Exciton-exciton annihilation in thin indium selenide layers.

The photocarrier recombination in van der Waals layers may determine the device performance based on these materials. Here, we investigated the photocarrier dynamics in a multilayer indium selenide nanofilm using transient absorption spectroscopy. The sub-bandgap transient absorption feature was attributed to the indirect intraband absorption of the photocarriers, which was then exploited as a probe to monitor the photocarrier dynamics. With increasing pump intensities, the photocarrier decay was accelerated because of the rising contribution from a bimolecular recombination channel that was then assigned to exciton-exciton annihilation. The rate constant of the exciton-exciton annihilation was given as (1.8 ± 0.1) × 10-15 cm2 ps-1 from a global fitting of the photocarrier decay kinetics for different pump intensities. Our finding suggests that, in contrast with their monolayer counterpart, the exciton-exciton annihilation is rather inefficient in multilayers due to their weaker Coulomb interaction. Hence, compared with monolayers, the lifetime of photocarriers in multilayers would not be significantly reduced under high-intensity pump conditions, and the apparent photocarrier lifetime could be further improved just by suppressing the monomolecular recombination channels such as trapping.

Investigation of the mechanism and influence of laser wavelength and energy on laser opto-ultrasonic dual detection.

Laser opto-ultrasonic dual (LOUD) detection, which uses laser irradiation of samples to generate spectral and ultrasonic signals simultaneously, can perform multimodal detection of element composition and structural property. As such, it has been applied to the detection of additive manufacturing (AM) components. Further, optimized parameters lead to better detection results. To the best of our knowledge, however, there is no study on the effect of laser properties on LOUD detection. Therefore, we studied the mechanism and influence of laser wavelength and energy on LOUD detection. In this work, the intensity, signal-to-noise ratio (SNR), and stability evolution of the laser excitation spectrum and ultrasonic signals at different wavelengths and energies were analyzed. It was found in the plasma evolution that high electron number density means a large amount of ablated mass generated, which was favorable for laser ultrasonic excitation and can produce higher SNR and a more stable signal. However, it also led to more atoms of the ground-state, which resulted in the self-absorption effect and reduced spectrum intensity in the spectrum analysis. Therefore, with self-absorption correction, better stability, and higher signal intensity, an SNR of spectral and ultrasonic signals can be obtained using 355 nm laser excitation at optimal energy. As a result, in the quantitative analysis of Cu and Si elements by LOUD detection, the determination coefficients (R2) were higher than 0.995, and the average relative errors were less than 2.5%, the limit of detection could reach the order of 100 ppm. Further, the defect size of 0.55 mm in the wire +arc additive manufacturing sample was detected by LOUD detection, and the average relative error was 5.59% compared with the digital radiography results, which indicate that laser wavelength and laser energy affect the intensity and stability of spectral and ultrasonic signals in LOUD detection, which means selecting appropriate laser parameters is important to obtain a high precision detection.

Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy.

There are two main challenges in the diagnosis of blood cancer. The first is to diagnose cancer from healthy control, and the second is to identify the types of blood cancer. The chemometrics method combined with laser-induced breakdown spectroscopy (LIBS) can be used for cancer detection. However, chemometrics methods were easily influenced by the spectral feature redundancy and noise, resulting in low accuracy rate because of their simple structure. We proposed an approach using LIBS combined with the ensemble learning based on the random subspace method (RSM). The serum samples were dripped onto a boric acid substrate for LIBS spectrum collection. The complete blood cancer sample set include leukemia [acute myeloid leukemia (AML) and chronic myelogenous leukemia (CML)], multiple myeloma (MM), and lymphoma. The results showed that the accuracy rates using k nearest neighbors (kNN) and linear discriminant analysis (LDA) only were 88.14% and 94.45%, respectively, while using RSM with LDA (RSM-LDA), the average accuracy rate was improved from 94.45% to 98.34%. Furthermore, the variable importance of spectral lines (Na, K, Mg, Ca, H, O, N, C-N) were evaluated by the RSM-LDA model, which can improve the recognition ability of blood cancer types. Comparing the RSM-LDA model and only with LDA, the results showed that the average accuracy rate for cancer type identification was improved from 80.4% to 91.0%. These results demonstrate that LIBS combined with the RSM-LDA model can discriminate the blood cancer from the health control, as well as the recognition the types for blood cancers.

Half-life determination of inorganic-organic hybrid nanomaterials in mice using laser-induced breakdown spectroscopy.

Inorganic or inorganic-organic hybrid nanomaterials have great potential for applications in the biomedical fields. Biological half-life is an essential pharmacokinetic parameter for these materials to function in vivo. Compared to inductively coupled plasma mass spectrometry (ICP-MS), which is the gold standard, laser-induced breakdown spectroscopy (LIBS) is a faster and more efficient elemental detection method. We investigated an efficient way to quantify the metabolic rate using LIBS. Nanoparticle platforms, such as manganese dioxide-bovine serum albumin (MnO2-BSA) or boehmite-bovine serum albumin (AlO(OH)-BSA) were injected into mice through intravenous administration for LIBS spectrum acquisition. First, the spectral background was corrected using the polynomial fitting method; The spectral interference was eliminated by Lorentz fitting for each LIBS spectrum simultaneously. The support vector regression (SVR) was then used for LIBS quantitative analyses. Finally, the LIBS results were compared with the ICP-MS ones. The half-lives of MnO2-BSA calculated by LIBS and ICP-MS were 2.49 and 2.42 h, respectively. For AlO(OH)-BSA, the half-lives detected by LIBS and ICP-MS were 3.46 and 3.57 h, respectively. The relative error of LIBS is within 5% compared to ICP-MS. The results demonstrate that LIBS is a valuable tool for quantifying the metabolic rates with a high degree of accuracy.

