Spectral Identification in the Attogram Regime through Laser-Induced Emission of Single Optically Trapped Nanoparticles in Air

Quantitative Analysis of Multi-Elements in a Micron-Sized Single Particle Based on Laser-Induced Breakdown Spectroscopy Signal Enhancement of an Optical Fiber Collimated System

With rapid, energy-intensive, and coal-fueled economic growth, global air quality is deteriorating, and particulate matter pollution has emerged as one of the major public health problems worldwide. It is extremely urgent to achieve carbon emission reduction and air pollution prevention and control, aiming at the common problem of weak and unstable signals of characteristic elements in the application of laser-induced breakdown spectroscopy (LIBS) technology for trace element detection. In this study, the influence of the optical fiber collimation signal enhancement method on the LIBS signal was explored. Then, the influence of the LIBS signal enhancement system based on an optical fiber collimated system on LIBS spectral signal intensity and signal-to-noise ratio (SNR) was compared, and the influences of different spectral preprocessing methods and different variable selection methods on the prediction performance of the random forest (RF) calibration model were investigated. Finally, the Savitzky-Golay convolution derivative (SG)-variable importance projection (VIP)-mutual information (MI)-RF (Zn), first-order derivative (D1st)-variable importance measurement (VIM)-successive projections algorithm (SPA)-RF (Cu), and D1st-VIM-MI-RF (Ni) optimal models were constructed according to the optimal spectral preprocessing method and the optimal hybrid variable selection method. The prediction performances of their optimal RF model after SG-VIP-MI (Zn), D1st-VIM-SPA (Cu), and D1st-VIM-MI (Ni) spectral preprocessing and hybrid variable selection method are presented as follows: Zn (Rp2 = 0.9860; MREP = 0.0590), Cu (Rp2 = 0.9817; MREP = 0.0405), and Ni (Rp2 = 0.9856; MREP = 0.0875). The above results demonstrate that the RF calibration model based on the optical fiber collimated LIBS signal enhancement method, the optimal spectral preprocessing method, and variable selection strategy overcome the key problems of low SNR and low quantitative accuracy in single particle detection. It is expected to provide a theoretical basis and technical support for in situ online rapid monitoring of particulate matter.

On-The-Flight trapping, LIBS analysis and discrimination of single meteorite particles generated by laser ablation

BACKGROUND

Thousands of micrometeorites fall to the Earth on a daily basis. Most of these meteorites have a rocky composition, but others are mainly composed of iron and nickel. Due to their small size, often ca. 100 μm in diameter, the process of searching for, collecting, and identifying these samples is remarkably tedious. In this work, we introduce a minimally invasive methodology for evaluating the full elemental composition of micrometeorites using optical emission spectroscopy of single particles produced by laser ablation of bulk targets.

RESULTS

Bulk meteorite samples were directly ablated within an ablation cell. From few micrograms of ablated matrix, we originated dry aerosols consisting of multielemental particles which were representative of the sample chemistry. SEM images confirmed that the generated particles exhibited spherical geometry. Particles were first optically trapped in air and, then, analyzed by laser-induced breakdown spectroscopy (LIBS). LIBS spectra evidenced compositional differences among samples. For example, Campo de Cielo meteorite featured a high iron content due to its metallic nature whereas LIBS results for Jbilet Winselwan and NWA 869 suggested that pyroxene components dominated the composition of the samples. In contrast, NWA 13739 and Vaca Muerta contained high aluminum intensity, thus indicating that the feldspathic component was dominant, as then verified by XRD. The intra-sample compositional variability were quite satisfactory, as revealed by the RSD data, below 45 %. For quantitative analysis, the percentages of FeO, SiO2, Al2O3, Na2O, MgO, TiO2, CaO, K2O, MnO, SrO, Cr2O3, and Li2O were calculated using CF-LIBS.

SIGNIFICANCE

This work demonstrates the applicability of laser excitation of individual particles in an optical trap for the multielemental analysis of meteorites. The methodology provides a complete overview of the samples, is capable of classificating them according to their main phases and yield preliminary quantitative information about those phases. Therefore, we present a minimally destructive pathway to be used as the first step in the inspection of these delicate samples. If the particles represent nanostructures inherent to the meteorites, it would constitute a major step in the analysis of extraterrestrial material that may provide fundamental insights into the structure and composition of the original materials at the microscale, a topic that remains an active area of research worldwide.

