two commonly used classifications based on either their velocity of detonation or their application. These classifications are highly useful in the military/legal field, but completely useless for the chemical determination of explosives. Because of this reason, a classification of explosives based on their chemical composition is comprehensively revised, discussed and updated. This classification seeks to merge those dispersed chemical classifications of explosives found in literature into a unique general classification, which might be useful for every researcher dealing with the analytical chemical identification of explosives. In the knowledge of the chemical composition of explosives, the most adequate analytical techniques to determine them are finally discussed.

Identification and classification of explosives using semi-supervised learning and laser-induced breakdown spectroscopy

Public places are often under threat from explosion events, which pose health and safety risks to the public. Therefore, the detection of explosive materials has become an important concern in the fields of antiterrorism and security. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated to be useful in identifying explosives but has limitations. This study focuses on using semi-supervised learning combined with LIBS for explosive identification. Labeled data were utilized for the construction of a semi-supervised model for distinguishing explosive clusters and improving the accuracy of the K-nearest neighbor algorithm. The method requires only minimal prior information, and the time for obtaining a large amount of labeled data can be saved. The results of our investigation demonstrated that semi-supervised learning with LIBS can be used to discriminate explosives from interfering substances (plastics) containing similar components. The algorithm exhibits good robustness and practicability.

Femtosecond laser induced breakdown spectroscopy based standoff detection of explosives and discrimination using principal component analysis

We report the standoff (up to ~2 m) and remote (~8.5 m) detection of novel high energy materials/explosive molecules (Nitroimidazoles and Nitropyrazoles) using the technique of femtosecond laser induced breakdown spectroscopy (LIBS). We utilized two different collection systems (a) ME-OCT-0007 (commercially available) and (b) Schmidt-Cassegrain telescope for these experiments. In conjunction with LIBS data, principal component analysis was employed to discriminate/classify the explosives and the obtained results in both configurations are compared. Different aspects influencing the LIBS signal strength at far distances such as fluence at target, efficiency of collection system etc. are discussed.

Advances in explosives analysis--part II: photon and neutron methods

The number and capability of explosives detection and analysis methods have increased dramatically since publication of the Analytical and Bioanalytical Chemistry special issue devoted to Explosives Analysis [Moore DS, Goodpaster JV, Anal Bioanal Chem 395:245-246, 2009]. Here we review and critically evaluate the latest (the past five years) important advances in explosives detection, with details of the improvements over previous methods, and suggest possible avenues towards further advances in, e.g., stand-off distance, detection limit, selectivity, and penetration through camouflage or packaging. The review consists of two parts. Part I discussed methods based on animals, chemicals (including colorimetry, molecularly imprinted polymers, electrochemistry, and immunochemistry), ions (both ion-mobility spectrometry and mass spectrometry), and mechanical devices. This part, Part II, will review methods based on photons, from very energetic photons including X-rays and gamma rays down to the terahertz range, and neutrons.

Rapid elemental analysis and provenance study of Blumea balsamifera DC using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera.

Influence of exothermic chemical reactions on laser-induced shock waves

Differences in the excitation of non-energetic and energetic residues with a 900 mJ, 6 ns laser pulse (1064 nm) have been investigated. Emission from the laser-induced plasma of energetic materials (e.g. triaminotrinitrobenzene [TATB], cyclotrimethylene trinitramine [RDX], and hexanitrohexaazaisowurtzitane [CL-20]) is significantly reduced compared to non-energetic materials (e.g. sugar, melamine, and l-glutamine). Expansion of the resulting laser-induced shock wave into the air above the sample surface was imaged on a microsecond timescale with a high-speed camera recording multiple frames from each laser shot; the excitation of energetic materials produces larger heat-affected zones in the surrounding atmosphere (facilitating deflagration of particles ejected from the sample surface), results in the formation of additional shock fronts, and generates faster external shock front velocities (>750 m s(-1)) compared to non-energetic materials (550-600 m s(-1)). Non-explosive materials that undergo exothermic chemical reactions in air at high temperatures such as ammonium nitrate and magnesium sulfate produce shock velocities which exceed those of the inert materials but are less than those generated by the exothermic reactions of explosive materials (650-700 m s(-1)). The most powerful explosives produced the highest shock velocities. A comparison to several existing shock models demonstrated that no single model describes the shock propagation for both non-energetic and energetic materials. The influence of the exothermic chemical reactions initiated by the pulsed laser on the velocity of the laser-induced shock waves has thus been demonstrated for the first time.

