Raman Spectroscopy of Single Light-Absorbing Carbonaceous Particles Levitated in Air Using an Annular Laser Beam

Multi-element Quantitative Analysis of Single Micro-sized Suspended Particles in Air with High Accuracy Based on Random Forest and Variable Selection Strategies

The chemical compositions of atmospheric particles have been studied for several decades, and the traditional techniques for particle analysis usually require time-consuming sample preparation. Within this study, simultaneous quantitative detection of multiple metallic species (Zn, Cu, and Ni) in single micro-sized suspended particles was investigated by combining random forest (RF) and variable selection strategies. Laser-induced breakdown spectra of 15 polluted black carbon samples were applied for establishing the RF model, and the movmean smoothing spectral pretreatment method and variable selection methods [variable importance measurement (VIM), genetic algorithm (GA), and variable importance projection (VIP)] were proposed. Finally, the optimized RF calibration model with the evaluation indicators of mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination (R²) was constructed based on the optimal input variables and model parameters. Compared with the univariate regression method, the VIP-RF (Zn) and VIM-RF (Cu and Ni) models showed a better correlation relationship (R_p² = 0.9662 for Zn, R_p² = 0.9596 for Cu, and R_p² = 0.9548 for Ni). For Zn, Cu, and Ni, the values of RMSEP (RMSE of prediction) decreased by 116.44, 68.94, and 102.10 ppm, while the values of MREP (MRE of prediction) decreased by 67, 55, and 48%, respectively. The values of ratio of prediction to deviation (RPD) of VIP-RF (Zn), VIM-RF (Cu), and VIM-RF (Ni) models were 5.4, 5.0, and 4.7, respectively. The performance of this combined approach displays a notable accuracy improvement in the quantitative analysis of single particles, suggesting that it is a promising tool for real-time air particulate matter pollution monitoring and control in the future.

Constructing a Raman and surface-enhanced Raman scattering spectral reference library for fine-particle analysis

Fine particles associated with haze pollution threaten the health of over 400 million people in China. Owing to excellent non-destructive fingerprint recognition characteristics, Raman and surface-enhanced Raman scattering (SERS) are often used to analyze the composition of fine particles to determine their physical and chemical properties as well as reaction mechanisms. However, there is no comprehensive Raman spectral library of fine particles. Furthermore, various studies that used SERS for fine-particle composition analysis showed that the uniqueness of the SERS substrates and different excitation wavelengths can produce a different spectrum for the same fine-particle component. To overcome this limitation, we conducted SERS experiments with a portable Raman spectrometer using two common SERS substrates (silver (Ag) foil and gold nanoparticles (Au NPs)) and a 785 nm laser. Herein, we introduced three main particle component types (sulfate-nitrate-ammonium (SNA), organic material, and soot) with a total of 39 chemical substances. We scanned the solid Raman, liquid Raman, and SERS spectra of these substances and constructed a fine-particle reference library containing 105 spectra. Spectral results indicated that for soot and SNA, the differences in characteristic peaks mainly originated from the solid-liquid phase transition; Ag foil had little effect on this difference, while the Au NPs caused a significant red shift in the peak positions of polycyclic aromatic hydrocarbons. Moreover, with various characteristic peak positions in the three types of spectra, we could quickly and correctly distinguish substances. We hope that this spectral library will aid in the future identification of fine particles.

Optical Trapping-Polarized Raman Microspectroscopy of Single Ethanol Aerosol Microdroplets: Droplet Size Effects on Rotational Relaxation Time and Viscosity

Optical trapping-polarized Raman microspectroscopy of single ethanol (EtOH) microdroplets with a diameter (d) of 6.1-16.5 μm levitated in an EtOH vapor-saturated air/N₂ gas atmosphere has been explored to elucidate the vibrational and rotational motions of EtOH in the droplets at 22.0 °C. The Raman spectral bandwidth of the C-C stretching vibrational mode observed for an aerosol EtOH microdroplet was narrower than that of bulk EtOH, suggesting that the vibrational/rotational motions of EtOH in the aerosol system were restricted compared to those in the bulk system. In practice, polarized Raman microspectroscopy demonstrated that the rotational relaxation time (τ_rot) of EtOH in an aerosol microdroplet with d = 16. 5 μm was slower (2.33 ps) than that in a bulk EtOH (1.65 ps), while the vibrational relaxation times (τ_vib) in the aerosol and bulk EtOH systems were almost comparable with one another: 0.86-0.98 ps. Furthermore, although the τ_vib value of an aerosol EtOH microdroplet was almost unchanged irrespective of d as described above, the τ_rot value increased from 2.33 to 3.57 ps with a decrease in d from 16.5 to 6.1 μm, which corresponded to the increase in EtOH viscosity (η) from 1.33 to 2.04 cP with the decrease in d. The droplet size dependences of τ_rot and η in an aerosol EtOH microdroplet were discussed in terms of the gas/droplet interfacial molecular arrangements of EtOH and Laplace pressure experienced by a spherical EtOH microdroplet in the gas phase.

