Novel Signal Enhancement Strategy for Laser-Induced Breakdown Spectroscopy via Miniaturized Atmospheric Pressure Glow Discharge

A Portable Microplasma Optical Emission Spectrometric Device with Online Digestion Function for Field and Sensitive Determination of Lead in Biological Samples

Accurate detection and screening of Pb in biological samples is helpful to assess the risk associated with lead pollution to human health. However, conventional atomic spectroscopic instruments are bulky and cumbersome, requiring additional sample pretreatment equipment, and difficult to perform field analysis with. Herein, a portable point discharge (PD) microplasma-optical emission spectrometric (OES) device with online digestion function is designed for field and sensitive determination of lead in biological samples. With rice as a model, online digestion of a batch of six 50 mg samples can be achieved in the HNO3 and H2O2 system within 25 min by a temperature control and timing module. Compared to the conventional microwave digestion, the digestion efficiency of this device reaches 97%. Pb in digestion solution is converted into volatile species by hydride generation (HG) and directly introduced into PD-OES for excitation and detection by a self-designed rotatable and telescopic cutoff gas sampling column. Six samples can be successively detected in 2 min, and argon consumption of the whole process is only <800 mL. Under the optimized conditions, the detection limit of Pb is 0.018 mg kg-1 (0.9 μg L-1) and precision is 3.6%. The accuracy and practicability of the present device are verified by measuring several certified reference materials and real biological samples. By virtue of small size (23.5 × 17 × 8.5 cm3), lightweight (2.5 kg), and low energy consumption (24.3 W), the present device provides a convenient tool for field analysis of toxic elements in biological samples.

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning

Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R²_P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.