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A simple and ultrasensitive method for detecting Pb2+ using resin-enrichment combined with laser-induced breakdown spectroscopy assisted by laser-induced fluorescence.

Sensing of hazardous metals is urgent in many areas (e.g., water pollution and meat products) as heavy metals threaten people's health. Laser-induced breakdown spectroscopy (LIBS), as a rapid, in situ, and multielemental analytical technique, has been widely utilized in rapid hazardous heavy metal sensing. However, loose and water-containing samples (e.g., meat, plant, and soil) are hard to analyze by LIBS directly, and heavy metal depth profiling for bulk samples remains suspenseful. Here, inspired by the Needle, the sword of Arya Stark in Game of Thrones, we propose an insertable, scabbarded, and nanoetched silver (NE-Ag) needle sensor for rapid hazardous element sensing and depth profiling. The NE-Ag needle sensor features a micro-nanostructure surface for inserting into the bulk sample and absorbing hazardous analytes. For accurate elemental depth profiling, we design a stainless-steel scabbard to wrap and protect the NE-Ag needle from pollution (unexpected contaminant absorption) during the needle insertion and extraction process. The results for cadmium (Cd) show that the relative standard deviation equals to 6.7% and the limit of detection reaches 0.8 mg/L (ppm). Furthermore, the correlations (Pearson correlation coefficient) for Cd and chromium (Cr) depth profiling results are no less than 0.96. Furthermore, the total testing time could be less than 1 h. All in all, the insertable and scabbarded NE-Ag needle senor has high potential in rapid hazardous heavy metal depth profiling in different industries.

We proposed a modified corner-cutting method for continuum background removal in laser-induced breakdown spectroscopy.

We proposed an improved algorithm based on the optimization of a combined atomic and ionic line algorithm for LIBS analysis.

Using an aspheric lens for laser focusing solves the energy dispersion caused by spherical aberration, reducing the plasma fluctuations.

Nitrogen in solution could be detected sensitively by using molecular laser-induced breakdown spectroscopy (LIBS).

A convenient LIBS approach was proposed to reduce the self-absorption effect of alkali and alkaline earth metal elements in soil.

To improve the qualitative accuracy of foreign protein adulteration in milk powder, a novel method named multidimensional spectral information laser-induced breakdown spectroscopy (MSI-LIBS) was proposed, which fully mined the effective information in the spectra by integrating the absolute intensity, the first derivative spectra, and the ratio spectra. Compared with traditional LIBS, the performance of the models based on MSI-LIBS was significantly improved. The accuracy of the cross-validation set of support vector machine, k-nearest neighbor, and random subspace method-linear discriminant analysis models increased from 80.98%, 75.61%, and 79.25% to 85.17%, 79.32%, and 81.18%, respectively. The accuracy of the prediction set increased from 81.50%, 76.03%, and 79.07% to 85.82%, 79.74%, and 81.28%, respectively. Furthermore, the visualization results of t-distributed stochastic neighbor embedding also showed that there was a more obvious boundary between the spectra of different samples based on MSI-LIBS. Therefore, these results fully prove the effectiveness of MSI-LIBS in improving the performance of LIBS classification.

Rose-petal-like superhydrophobic surfaces with strong water adhesion are promising for microdroplet manipulation and lossless droplet transfer. Assembly of self-grown micropillars on shape-memory polymer sheets with their surface adhesion finely tunable was enabled using a picosecond laser microprocessing system in a simple, fast, and large-scale manner. The processing speed of the wettability-finely-tunable superhydrophobic surfaces is up to 0.5 cm

