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A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Analytical Methods, Instrumentation and Miniaturization".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 20130

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Special Issue Editor

Prof. Dr. Zhe Wang

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Guest Editor

Department of Energy and Power Engineering, Tsinghua University, Beijing 10084, China
Interests: LIBS; quantitative analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am delighted to organize a Special Issue in the journal Chemosensors on the topic “Application of Laser-Induced Breakdown Spectroscopy”. The main purpose of this SI is to report the recent progress made in the application of LIBS in different fields to provide a clearer picture on how this technology should be developed in the future and to show how powerful it is to people who are interested in elementary chemical analysis.

Any interesting applications with unique facility design, quantification methods, understanding improvement, and successful demonstration are welcome.

Prof. Dr. Zhe Wang
Guest Editor

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Keywords

LIBS
laser-induced breakdown spectroscopy
quantative analysis
qualitative analysis
classification
application

Related Special Issue

Application of Laser-Induced Breakdown Spectroscopy, 2nd Edition in Chemosensors (2 articles)

Published Papers (12 papers)

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Research

19 pages, 12063 KiB

Open AccessArticle

Determination of Elemental Composition and Content in Stream Sediments by Laser-Induced Breakdown Spectroscopy

by Hongpeng Wang, Xinru Yan, Yingjian Xin, Peipei Fang, Yian Wang, Sicong Liu, Jianjun Jia, Liang Zhang and Xiong Wan

Chemosensors 2023, 11(7), 377; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors11070377 - 05 Jul 2023

Viewed by 1013

Abstract

The stream sediment (SS) records evolution information of the water system structure and sedimentary environment in specific regions during different geological periods, which is of great significance for studying the ancient planetary environment and the law of water system changes. Based on the SS of different geographical environments on Earth, remote laser-induced breakdown spectroscopy (remote-LIBS) technology combined with the multidimensional scaling-back propagation neural network (MDS-BPNN) algorithm was used to conduct an in-depth analysis of remote qualitative and quantitative detection of the elemental composition and content of SS. The results show that the detection system based on remote LIBS combined with an artificial neural network algorithm can achieve an ideal quantitative analysis of major and trace elements. The coefficients of determination (R²) of the test set for major elements is greater than 0.9996, and the root mean square error (RMSE) is less than 0.7325. The coefficients of determination (R²) of the test set for trace elements is greater than 0.9837, and the root mean square error is less than 42.21. In addition, for the application scenario of exploring extraterrestrial life, biominerals represented by stromatolite phosphorite (SP) are easy to form sand and enter into SS under weathering. Therefore, this paper discusses the feasibility of using remote-LIBS technology to detect and identify such minerals under the disappearance of SPs’ macro- and micro-characteristics. From our research, we can find that remote-LIBS technology is the preferred candidate for discovering dust-covered biominerals. In geological environments rich in water system sedimentary rocks, such as Mars’ ancient riverbeds, LIBS technology is crucial for deciphering the “life signals” hidden in the Martian sand. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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15 pages, 4956 KiB

Open AccessArticle

Highly Sensitive Detection of Heavy Metal Elements Using Laser-Induced Breakdown Spectroscopy Coupled with Chelating Resin Enrichment

by Jinmei Wang, Gang Li, Peichao Zheng, Sahar Shata, Hafiz Imran Ahmad Qazi, Jianshu Lu, Shaojian Liu, Hongwu Tian and Daming Dong

Chemosensors 2023, 11(4), 228; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors11040228 - 07 Apr 2023