Ultrafast Laser Excitation Improves LIBS Performance for the Analysis of Optically Trapped Single Nanoparticles Owing to Characteristic Interaction Mechanisms

Owing to the exceedingly small mass involved, complete elemental characterization of single nanoparticles demands a highly precise control of signal background and noise sources. LIBS has demonstrated remarkable merits for this task, providing a unique tool for the multielemental analysis of particles on the attogram-picogram mass scale. Despite this outstanding sensitivity, the air plasma acting as a heat source for particle dissociation and excitation is a meddling agent, often limiting the acquisition of an accurate sample signature. Although thermal effects associated with ultrashort laser pulses are known to be reduced when compared to the widely used nanosecond pulse duration regime, attempts to improve nanoinspection performance using ultrafast excitation have remained largely unexplored. Herein, picosecond laser pulses are used as a plasma excitation source for the elemental characterization of single nanoparticles isolated within optical traps in air at atmospheric pressure. Results for picosecond excitation of copper particles lead to a mass detection limit of 27 attogram, equivalent to single particles 18 nm in diameter. Temporally and wavelength-resolved plasma imaging reveals unique traits in the mechanism of atomic excitation in the picosecond regime, leading to a deeper understanding of the interactions occurring in single nanoparticle spectroscopy.

Individual Micron-Sized Aerosol Qualitative Analysis-Combined Raman Spectroscopy and Laser-Induced Breakdown Spectroscopy by Optical Trapping in Air

The attribution of single particle sources of atmospheric aerosols is an essential problem in the study of air pollution. However, it is still difficult to qualitatively analyze the source of a single aerosol particle using noncontact in situ techniques. Hence, we proposed using optical trapping to combine gated Raman spectroscopy with laser-induced breakdown spectroscopy (LIBS) in a single levitated micron aerosol. The findings of the spectroscopic imaging indicated that the particle plasma formed by a single particle ablation with a pulsed laser within 7 ns deviates from the trapped particle location. The LIBS acquisition field of view was expanded using the 19-bundle fiber, which also reduces the fluctuation of a single particle signal. In addition, gated Raman was utilized to suppress the fluorescence and increase the Raman signal-to-noise ratio. Based on this, Raman can measure hard-to-ionize substances with LIBS, such as sulfates. The LIBS radical can overcome the restriction that Raman cannot detect ionic chemicals like fluoride and chloride in halogens. To test the capability of directly identifying distinctive feature compounds utilizing spectra, we detected anions using Raman spectroscopy and cations using LIBS. Four typical mineral aerosols are subjected to precise qualitative evaluations (marble, gypsum, baking soda, and activated carbon adsorbed potassium bicarbonate). To further validate the application potential for substances with indistinctive feature discrimination, we employed machine learning algorithms to conduct a qualitative analysis of the coal aerosol from ten different origin regions. Three data fusion methodologies (early fusion, intermediate fusion, and late fusion) for Raman and LIBS are implemented, respectively. The accuracy of the late fusion model prediction using StackingClassifier is higher than that of the LIBS data (66.7%) and Raman data (86.1%) models, with an average accuracy of 90.6%. This research has the potential to provide online single aerosol analysis as well as technical assistance for aerosol monitoring and early warning.

Pressure Effects on Simultaneous Optical and Acoustics Data from Laser-Induced Plasmas in Air: Implications to the Differentiation of Geological Materials

The shockwave generated alongside the plasma is an intimately linked, yet often neglected additional input for the characterization of solid samples by laser-induced breakdown spectroscopy (LIBS). The present work introduces a dual LIBS-acoustics sensor that takes advantage of the analysis of the acoustic spectrum yielded by shockwaves produced on different geological samples to enhance the discrimination power of LIBS in materials featuring similar optical emission spectra. Six iron-based minerals were tested at a distance of 2 m using 1064 nm laser light and under pressure values ranging from 7 to 1015 mbar. These experimental parameters were selected to assess the effects of pressure, one of the main factors conditioning the propagation of sound as well as a commonly investigated influence in LIBS experiments. Moreover, precise values for carrying out the analyses were set based on one of the most exciting scenarios in which LIBS data may be enhanced by laser-induced acoustics: space exploration. This is exemplified by the tasks performed by the Mars 2020 SuperCam instrument located onboard the Perseverance rover. Authors evaluated the use of acoustic signals both in the time-domain and frequency-domain in sensitive cases for the distinguishing of minerals exhibiting LIBS spectra featuring almost the same emission lines using PCA schemes for each pressure setting. Thus, we report herein the impact of the surrounding pressure level upon this diagnostic tool. Overall, this paper seeks to show how the analytical potential of simultaneous phenomena taking place during a laser-produced plasma event is subjected to the defined operational conditions.