Spectral fluctuation is one of the main obstacles affecting the further development of LIBS, and it is also the current research hotspot and difficulty. To meet the requirements of industrial monitoring, a novel method named plasma image-spectrum fusion laser induced breakdown spectroscopy (PISF-LIBS) was proposed to correct the spectral fluctuation and improve the quantitative accuracy. In this method, by systematically analyzing the spectral radiation model, six main factors affecting the spectral stability were obtained. Further, the standard spectrum in the ideal plasma state which is not affected by these six factors was calculated, and the deviation from the actual spectrum was obtained. According to the above analysis, the calculated deviation was mainly affected by these six factors and can be estimated through them. Therefore, this study creatively proposed to use the effective information in the plasma images and spectra to indirectly characterize the deviation, so as to realize the correction of spectral fluctuation. To verify the wide applicability of PISF-LIBS in experimental conditions, the LIBS spectra of aluminum alloy obtained under four different experimental conditions were used. After PISF-LIBS correction, the R

Lithium salts are commonly used as medication for Bipolar Disorder (BD) and depression. However, there are limited methods to quantify intracellular lithium. Most methods to analyze intracellular electrolytes require tedious sample processing, specialized and often expensive machinery, sometimes involving harmful chemicals, and a bulk amount of the sample. In this work, we report a novel method (FROZEN!) based on cell isolation (from the surrounding medium) through rapid de-ionized water cleaning, followed by flash freezing for preservation. SKOV3 cells were cultured in normal medium and a medium containing 1.0 mM lithium. Lithium and other intracellular electrolytes in the isolated and preserved cells were simultaneously analyzed with laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF). Key electrolytes such as sodium, potassium, and magnesium, along with lithium, were detectable at the single-cell level. We found that cells cultured in the lithium medium have an intracellular lithium concentration of 0.5 mM. Concurrently, the intracellular concentrations of other positively charged electrolytes (sodium, potassium, and magnesium) were reduced by the presence of lithium. FROZEN! will greatly facilitate research in intracellular electrolyte balance during drug treatment, or other physiological stresses. In particular, the cell isolation and preservation steps can be easily performed by many laboratories worldwide, after which the samples are sent to an analytical laboratory for electrolyte analysis.

Diagnosis of the Graves’ ophthalmology remains a significant challenge. We identified between Graves’ ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves’ ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves’ ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves’ ophthalmology.

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.

The identification of steels is a crucial step in the process of recycling and reusing steel waste. Laser-induced breakdown spectroscopy (LIBS) coupled with machine learning is a convenient method to classify the types of materials. LIBS can generate characteristic spectra of various samples as input variable for steel classification in real time. However, the performance of classification model is limited to the complex input due to similar chemical composition in samples and nonlinearity problems between spectral intensities and elemental concentrations. In this study, we developed a method of LIBS coupled with deep belief network (DBN), which is suitable to deal with a nonlinear problem, to classify 13 brands of special steels. The performance of the training and validation sets were used as the standard to optimize the structure of DBN. For different input, such as the intensities of full-spectra signals and characteristic spectra lines, the accuracies of the optimized DBN model in the training, validation, and test set are all over 98%. Moreover, compared with the self-organizing maps, linear discriminant analysis (LDA), k-nearest neighbor (KNN) and back-propagation artificial neural networks (BPANN), the result of the test set showed that the optimized DBN model performed second best (98.46%) in all methods using characteristic spectra lines as input. The test accuracy of the DBN model could reach 100% and the maximum accuracy of other methods ranged from 62.31% to 96.16% using full-spectra signals as input. This study demonstrates that DBN can extract representative feature information from high-dimensional input, and that LIBS coupled with DBN has great potential for steel classification.