Cited by 1 | Viewed by 1540

Abstract

In this study, we demonstrate a unique method for the detection of heavy metals such as chromium (Cr), copper (Cu), lead (Pb), and nickel (Ni) at trace levels in aqueous solutions via laser-induced breakdown spectroscopy (LIBS) enriched using chelating resin. Reduction in sample time preparation was shortened by tailoring the sample processing time with the aid of an enrichment method. The calibration curves and LODs of CH-90 chelating resin for enriching Cr, Cu, Pb, and Ni cations were established under the optimal conditions. The linear correlation coefficients were all above 0.98, and the detection limits were 0.148 mg/L, 0.150 mg/L, 0.149 mg/L, and 0.240 mg/L, corresponding to Cr, Cu, Pb, and Ni, respectively. The quantitative evaluation of the obtained results signifies that the proposed method is highly sensitive for detecting trace elements and offers a good correspondence of the acquired linear correlation with its calibration model. Results comparison with past studies suggests that the proposed method is able to achieve lower LODs for elements under investigation. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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13 pages, 3098 KiB

Open AccessArticle

Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing

by Yang Ni, Bowen Fan, Bin Fang, Jiuling Meng, Yubo Zhang and Tao Lü

Chemosensors 2022, 10(11), 472; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10110472 - 11 Nov 2022

Viewed by 1364

Abstract

Minor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni), chromium (Cr), and titanium (Ti) in stainless steel. For data pre-processing, cubic spline interpolation and wavelet threshold transform algorithms were used to perform baseline removal and denoising. The results show that this set of pre-processing methods can effectively improve the signal-to-noise ratio, remove the baseline of spectral baseline, reduce the average relative error, and reduce relative standard deviation of BP-ANN predictions. It indicates that BP-ANN combined with pre-processing methods has promising applications for the determination of Ni, Cr, and Ti in stainless steel with LIBS and improves prediction accuracy and stability. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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15 pages, 4948 KiB

Open AccessArticle

A Comparative Study of the Method to Rapid Identification of the Mural Pigments by Combining LIBS-Based Dataset and Machine Learning Methods

by Duixiong Sun, Yiming Zhang, Yaopeng Yin, Zhao Zhang, Hengli Qian, Yarui Wang, Zongren Yu, Bomin Su, Chenzhong Dong and Maogen Su

Chemosensors 2022, 10(10), 389; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10100389 - 24 Sep 2022

Cited by 6 | Viewed by 1372

Abstract

Due to the similar chemical composition and matrix effect, the accurate identification of mineral pigments on wall paintings has brought great challenges. This work implemented an identification study on three mineral pigments with similar chemical compositions by combining LIBS technology with the K-nearest neighbor algorithm (KNN), random forest (RF support vector machine (SVM), back propagation artificial neural network (Bp-ANN) and convolutional neural network (CNN) to find the most suitable identification method for mural research. Using the SelectKBest algorithm, 300 characteristic lines with the largest difference among the three pigments were determined. The identification models of KNN, RF, SVM, Bp-ANN and CNN were established and optimized. The results showed that, except for the KNN model, the identification accuracy of other models for mock-up mural samples was above 99%. However, only the identification accuracy of 2D-CNN models reached above 94% for actual mural samples. Therefore, the 2D-CNN model was determined as the most suitable model for the identification and analysis of mural pigments. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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15 pages, 3706 KiB

Open AccessArticle

Chitosan Homogenizing Coffee Ring Effect for Soil Available Potassium Determination Using Laser-Induced Breakdown Spectroscopy

by Xiaolong Li, Rongqin Chen, Zhengkai You, Tiantian Pan, Rui Yang, Jing Huang, Hui Fang, Wenwen Kong, Jiyu Peng and Fei Liu

Chemosensors 2022, 10(9), 374; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10090374 - 18 Sep 2022