Metronidazole-loaded gold nanoparticles in natural rubber latex as a potential wound dressing

Gold nanoparticles (AuNPs) have shown interesting properties and specific biofunctions, providing benefits and new opportunities for controlled release systems. In this research, we demonstrated the use of natural rubber latex (NRL) from Hevea brasiliensis as a carrier of AuNPs and the antibiotic metronidazole (MET). We prepared AuNP-MET-NRL and characterized by physicochemical, biological and in vitro release assays. The effect of AuNPs on MET release was evaluated using UV-Vis and Laser-Induced Breakdown Spectroscopy (LIBS) techniques. AuNPs synthesized by Turkevich and Frens method resulted in a spherical shape with diameters of 34.8 ± 5.5 nm. We verified that there was no emergence or disappearance of new vibrational bands. Qualitatively and quantitatively, we showed that the MET crystals dispersed throughout the NRL. The Young's modulus and elongation values at dressing rupture were in the range appropriate for human skin application. 64.70% of the AuNP-MET complex was released within 100 h, exhibiting a second-order exponential release profile. The LIBS technique allowed monitoring of the AuNP release, indicating the Au emission peak reduction at 267.57 nm over time. Moreover, the dressing displayed an excellent hemocompatibility and fibroblast cell viability. These results demonstrated that the AuNP-MET-NRL wound dressing is a promising approach for dermal applications.

LIBS-Acoustic Mid-Level Fusion Scheme for Mineral Differentiation under Terrestrial and Martian Atmospheric Conditions

The shockwave produced alongside the plasma during a laser-induced breakdown spectroscopy event can be recorded as an acoustic pressure wave to obtain information related to the physical traits of the inspected sample. In the present work, a mid-level fusion approach is developed using simultaneously recorded laser-induced breakdown spectroscopy (LIBS) and acoustic data to enhance the discrimination capabilities of different iron-based and calcium-based mineral phases, which exhibit nearly identical spectral features. To do so, the mid-level data fusion approach is applied concatenating the principal components analysis (PCA)-LIBS score values with the acoustic wave peak-to-peak amplitude and with the intraposition signal change, represented as the slope of the acoustic signal amplitude with respect to the laser shot. The discrimination hit rate of the mineral phases is obtained using linear discriminant analysis. Owing to the increasing interest for in situ applications of LIBS + acoustics information, samples are inspected in a remote experimental configuration and under two different atmospheric traits, Earth and Mars-like conditions, to validate the approach. Particularities conditioning the response of both strategies under each atmosphere are discussed to provide insight to better exploit the complex phenomena resulting in the collected signals. Results reported herein demonstrate for the first time that the characteristic sample input in the laser-produced acoustic wave can be used for the creation of a statistical descriptor to synergistically improve the capabilities of LIBS of differentiation of rocks.

Size-dependent synergetic seeding effects in the inspection of airborne dry nanoaerosols by LIBS

In the present paper, confined dry Cu nanoaerosols of controlled particle size are inspected under a time-resolved LIBS scheme to explore the effect of laser-particulate matter interaction upon the detection capability of airborne nanoparticulate material. Optically catapulted streams probed showed linear intensity vs mass correlation and similar signal stability which is linked to the seeding effect caused by smaller particles yielding hotter, albeit shorter plasmas. Seeding effect is demonstrated by hyperspectral time-resolved aerosol inspection, which exposes both, the interaction between multiple plasma nuclei and the discrete nature of the laser-particle interaction. Observed population/exhaustion cycles at the focal volume of the inspection laser explained the uncertainty values characteristic of LIBS inspection of aerosols. A thorough inspection of the emission in time evidenced a significantly different evolution of the intensity profile for commonly monitored Cu lines owed not only to the nature of the monitored transit and pulse energy, but also to particle size. These results suggest that the experimental settings for quantitative ultrafine aerosol inspection need to be tuned according to the target particle size and the particle density of the aerosol as seeding effects facilitates signal saturation, therefore this effect simultaneously contributes to and detracts from the analytical performance of LIBS on nanometric aerosols.

