A review of laser-induced breakdown spectroscopy for plastic analysis

LIBS as a novel tool for the determination of the imidization degree of polyimides

Due to their outstanding chemical and physical properties, polyimides are widely used in industrial applications. The degree of imidization of polyimides significantly influences their properties, making it an important factor in tailoring the material for specific applications. Imidization refers to the process of converting a precursor poly(amic acid) by removing water, and it is essential to analyze this process in detail to tune the final structure and properties of the material. Conventional techniques for this task include Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), or differential scanning calorimetry (DSC), but they lack the possibility of spatially and/or depth-resolved analysis or do not enable in-line monitoring capabilities. To overcome these limitations, we propose laser-induced breakdown spectroscopy (LIBS) as a powerful tool for the monitoring of the imidization reaction. To establish a measurement method, a total of 130 in-house prepared, self-synthesized samples were thermally cured to exhibit varying imidization degrees. IR spectroscopy served as a reference technique during method development, and a novel formula for calculating the degree of imidization, based on the C2 and H signal trends, was introduced. The calculated imidization degrees of model thin films based on LIBS were in good accordance with the IR reference method although minor differences between the two methods were expected due to varying information depth and the size of the sampled area. Additionally, the robustness of the procedure was demonstrated by depth profiling of a stacked model polymer, spiking with commercially available additives and, ultimately, by analyzing industry-relevant polymer samples.

Capabilities of simultaneous 193 nm - LIBS/LA-ICP-MS imaging for microplastics characterization

Microplastics (MPs) are currently one of the major environmental challenges within our society. With the awareness of the impact of MPs on the environment increasing over the last years, the need for increased monitoring as well as comprehensive analysis to better understand the fate and impact of MPs has become more and more important. A major aspect of MP characterization is the assignment of the polymer type of individual particles. Here, per- and poly-fluoroalkyl substances (PFAS), originating from fluor-containing polymers, have gained a lot of attention due to the severe environmental impact. Additionally, quantitative analysis of the metal content is of great interest in the field, since MPs are prone to either leaching (in)organic additives into the environment or taking up and accumulating hazardous components (e.g., heavy metals). In this work we demonstrate the capabilities of a simultaneous LIBS/LA-ICP-MS setup for the analysis of MPs. In the first part, we demonstrate the potential of targeted LIBS analysis for the imaging of fluor-containing polymers. Using a laser spot size of 5 μm combined with highly sensitive ICCD detection enables analysis of particles in the low μm range. In the second part we combine the polymer-identification capabilities of LIBS with the high sensitivity of ICP-MS to perform matrix-matched quantification of the metal content of individual MPs. In this case we use a spot size of 50 μm facilitating polymer classification with a broadband spectrometer, resulting in detection limits of 0.72 μg/g for Pb and 9.5 μg/g for Sn simultaneously measured using ICP-MS.

Portable Pyrolysis-Point Discharge Optical Spectrometer for In Situ Plastic Polymer Identification by Coupling with Machine Learning

Rapid and in situ identification of specific polymers is a challenging and crucial step in plastic recycling. However, conventional techniques continue to exhibit significant limitations in the rapid and field classification of plastic products, especially with the wide range of commercially available color polymers because of their large size, high energy consumption, and slow and complicated analysis procedures. In this work, a simple analytical system integrating a miniature and low power consumption (22.3 W) pyrolyzer (Pyr) and a low temperature, atmospheric pressure point discharge optical emission spectrometer (μPD-OES) was fabricated for rapidly identifying polymer types. Plastic debris is decomposed in the portable pyrolyzer to yield volatile products, which are then swept into the μPD-OES instrument for monitoring the optical emission patterns of the thermal pyrolysis products. With machine learning, five extensively used raw polymers and their consumer plastics were classified with an accuracy of ≥97.8%. Furthermore, the proposed method was applied to the identification of the aged polymers and plastic samples collected from a garbage recycling station, indicating its great potential for identification of environmentally weathered plastics. This portable Pyr-μPD-OES system provides a cost-effective tool for rapid and field identification of polymer types of recycled plastic for proper management and resource recycling.