Cited by 1 | Viewed by 1910

Abstract

In order to rationally apply potassium fertilizer, it is very important to realize the rapid and accurate evaluation of soil available potassium (K). Conventional methods are time-consuming, consumables-consuming and laborious. A high-efficiency method was proposed in this study to meet the demand for rapid evaluation, including rapid extraction, uniform evaporation and LIBS detection. To shorten the extraction time, we increased the oscillation frequency and removed the operation of dry filtration. Compared with the conventional extraction method of the Chinese national standard (CNS), the extraction time was reduced from 30 min to 2 min. In addition, we developed a uniform evaporation method for liquid–solid transformation on the batch-detection fixed area aluminum substrate. This method reduced the moisture interference. At the same time, increasing the liquid viscosity and restricting the liquid area and shape could reduce the coffee ring effect (CRE). The determination coefficient of the calibration curve by our method was 0.99, and the limit of quantitation reached 0.8 mg/kg. Real soil samples were taken as validation, and the average relative error between our method and the CNS method was 3.58%. The results indicate that our method combined with LIBS technology could provide a fast and accurate evaluation of soil available K. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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10 pages, 2936 KiB

Open AccessArticle

Elemental Analysis of V, Mo, Cr, Mn, Al, Ni, and Cu in Steel Alloy with Femtosecond Laser Ablation Spark-Induced Breakdown Spectroscopy

by Xiaoyong He, Qi Yang, Dongxiong Ling, Dongshan Wei and Hongcheng Wang

Chemosensors 2022, 10(9), 370; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10090370 - 17 Sep 2022

Cited by 2 | Viewed by 1583

Abstract

Femtosecond laser ablation spark-induced breakdown spectroscopy (fs LA-SIBS) was developed to quantitatively analyze vanadium, molybdenum, chromium, manganese, aluminum, nickel, and copper in a steel alloy. In the experiment, a femtosecond laser operating at a repetition rate of 1 kHz was used as the laser ablation source, and spark discharge was utilized to re-excite the plasma and enhance the atomic intensity. A compact fiber spectrometer was used to record and analyze the plasma emission spectra in a nongated signal-recording mode. The calibration curves of V, Mo, Cr, Mn, Al, Ni, and Cu elements in steel alloy samples were established, and the detection limits of these elements were determined to be 10.9, 12.6, 4.0, 5.7, 8.7, 7.9, and 3.1 ppm with fs LA-SIBS, respectively, which were 4–12-fold better than those achieved with femtosecond laser-induced breakdown spectroscopy (fs LIBS). Compared with conventional LIBS, the fs LA-SIBS technique provided a rapid and high spatial resolution approach to quantitative elemental analysis, with better analytical sensitivity. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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15 pages, 5515 KiB

Open AccessArticle

On the Spectral Identification and Wavelength Dependence of Rare-Earth Ore Emission by Laser-Induced Breakdown Spectroscopy

by Muhammad Sher Afgan, Zongyu Hou, Weiran Song, Jiachen Liu, Yuzhou Song, Weilun Gu and Zhe Wang

Chemosensors 2022, 10(9), 350; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10090350 - 25 Aug 2022

Cited by 5 | Viewed by 1745

Abstract

The increasing demand for rare earth elements (REE) requires faster analysis techniques for their rapid exploration. Laser-induced breakdown spectroscopy (LIBS) has on-site and real time analysis capability. However, interference and the weaker emission of minor REEs are key challenges for the complex REE emission spectra. Using simulations and experimental results, we presented essential principles for improved line identification in the transient spectra of complicated samples, such as those of REE ores (e.g., monazite). Knowledge of plasma conditions, spectral collection setup, and capability of the spectral system are key parameters to consider for the identification of an emission line in such spectra. Furthermore, emission intensity dependence on laser wavelength was analyzed for major and minor REEs using IR (1064 nm), visible (532 nm) and UV (266 nm) irradiation. A higher plasma temperature was found with the IR laser, while stronger material ablation was observed by UV irradiation. Higher particle density by UV laser ablation was the key factor in the higher signal intensity of the minor elements, and this laser can improve the emission signals for LIBS use as an REE analyzer. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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17 pages, 7268 KiB

Open AccessArticle

Study on LIBS Standard Method via Key Parameter Monitoring and Backpropagation Neural Network

by Rui Wang and Xiaohong Ma

Chemosensors 2022, 10(8), 312; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10080312 - 05 Aug 2022

