High-Efficiency Miniaturized Ultrasonic Nebulization Sample Introduction System for Elemental Analysis of Microvolume Biological Samples by Inductively Coupled Plasma Quadrupole Mass Spectrometry.

Sensitive and high-throughput analysis of trace elements in volume-limited biological samples is highly desirable for clinical research and health risk assessments. However, the conventional pneumatic nebulization (PN) sample introduction is usually inefficient and not well-suited for this requirement. Herein, a novel high-efficiency (nearly 100% sample introduction efficiency) and low-sample-consumption introduction device was developed and successfully coupled with inductively coupled plasma quadrupole mass spectrometry (ICP-QMS). It consists of a micro-ultrasonic nebulization (MUN) component with an adjustable nebulization rate and a no-waste spray chamber designed based on fluid simulation. The proposed MUN-ICP-QMS could achieve sensitive analysis at a low sampling rate of 10 µL min-1 with an extremely low oxide ratio of 0.25% where the sensitivity is even higher comparing to PN (100 µL min-1). The characterization results indicate that the higher sensitivity of MUN is attributed to the smaller aerosol size, higher aerosol transmission efficiency, and improved ion extraction. In addition, it offers a fast washout (20 s) and reduced sample consumption (as low as 7 µL). The absolute LODs of the studied 26 elements by MUN-ICP-QMS are improved by 1-2 orders of magnitude compared with PN-ICP-QMS. The accuracy of the proposed method was validated by the analysis of human serum, urine, and food-related certified reference materials. Furthermore, preliminary results of serum samples from patients with mental illnesses demonstrated its potential in the field of metallomics.

Development of a Portable Method for Serum Lithium Measurement Based on Low-Cost Miniaturized Ultrasonic Nebulization Coupled with Atmospheric-Pressure Air-Sustained Discharge.

An accurate, rapid but cheap, and portable method for monitoring of serum lithium (Li) is highly desirable for mental patients who take Li medicine for treatment. Conventional techniques are usually bulky, costly, and cannot provide on-site real-time measurements. Herein, a miniaturized, reliable, cost-effective, and portable optical emission method for rapid and sensitive determination of serum Li was developed based on a combination of miniaturized ultrasonic nebulization (MUN) and a low-power (≈22 W) atmospheric-pressure air-sustained discharge (APAD) excitation source. The proposed method eliminates the use of any compressed gas or pump and can achieve serum Li detection within 40 s with low sample consumption (less than 20 µL serum). Except for dilution with water, no extra treatment is needed for serum Li analysis by MUN-APAD-OES. In addition, it offers a significant advantage of good tolerance to the coexisting high concentration of Na, K, Ca, and Mg, which is in contrast with the obvious matrix effect encountered in conventional inductively coupled plasma optical emission spectrometry (ICP-OES). Different operating parameters affecting the performance of MUN-APAD-OES were evaluated. Under optimized conditions, the detection limit of Li (670.8 nm) was calculated to be 0.6 µg L-1 (6 µg L-1 in serum). Finally, the accuracy of the proposed method was validated by the analysis of two certified reference materials (Seronorm serum L-1 and L-2 RUO), six real human serum samples, and eight real animal serum samples. All of the results indicate that the low-cost and low-power MUN-APAD-OES provides a promising reliable method for on-site serum Li measurement and may also be extended to other elements.

Size and isotope analysis of individual nanoparticles by multi-collector ICP-MS using "event-triggered signal capture" with a high-speed oscilloscope.

Accurate quantitative elemental and isotope analysis of nanoparticles at the single-particle level is crucial for better understanding their origin, properties and behaviors. Single particle inductively coupled plasma-mass spectrometry (spICP-MS) has emerged as a promising technique for nanoparticle analysis. However, challenges persist in obtaining accurate and consistent element profiles and ratios for small-sized nanoparticles by conventional quadrupole (QMS) or time-of-flight mass analyzers (TOF-MS) due to their low level and transient nature. In this paper, we present a novel analytical method for single nanoparticle analysis using multiple collector ICP-MS (MC-ICP-MS) combined with a modern high-speed digital oscilloscope. The single particle events are acquired using an "event-triggered signal capture" (ETSC) technique, which enables the simultaneously capture and visualization of multiple isotopes of transient individual particle profiles with nanosecond time resolution. This greatly facilitates precise and efficient analysis of nanoparticles. The minimum detectable particle size is calculated to be as small as 8 nm (∼1 ag 109Ag) for AgNPs. Based on the 109/107Ag ratios obtained from 2000 particles, the precisions of 109/107Ag ratio measurements on 20 nm, 40 nm, 60 nm, 80 nm and 100 nm were approximately 0.086 (SD), 0.063 (SD), 0.051 (SD), 0.040 (SD), and 0.029 (SD), which is limited by counting statistics of the isotopic signals. Furthermore, the achieved standard error of 109/107Ag can be reduced to sub-permil level (0.7 ‰) even for the measurement of 20 nm AgNPs (N = 17,000). These results demonstrate that the ETSC provides a unique method for isotope analysis of single particles, holding great potential for enhancing our understanding of nanoparticles.

Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network.

In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrate that the use of CNN could efficiently overcome the complex matrix effects, and thus allows for on-site Li quantitative determination in brine samples by LIBS. Specifically, two CNN models with different input data (M-CNN with matrix emission lines, and DP-CNN with double Li lines) were constructed based on the primary matrix features on spectrum and Boltzmann equation, respectively. It was observed that DP-CNN model could greatly improve the accuracy of Li analysis. We also compared the quantitative analysis capabilities of DP-CNN model with partial least squares regression (PLSR) and principal component analysis-support vector regression (PCA-SVR) model, and the results clearly showed DP-CNN offers the best quantification results (higher accuracy and less matrix interference). Finally, five real brine samples were successfully analyzed by the proposed DP-CNN model, confirming by the average absolute error of the prediction of 0.28 mg L-1 and the average relative error of 3.48%. These results clearly demonstrate that input data plays an important role in the training of CNN model in LIBS analysis, and the proposed DP-CNN provides an effective approach to solve the matrix effects encountered in LIBS for Li measurement in brine samples.