Application of Laser-Induced Breakdown Spectroscopy for Depth Profiling of Multilayer and Graded Materials

Laser-induced breakdown spectroscopy (LIBS) has emerged as a powerful analytical method for the elemental mapping and depth profiling of many materials. This review offers insight into the contemporary applications of LIBS for the depth profiling of materials whose elemental composition changes either abruptly (multilayered materials) or continuously (functionally graded or corroded materials). The spectrum of materials is discussed, spanning from laboratory-synthesized model materials to real-world products including materials for fusion reactors, photovoltaic cells, ceramic and galvanic coatings, lithium batteries, historical and archaeological artifacts, and polymeric materials. The nuances of ablation conditions and the resulting crater morphologies, which are instrumental in depth-related studies, are discussed in detail. The challenges of calibration and quantitative profiling using LIBS are also addressed. Finally, the possible directions of the evolution of LIBS applications are commented on.

Advancing Plastic Recycling: Challenges and Opportunities in the Integration of 3D Printing and Distributed Recycling for a Circular Economy

The concept of the circular economy has emerged as a promising solution to address the mounting concerns surrounding plastic waste and the urgent need for sustainable resource management. While conventional centralized recycling remains a common practice for plastic waste, centralized facilities may prove inadequate in handling the ever-increasing volumes of plastic waste generated globally. Consequently, exploring alternative recycling methods, such as distributed recycling by additive manufacturing, becomes paramount. This innovative approach encompasses actively involving communities in recycling practices and promotes a circular economy. This comprehensive review paper aims to explore the critical aspects necessary to realize the potential of distributed recycling by additive manufacturing. In this paper, our focus lies on proposing schemes that leverage existing literature to harness the potential of distributed recycling by additive manufacturing as an effective approach to plastic waste management. We explore the intricacies of the recycling process, optimize 3D printing parameters, address potential challenges, and evaluate the mechanical properties of recycled materials. Our investigation draws heavily from the literature of the last five years, as we conduct a thorough critical assessment of DRAM implementation and its influence on the properties of 3D printing structures. Through comprehensive analysis, we reveal the potential of recycled materials in delivering functional components, with insights into their performance, strengths, and weaknesses. This review serves as a comprehensive guide for those interested in embracing distributed recycling by additive manufacturing as a transformative approach to plastic recycling. By fostering community engagement, optimizing 3D printing processes, and incorporating suitable additives, it is possible to collectively contribute to a more sustainable future while combatting the plastic waste crisis. As progress is made, it becomes essential to further delve into the complexities of material behavior, recycling techniques, and the long-term durability of recycled 3D printed components. By addressing these challenges head-on, it is feasible to refine and advance distributed recycling by additive manufacturing as a viable pathway to minimize plastic waste, fostering a circular economy and cultivating a cleaner planet for generations to come.

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy

Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R² was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.

Laser-based techniques: Novel tools for the identification and characterization of aged microplastics with developed biofilm

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.

Rapid Identification of Beached Marine Plastics Pellets Using Laser-Induced Breakdown Spectroscopy: A Promising Tool for the Quantification of Coastal Pollution

The rapid identification of beached marine micro-plastics is essential for the determination of the source of pollution and for planning the most effective strategies for remediation. In this paper, we present the results obtained by applying the laser-induced breakdown spectroscopy (LIBS) technique on a large sample of different kinds of plastics that can be found in a marine environment. The use of chemometric analytical tools allowed a rapid classification of the pellets with an accuracy greater than 80%. The LIBS spectrum and statistical tests proved their worth to quickly identify polymers, and in particular, to distinguish C-O from C-C backbone pellets, and PE from PP ones. In addition, the PCA analysis revealed a correlation between appearance (surface pellets roughness) and color (yellowing), as reported by other recent studies. The preliminary results on the analysis of metals accumulated on the surface of the pellets are also reported. The implication of these results is discussed in view of the possibility of frequent monitoring of the marine plastic pollution on the seacoast.