Optical-Trapping Laser Techniques for Characterizing Airborne Aerosol Particles and Its Application in Chemical Aerosol Study

We present a broad assessment on the studies of optically-trapped single airborne aerosol particles, particularly chemical aerosol particles, using laser technologies. To date, extensive works have been conducted on ensembles of aerosols as well as on their analogous bulk samples, and a decent general description of airborne particles has been drawn and accepted. However, substantial discrepancies between observed and expected aerosols behavior have been reported. To fill this gap, single-particle investigation has proved to be a unique intersection leading to a clear representation of microproperties and size-dependent comportment affecting the overall aerosol behavior, under various environmental conditions. In order to achieve this objective, optical-trapping technologies allow holding and manipulating a single aerosol particle, while offering significant advantages such as contactless handling, free from sample collection and preparation, prevention of contamination, versatility to any type of aerosol, and flexibility to accommodation of various analytical systems. We review spectroscopic methods that are based on the light-particle interaction, including elastic light scattering, light absorption (cavity ring-down and photoacoustic spectroscopies), inelastic light scattering and emission (Raman, laser-induced breakdown, and laser-induced fluorescence spectroscopies), and digital holography. Laser technologies offer several benefits such as high speed, high selectivity, high accuracy, and the ability to perform in real-time, in situ. This review, in particular, discusses each method, highlights the advantages and limitations, early breakthroughs, and recent progresses that have contributed to a better understanding of single particles and particle ensembles in general.

Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air

Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ manner is still a challenging task. In this work, we explore the laser trapping-LIBS-machine learning to analyze four elements (Zn, Ni, Cu, and Cr) absorbed in a single micro-carbon black particle in air. By employing a hollow laser beam for trapping, the particle can be restricted in a range as small as ∼1.72 μm, which is much smaller than the focal diameter of the flat-topped LIBS exciting laser (∼20 μm). Therefore, the particle can be entirely and homogeneously radiated, and the LIBS spectrum with a high signal-to-noise ratio (SNR) is correspondingly achieved. Then, two types of calibration models, i.e., the univariate method (calibration curve) and the multivariate calibration method (random forests (RF) regression), are employed for data processing. The results indicate that the RF calibration model shows a better prediction performance. The mean relative error (MRE), relative standard deviation (RSD), and root-mean-squared error (RMSE) are reduced from 0.1854, 363.7, and 434.7 to 0.0866, 179.8, and 216.2 ppm, respectively. Finally, simultaneous and quantitative determination of the four metal contents with high accuracy is realized based on the RF model. The method proposed in this work has the potential for online single aerosol particle analysis and further provides a theoretical basis and technical support for the precise prevention and control of composite air pollution.

Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores' selection in the metallurgical industry.

Subfemtogram Simultaneous Elemental Detection in Multicomponent Nanomatrices Using Laser-Induced Plasma Emission Spectroscopy within Atmospheric Pressure Optical Traps

Simultaneous detection of multiple constituents in the characterization of state-of-the-art nanomaterials is an elusive topic to a majority of the analytical techniques covering the field of nanotechnology. Optical catapulting (OC) and optical trapping (OT) have recently been combined with laser-induced breakdown spectroscopy (LIBS) to provide single-nanoparticle resolution and attogram detection power. In the present work, the multielemental capabilities of this approach are demonstrated by subjecting two different types of nanometric ferrite particles to LIBS analysis. Up to three metallic elements in attogram quantities are consistently detected within single laser events. Individual excitation efficiency for each species is quantified from particle spectra showing an exponential correlation between photon production and the energy of the upper level of the monitored atomic line. Moreover, a new sampling strategy based in skimmer-like 3D printed cones that allows for thin dry nanoparticle aerosols to be formed via optical catapulting is introduced. Enhanced sampling resulted in an increase of the sampling throughput by facilitating stable atmospheric-pressure optical trapping of individual particles and spectroscopic chemical characterization within a short timeframe.