Spectroscopic observation of neutral carbon during photodissociation of explosive-related compounds in the vapor phase

We perform time-resolved laser-induced fluorescence measurements of mononitrotoluenes (MNTs) and dinitrotoluenes (DNTs) in nitrogen and air. We observe the multipeak emission spectrum of NO and find that the emission peak intensity in the 247-248 nm range is stronger than expected compared to the other NO emission peak intensities. This increased emission intensity is believed to be due to neutral carbon [C(I)], which has a strong emission peak at 247.85 nm. By comparing the ratios of integrated emission peak intensities with those expected from the Franck-Condon factors for NO, we are able to identify samples that exhibit C(I) emission. We show that the DNTs exhibit C(I) emission for gate delays of 1500 ns and beyond, while the MNTs exhibit C(I) emission for gate delays of only up to about 500 ns. Carbon deposits in the analysis chamber confirm the presence of C. Ambient NO in air enhances the observed NO+C(I) signal from MNTs and DNTs.

Influence of molecular structure on the laser-induced plasma emission of the explosive RDX and organic polymers

A series of organic polymers and the military explosive cyclotrimethylenetrinitramine (RDX) were studied using the light emission from a femtosecond laser-induced plasma under an argon atmosphere. The relationship between the molecular structure and plasma emission was established by using the percentages of the atomic species (C, H, N, O) and bond types (C-C, C═C, C-N, and C≡N) in combination with the atomic/molecular emission intensities and decay rates. In contrast to previous studies of organic explosives in which C2 was primarily formed by recombination, for the organic materials in this study the percentage of C-C (and C═C) bonds was strongly correlated to the molecular C2 emission. Time-resolved emission spectra were collected to determine the lifetimes of the atomic and molecular species in the plasma. Observed differences in decay rates were attributed to the differences in both the molecular structure of the organic polymers or RDX and the chemical reactions that occur within the plasma. These differences could potentially be exploited to improve the discrimination of explosive residues on organic substrates with laser-induced breakdown spectroscopy.

Influence of metal substrates on the detection of explosive residues with laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy is a promising approach for explosive residue detection, but several limitations to its widespread use remain. One issue is that the emission spectra of the residues are dependent on the substrate composition because some of the substrate is usually entrained in the laser-induced plasma and the laser-material interaction can be significantly affected by the substrate type. Here, we have demonstrated that despite the strong spectral variation in cyclotrimethylenetrinitramine (RDX) residues applied to various metal substrates, classification of the RDX residue independent of substrate type is feasible. Several approaches to improving the chemometric models based on partial least squares discriminant analysis (PLS-DA) have been described: classifying the RDX residue spectra together in one class independent of substrate, using selected emission intensities and ratios to increase the true positive rate (TPR) and decrease the false positive rate (FPR), and fusing the results from two PLS-DA models generated using the full broadband spectra and selected intensities and ratios. The combination of these approaches resulted in a TPR of 97.5% and a FPR of 1.0% for RDX classification on metal substrates.

Selective sampling and laser-induced breakdown spectroscopy (LIBS) analysis of organic explosive residues on polymer surfaces

A method for selective sampling and analysis of explosive residues on solid surfaces based on laser-induced breakdown spectroscopy (LIBS) is presented. Organic explosives are difficult to analyze when present as residues on organic materials. Under these circumstances LIBS suffers from the limitations imposed by the limited spectroscopic information available for the analysis. Since ablation and subsequent plasma formation are sensitive to the beam focal conditions and the pulse energy deposited on the surface, the choice of an appropriate set of experimental conditions increases the surface sensitivity of the analysis and hence a selective inspection of the residue in the absence of spectral contribution from the organic support analyzed. 2-Mononitrotoluene (MNT), 2,6-dinitrotoluene (DNT), and 2,4,6-trinitrotoluene (TNT) are used as model residues, whereas nylon and Teflon are used as illustrative surfaces of daily life objects. The results demonstrate that selective sampling is successfully achieved in all cases when the plasma formation threshold of the residues and the object is substantially different. Plasma imaging demonstrates that the species distribution along the plume changes with beam focal conditions, which is exploited here to further increase the selectivity of the approach.