Polyolefins and Polyethylene Terephthalate Package Wastes: Recycling and Use in Composites

Plastics are versatile materials used in a variety of sectors that have seen a rapid increase in their global production. Millions of tonnes of plastic wastes are generated each year, which puts pressure on plastic waste management methods to prevent their accumulation within the environment. Recycling is an attractive disposal method and aids the initiative of a circular plastic economy, but recycling still has challenges to overcome. This review starts with an overview of the current European recycling strategies for solid plastic waste and the challenges faced. Emphasis lies on the recycling of polyolefins (POs) and polyethylene terephthalate (PET) which are found in plastic packaging, as packaging contributes a signification proportion to solid plastic wastes. Both sections, the recycling of POs and PET, discuss the sources of wastes, chemical and mechanical recycling, effects of recycling on the material properties, strategies to improve the performance of recycled POs and PET, and finally the applications of recycled POs and PET. The review concludes with a discussion of the future potential and opportunities of recycled POs and PET.

Review of Element Analysis of Industrial Materials by In-Line Laser—Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is a rapidly developing technique for chemical materials analysis. LIBS is applied for fundamental investigations, e.g., the laser plasma matter interaction, for element, molecule, and isotope analysis, and for various technical applications, e.g., minimal destructive materials inspection, the monitoring of production processes, and remote analysis of materials in hostile environment. In this review, we focus on the element analysis of industrial materials and the in-line chemical sensing in industrial production. After a brief introduction we discuss the optical emission of chemical elements in laser-induced plasma and the capability of LIBS for multi-element detection. An overview of the various classes of industrial materials analyzed by LIBS is given. This includes so-called Technology materials that are essential for the functionality of modern high-tech devices (smartphones, computers, cars, etc.). The LIBS technique enables unique applications for rapid element analysis under harsh conditions where other techniques are not available. We present several examples of LIBS-based sensors that are applied in-line and at-line of industrial production processes.

Identification of 20 polymer types by means of laser-induced breakdown spectroscopy (LIBS) and chemometrics

Over the past few years, laser-induced breakdown spectroscopy (LIBS) has earned a lot of attention in the field of online polymer identification. Unlike the well-established near-infrared spectroscopy (NIR), LIBS analysis is not limited by the sample thickness or color and therefore seems to be a promising candidate for this task. Nevertheless, the similar elemental composition of most polymers results in high similarity of their LIBS spectra, which makes their discrimination challenging. To address this problem, we developed a novel chemometric strategy based on a systematic optimization of two factors influencing the discrimination ability: the set of experimental conditions (laser energy, gate delay, and atmosphere) employed for the LIBS analysis and the set of spectral variables used as a basis for the polymer discrimination. In the process, a novel concept of spectral descriptors was used to extract chemically relevant information from the polymer spectra, cluster purity based on the k-nearest neighbors (k-NN) was established as a suitable tool for monitoring the extent of cluster overlaps and an in-house designed random forest (RDF) experiment combined with a cluster purity-governed forward selection algorithm was employed to identify spectral variables with the greatest relevance for polymer identification. Using this approach, it was possible to discriminate among 20 virgin polymer types, which is the highest number reported in the literature so far. Additionally, using the optimized experimental conditions and data evaluation, robust discrimination performance could be achieved even with polymer samples containing carbon black or other common additives, which hints at an applicability of the developed approach to real-life samples.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.

Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers

To advance our understanding of the environmental fate and transport of macro- and micro-plastic debris, robust and reproducible methods, technologies, and analytical approaches are necessary for in situ plastic-type identification and characterization. This investigation compares four spectroscopic techniques: attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), near-infrared (NIR) reflectance spectroscopy, laser-induced breakdown spectroscopy (LIBS), and X-ray fluorescence (XRF) spectroscopy, coupled to seven classification methods, including machine learning classifiers, to determine accuracy for identifying type of both consumer plastics and marine plastic debris (MPD). With machine learning classifiers, consumer plastic types were identified with 99, 91, 97, and 70% success rates for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. The classification of MPD had similar or lower success rates, likely arising from alterations to the plastic from environmental weathering processes with success rates of 99, 81, 76, and 66% for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. Success rates indicate that ATR-FTIR, NIR reflectance spectroscopy, and LIBS coupled with machine learning classifiers can be used to identify both consumer and environmental plastic samples.

Combined LA-ICP-MS/LIBS: powerful analytical tools for the investigation of polymer alteration after treatment under corrosive conditions

Polymers are used in a variety of different areas, including applications in food packaging, automotive and the semiconductor industry. Information about degradation of these materials during application, but also uptake of pollutants from the surrounding environment is therefore of great interest. Conventional techniques used for polymer characterization such as FT-IR or Raman spectroscopy, but also thermo-analytical techniques offer insights into degradation processes but lack the possibility to detect uptake of inorganic species. Moreover, these techniques do not allow the measurement of depth profiles, thus information about degradation or pollutant uptake with sample depth is not accessible. In this work, we propose LA-ICP-MS and LIBS as powerful analytical tools for polymer characterization, overcoming the limitations of conventional analytical techniques used for polymer analysis. Applicability of the developed procedures is demonstrated by the analysis of artificially weathered polyimides and modern art materials, indicating that the degradation of the polymer but also the uptake of corrosive gases is not limited to the sample surface. Finally, a tandem LA-ICP-MS/LIBS approach is employed, which combines the advantages of both laser-based procedures, enabling the simultaneous analysis of polymer degradation and cadmium uptake of polystyrene after exposure to UV radiation and treatment with artificial sea water.

Accurate quantification of alkalinity of sintered ore by random forest model based on PCA and variable importance (PCA-VI-RF)

The alkalinity of sintered ore has an important impact on the quality, output, and energy consumption of blast furnace smelting, and there is an urgent need for a method for accurate quantifying of the alkalinity of sintered ore. The present work explores the combination of the laser-induced breakdown spectroscopy (LIBS) technique and random forest (RF) based on principal component analysis (PCA) and variable importance for the quantitative analysis of the alkalinity of sintered ore. Sixteen sintered ore samples were used in this study, and the characteristic lines of LIBS spectra for sintered ore samples can be identified based on the National Institute of Standards and Technology (NIST) database. At first, abnormal spectra are identified and rejected by PCA coupled with Mahalanobis distance (MD). Then, the input variable for the RF calibration model is optimized according to the variable importance threshold obtained by the RF model, and two RF model parameters of ${{n}_{\rm tree}}$n_tree and ${{m}_{\rm try}}$m_try are determined by out-of-bag estimate. Finally, the PCA-VI-RF model is built under the optimal model parameters. In order to verify the predictive ability of the quantitative model, the PCA-VI-RF model prediction results were compared with the RF model, partial least-squares model, and least-squares support vector machine model. The result demonstrated that PCA-VI-RF shows better analytical performance than other methods. Compared with the RF model with the original spectrum as input, the averaged relative errors of test results decreased from 5.82% to 3.94%, coefficients of determination (${R^2}$R²) of the test set increased from 0.8957 to 0.9814, and the root mean square error decreased from 0.1502% to 0.0860%. The speed of modeling and prediction has also been greatly improved, and the modeling time was reduced from 4675.56 to 16.86 s. The stability of the PCA-VI-RF model was verified by the relative standard deviation (RSD) of the test data prediction results, and the RSD reached below 4.74%. This study shows LIBS combining PCA-VI-RF is an effective method for accurate quantification of the alkalinity of sintered ore. It has great significance for the potential application of real-time online analysis of the alkalinity of sintered ore.