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.

Aerosol Droplet Surface Measurement Methods

Aerosol droplets play a critical role in the development of weather patterns, yet are notoriously difficult to analyze because of their small size, transient nature and potentially complex composition. As a result, there has been a surge in recent years in the development of analysis techniques aimed at the study of aerosol droplets, namely of their surface tension properties, which are thought to play a great role in aerosol/cloud growth and subsequently having an impact on the resulting weather patterns. To capture the state of the field at this key time, we have collected and described some of the most relevant and influential studies, with a focus on those that have had the most impact. This review will present and describe the most used analytical techniques for studying the surface tension of micrometer-sized aqueous droplets, with a focus on historical trends and how the current techniques are posed to revolutionize the field.

Particle Manipulation with External Field; From Recent Advancement to Perspectives

Physical forces, such as dielectric, magnetic, electric, optical, and acoustic force, provide useful principles for the manipulation of particles, which are impossible or difficult with other approaches. Microparticles, including polymer particles, liquid droplets, and biological cells, can be trapped at a particular position and are also transported to arbitrary locations in an appropriate external physical field. Since the force can be externally controlled by the field strength, we can evaluate physicochemical properties of particles from the shift of the particle location. Most of the manipulation studies are conducted for particles of sub-micrometer or larger dimensions, because the force exerted on nanomaterials or molecules is so weak that their direct manipulation is generally difficult. However, the behavior, interactions, and reactions of such small substances can be indirectly evaluated by observing microparticles, on which the targets are tethered, in a physical field. We review the recent advancements in the manipulation of particles using a physical force and discuss its potentials, advantages, and limitations from fundamental and practical perspectives.

The application of Raman spectroscopy combined with multivariable analysis on source apportionment of atmospheric black carbon aerosols

Source apportionment studies become increasingly crucial for black carbon (BC) in atmospheric particulate matters given its linkage with adverse public health and climate impacts. In this work, a facile and rapid method using Raman spectra combined with stepwise discriminant analysis (SDA) was proposed to identify and quantify the contributions of atmospheric BC sources. Four BC samples from biomass burning, coal combustion, gasoline and diesel vehicle emission were characterized by Raman spectra. The SDA model was established based on 10 parameters with significant differences (p < 0.05), giving an accuracy of 83% with a cross-validation rate of 80%. Utilizing four suggested discriminant variables from SDA model, vehicle emission was predicted as the dominant contributor to ambient BC particles, among which gasoline contributed much higher than diesel at an urban road intersection in Shanghai, China. This new method shows great potential to classify and investigate the sources of atmospheric BC aerosols and provide more effective information on air pollution control measures.

Online Characterization of Single Airborne Carbon Nanotube Particles Using Optical Trapping Raman Spectroscopy

Carbon nanotubes (CNTs) have become recognized as a potential environmental and health hazard as their applications are broadening and manufacturing costs are reducing. Fundamental information of CNTs in air is of significant importance to our understanding of their environmental fate as well as to further applications. Extensive efforts have been made over decades on characterizing CNTs; however, a majority of the studies are of bulk or CNTs dispersed on substrates. In the present study, we characterize single CNT particles in air using optical trapping Raman spectroscopy (OT-RS). Different types of CNT particles, as well as glassy carbon spheres, were optically trapped in air. Their physical properties were viewed by microscopic bright field images and scattering images; their chemical properties and structural information can be inferred from characteristic Raman bands. The system can also spatially resolve the morphology and chemical distribution of optically trapped CNT particles in air. The OT-RS technique combines single-particle morphological and chemical information and offers an online method to characterize the physicochemical properties of single CNT particles at their native states in air.