Simultaneous Compositional and Grain Size Measurements Using Laser Opto-Ultrasonic Dual Detection for Additive Manufacturing.

Metal-based additive manufacturing (AM) is a disruptive technique with great potential across multiple industries; however, its manufacturing quality is unstable, leading to an urgent requirement for component properties detection. The distribution of grain size has an important effect on many mechanical properties in AM, while the distribution of added elements, such as titanium (Ti), has a measurable effect on the grain size of an aluminum (Al) alloy. Therefore, the detection of the distributions of grain size and elements is of great significance for AM. In this study, we investigated the distribution of grain size and elements simultaneously for wire + arc additive manufacturing (WAAM) with an Al alloy using laser opto-ultrasonic dual (LOUD) detection. The average grain size obtained from the acoustic attenuation of ultrasonic signals was consistent with the results of electron backscatter diffraction (EBSD), with a coefficient of determination (R2) of 0.981 for linear fitting. The Ti element distribution obtained from optical spectra showed that the enrichment of Ti corresponded to the grain refinement area in the detected area. The X-ray diffraction (XRD) spectra showed that the spectral peaks were moved from Al to AlTi and Al2Ti forms in the Ti-rich areas, which confirmed the LOUD results. The results indicated that LOUD detection holds promise for becoming an effective method of analyzing the mechanical and chemical properties of components simultaneously, which could help explain the complex physical and chemical changes in AM and ultimately improve the manufacturing quality.

Chlorine and sulfur determination in water using indirect laser-induced breakdown spectroscopy.

The detection sensitivity of chlorine (Cl) and sulfur (S) elements is poor using direct laser-induced breakdown spectroscopy (LIBS) because of the high ionization energy of Cl and S. Therefore, a new technique, namely indirect laser-induced breakdown spectroscopy (ID-LIBS), was proposed to improve the detection sensitivity of Cl and S elements. The method detected Cl in water by indirectly detecting the excess silver (Ag) after the precipitation reaction of Ag and chloride. Similarly, the method indirectly detected S in water by detecting the excess barium (Ba) after the precipitation reaction of Ba and sulfate, due to Ag and Ba with low ionization energy and easy excitation. The lines of Ag I 546.5 nm and Ba I 553.5 nm were detected. The R2 values of 0.999 and 0.997 were obtained for Cl and S, respectively. The limit of quantitation (LoQ) was 2 mg/L for Cl and 5 mg/L for S. The detection sensitivity was improved by about three orders of magnitude compared to using Cl I 822.17 nm and S I 921.28 nm. The results showed that the technique of indirect LIBS can achieve the sensitive detection of Cl and S in water indicating that the technique has tremendous potential for element analysis of water.

A plasma-image-assisted method for matrix effect correction in laser-induced breakdown spectroscopy.

The matrix effect is one of the main bottlenecks for the laser-induced breakdown spectroscopy (LIBS) technique. In this work, image-assisted, laser-induced breakdown spectroscopy (IA-LIBS) based on the Lomakin-Scherbe formula was put forward as a correction to the matrix effect. The brightness and area information in the plasma image was extracted to correct the spectral line intensities among which the brightness information characterizes the plasma temperature, and the area information characterizes the ablative mass. To verify the feasibility of this method, the experiment was conducted on metal samples and pressed samples. The method was applied for quantitative analysis of copper (Cu), magnesium (Mg) in metal samples and chromium (Cr), manganese (Mn) in pressed samples. For the metal samples, after correcting the matrix effect by IA-LIBS, the determination coefficient R squared (R2) of Cu I 510.55 nm and Mg I 518.36 nm calibration curves were increased from 0.726 to 0.942 to 0.992 and 0.988, respectively. The root-mean-square-error of cross-validation (RMSECV) and the average relative error (ARE) decreased by 75.10% and 77.18%, respectively. For the pressed samples, R2 of Cr I 520.84 nm and Mn I 403.07 nm calibration curves corrected by IA-LIBS increased from 0.364 to 0.098 to 0.975 and 0.980; and RMSECV and ARE decreased by 77.88% and 83.83%, respectively. The experimental results showed that IA-LIBS had an obvious improvement on elimination of the matrix effect for the different samples and the different elements. Therefore, IA-LIBS will become a promising technology and will greatly promote the development of LIBS in various fields.

Multivariate quantitative analysis of metal elements in steel using laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS), an element-detection technology with the advantages of no sample preparation and in situ detection of metal samples, is suitable for the quantitative analysis of metal samples. However, severe spectral interference in the detection of metal samples makes the quantitative analysis difficult. Three quantitative analysis methods, including single-variable calibration, partial least squares regression (PLSR), and support vector regression (SVR), are used to conduct the quantitative analysis of four common metal elements (Manganese (Mn), Chromium (Cr), Vanadium (V), and Titanium (Ti)). The PLSR model adds interference spectrum lines to the model for linear modeling, while the SVR model adds interference spectrum lines to the model for nonlinear modeling. The quantitative analysis results of the nonlinear SVR model are the best. The R square (R2) values of Mn, Cr, V, and Ti are 0.993, 0.995, 0.990, and 0.992, respectively. The root-mean-squared errors of the prediction set of Mn, Cr, V, and Ti are 0.044, 0.045, 0.011, and 0.014, respectively. Therefore, the results of PLSR and SVR are better than the calibration curves of the spectral intensity and concentration due to the influence of multivariate factors. SVR has almost no element bias, while PLSR and the single-variable calibration model have different quantitative results due to the different degrees of influence on spectral lines. These results demonstrate that the combined influence of the spectral interference, background noise, and self-absorption can be suppressed by the nonlinear quantitative analysis model in the steel field using LIBS.