The single sample calibration laser-induced breakdown spectroscopy (SSC-LIBS) is quite suitable for the fields where the standard sample is hard to obtain, including space exploration, geology, archaeology, and jewelry identification. But in practice, the self-absorption effect of plasma destroys the linear relationship of spectral intensity and element concentration based on the Lomakin-Scherbe formula which is the guarantee of the high accuracy of the SSC-LIBS. Thus, the self-absorption effect limits the quantitative accuracy of SSC-LIBS greatly. In this work, an improved SSC-LIBS with self-absorption correction (SSC-LIBS with SAC) is proposed for the promotion of quantitative accuracy of SSC-LIBS. The SSC-LIBS with SAC can correct the intensity ratio of spectral lines in the calculation of SSC-LIBS through relative self-absorption coefficient K without complicated preparatory information. The alloy samples and pressed ore samples were used to verify the effect of the SSC-LIBS with SAC. Compared with SSC-LIBS, for alloy samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 0.83 wt.% and 13.75% to 0.40 wt.% and 4.06%, respectively. For the pressed ore samples, the average RMSEP and average ARE of SSC-LIBS with SAC decreased from 4.77 wt.% and 90.48% to 2.34 wt.% and 14.60%. The experimental result indicates that SSC-LIBS with SAC has a great improvement of quantitative accuracy and better universality compared with traditional SSC-LIBS, which is a mighty promotion of the wide application of SSC-LIBS.

Due to the effect of bagging on fruit growth, non-destructive and in situ soluble solid content (SSC) in citrus detection remains a challenge. In this work, a new method for accurately quantifying SSC in citrus using hyperspectral imaging of citrus leaves was proposed. Sixty-five Ehime Kashi No. 28 citruses with surrounding leaves picked at two different times were picked for the experiment. Using the principal components analysis combined with Gaussian process regression model, the correlation coefficients of prediction-real value by citrus and its leaves in cross-validation were 0.972 and 0.986, respectively. In addition, the relationship between citrus leaves and SSC content was further explored, and the possible relationship between chlorophyll in leaves and SSC of citrus was analyzed. Comparing the quantitative analysis results by citrus and its leaves, the results show that the proposed method is a non-destructive and reliable method for determining the SSC by citrus leaves and has broad application prospects in indirect detection of citrus.

As an important variant of calibration-free laser-induced breakdown spectroscopy (CF-LIBS), one-point calibration LIBS (OPC-LIBS) corrects the Boltzmann plot of the unknown sample by using one known sample and obtains higher quantitative accuracy than CF-LIBS. However, the self-absorption effect restricts its accuracy. In this work, a new self-absorption correction (SAC) method for OPC-LIBS is proposed to solve this problem. This method uses an algorithm to correct the self-absorption and does not require the calculation of the self-absorption coefficient. To verify the effectiveness of this SAC method, Ti, V, and Al elements in two titanium alloys were determined by classical OPC-LIBS and OPC-LIBS with SAC. The average relative errors (AREs) of all elements in the two samples were decreased from 8.78% and 9.28% to 8.07% and 7.56%, respectively. The results demonstrated the effectiveness of this SAC method for OPC-LIBS.

Electrolyte disturbance is very common and harmful, increasing the mortality of critical patients. Hence, rapid and accurate detection of electrolyte levels is vital in clinical practice. Laser-induced breakdown spectroscopy (LIBS) has the advantage of rapid and simultaneous detection of multiple elements, which meets the needs of clinical electrolyte detection. However, the cracking caused by serum drying and the effect of the coffee-ring led to the unstable spectral signal of LIBS and inaccurate detection results. Herein, we propose the ordered microarray silicon substrates (OMSS) obtained by laser microprocessing, to solve the disturbance caused by cracking and the coffee-ring effect in LIBS detection. Moreover, the area of OMSS is optimized to obtain the optimal LIBS detection effect; only a 10 uL serum sample is required. Compared with the silicon wafer substrates, the relative standard deviation (RSD) of the serum LIBS spectral reduces from above 80.00% to below 15.00% by the optimized OMSS, improving the spectral stability. Furthermore, the OMSS is combined with LIBS to form a sensing platform for electrolyte disturbance detection. A set of electrolyte disturbance simulation samples (80% of the ingredients are human serum) was prepared for this platform evaluation. Finally, the platform can achieve an accurate quantitative detection of Na and K elements (Na: RSD 2 = 0.991; K: RSD 2 = 0.981), and the detection time is within 5 min. The LIBS sensing platform has a good prospect in clinical electrolyte detection and other blood-related clinical diagnoses.

Radiofrequency (RF) coaxial cables are one of the vital components for the power sources of capacitively coupled plasmas (CCPs), by which the RF power is transferred to excite the plasma. Usually, the cables can be treated as transmission lines (TLs). However, few studies of TLs in CCP power sources were conducted due to the nonlinear coupling between TLs and the plasma. In this work, we developed a numerical scheme of TLs based on the Lax–Wendroff method and realized the nonlinear bidirectional coupling among the lumped-element model, transmission line model, and electrostatic particle-in-cell model. Based on the combined model, three discharge patterns were found, including weak matching state, normal state, and over matching state. The great differences among the three patterns indicated that the TLs could change the impedance matching of the device and significantly affect the plasma properties.

Measurements on refractive index and its distribution of microfluidics are widely applied in the field of biological detection, chemical analysis, material researches, medical diagnostics, and lab on a chip. Since the volume of the microfluidics is ultra-small, such as nanoliters or even picoliters, measuring the refractive index of microfluidics is challenging. We here presents an effective method to measure the refractive index and its two-dimensional distribution of picoliter microfluidics by the SWLI (scanning white light interference) microscopy technology. This method has the advantages of simplicity, low cost, non-contact, wide measurement range and high precision. We conduct the experiment to measure the refractive index of 2.4 picoliters liquid. The resolution is 10-4RIU (refractive index unit), and the relative error is 10-4 compared with the Abbe refractometer. We obtained the relationship between refractive index and concentration of glucose solution. In addition, we can measure the 2D (two-dimensional) distribution of RI (refractive index) of liquids. We can observe the liquid interface between deionized water and liquid paraffin clearly. Besides, we can observe the process of solute dissolution in the liquid and the change of refractive index.

Quantitative trace element analysis is crucial to a range of industries, including food, natural and man-made materials, and biomedicine. For this purpose, we here developed laser-induced breakdown spectroscopy (LIBS) with dried droplet sample pretreatment. This novel method is quantitative and especially suited to analyzing small solid samples with trace concentrations of low atomic number elements. The dried droplet pretreatment involves acid digesting the sample and allowing a droplet to dry on filter paper free of the analytes. LIBS was then performed on the dried droplet using pulsed laser ablation and spectroscopic detection from 200–1000 nm with 0.1 nm resolution. A standard addition approach was employed to quantify LIBS signals by adding known amounts of analytes into the droplets. The method was demonstrated by measuring sodium, potassium and calcium in four selected samples. All the standard addition calibration curves showed good linearity, with all regression coefficients >0.981 and relative standard deviation

Heavy metal particles in water are mainly derived from acidic industrial wastewater and seriously endanger the environment and public health. In this work, the pH effect on the detection of toxic metals in wastewater by laser-induced breakdown spectroscopy coupled with a phase transformation method (LIBS-PT) was investigated. Heavy metals of cadmium (Cd) and chromium (Cr) were selected as examples. The results showed that the presence of acids in wastewater inhibited the spectral enhancement of LIBS-PT on a metal substrate. This was mainly due to the presence of the salt floccule formed by the reaction of an acid with a metal substrate on the substrate surface. The floccule content increased as pH decreased, and the corresponding substrate ablation threshold increased. Therefore, more laser energy was used for ablation, resulting in reduced laser energy for ionization and reduced electron density. Eventually, spectral intensity decreased as electron density decreased. However, there was no significant change in plasma temperature. Meanwhile, the determination coefficients (R2) of Cd and Cr were all above 0.99 under the optimal pH 6.5 and on the optimal zinc (Zn) substrate. Limits of detection (LoDs) of 0.0089 mg L−1 and 0.0006 mg L−1 for Cd and Cr were obtained, respectively. The LoDs of Cd and Cr elements met the sewage discharge standard of China. The results indicated that the detection sensitivity of heavy metal elements in acidic wastewater can be significantly improved by optimizing the pH value of the solution using LIBS-PT.