Cited by 1 | Viewed by 1248

Abstract

This paper proposes a method based on key parameter monitoring and a backpropagation neural network to standardize LIBS spectra, named KPBP. By monitoring the laser output energy and the plasma flame morphology and using the backpropagation neural network algorithm to fit the spectral intensity, KPBP standardizes spectral segments containing characteristic lines. This study first conducted KPBP experiments on the spectra of pure aluminium, monocrystalline silicon, and pure zinc to optimize the KPBP model and then performed KPBP standardization on the characteristic spectral lines of a GSS-8 standard soil sample. The spectral intensity relative standard deviations (RSDs) of Al 257.51 nm, Si 298.76 nm, and Fe 406.33 nm dropped from 12.57%, 16.60%, and 14.10% to 3.40%, 3.20%, and 4.07%, respectively. Compared with the internal standard method and the standard normal variate method, KPBP obtained the smallest RSD. The study also used a GSS-23 standard soil sample and a Beijing farmland soil sample to conduct KPBP optimization experiments. The RSD of spectral intensity was still significantly reduced, proving that the KPBP method has stable effects and wide applicability to improve the repeatability of LIBS soil analysis. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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13 pages, 3591 KiB

Open AccessCommunication

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

by Xinyang Zhang, Zhongmou Sun, Zhuoyan Zhou, Saifullah Jamali and Yuzhu Liu

Chemosensors 2022, 10(7), 259; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10070259 - 03 Jul 2022

Cited by 6 | Viewed by 1737

Abstract

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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8 pages, 1888 KiB

Open AccessCommunication

Study on Microdamage Quantitative Analysis of Cd and Pb in Leaves by Laser Induced Breakdown Spectroscopy

by Li Fang, Mingjun Ma, Gaofang Yin, Xiaowei Chen, Fuqiang Chen and Nanjing Zhao

Chemosensors 2022, 10(7), 242; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10070242 - 25 Jun 2022

Cited by 3 | Viewed by 1362

Abstract

Recent years, research on the detection of heavy metals in Traditional Chinese Medicine (TCM) by laser induced breakdown spectroscopy (LIBS) have gradually increased. Current main methods of establishing calibration curve are based on grounding and pelleting of the tested samples. Although compared to digested samples, grounding and pelleting of the sample is already quite simple, it cannot fully reflect the advantages of LIBS: rapid analysis, and, also, the uneven distribution of heavy metals in the TCM is ignored. In order to avoid grinding and pelleting sample to be tested, and to achieve microdamage quantitative analysis by LIBS, this article presents a new method for establishing calibration curve. The experiment in this paper based on a study with Cd and Pb in leaves of laurel. The preparation of calibration samples and the establishment of calibration methods for microdamage quantitative analysis were presented, which proved the feasibility of microdamage quantitative analysis by LIBS. The square of the linear relationship coefficient R of Pb was higher than 0.82. This method provides a guiding method for the rapid quantitative analysis of heavy metals in TCM by LIBS. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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13 pages, 3048 KiB

Open AccessArticle

Classification of Aviation Alloys Using Laser-Induced Breakdown Spectroscopy Based on a WT-PSO-LSSVM Model

by Haorong Guo, Minchao Cui, Zhongqi Feng, Dacheng Zhang and Dinghua Zhang

Chemosensors 2022, 10(6), 220; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10060220 - 10 Jun 2022

Cited by 4 | Viewed by 1559

Abstract

It is well-known that aviation alloys of different grades show large differences in mechanical properties. At present, alloys must be strictly distinguished in the manufacturing plant because their close appearance and density are easily confused In this work, the wavelet transform (WT) method combined with the least squares support vector machine (LSSVM) is applied to the classification and identification of aviation alloys by laser-induced breakdown spectroscopy (LIBS). This experiment employed six different grades of aviation alloy as the classification samples and obtained 100 sets of spectral data for each sample. This research included the steps of preprocessing the obtained spectral data, model training, and parameter optimization. Finally, the accuracy of the training set was 99.98%, and the accuracy of the test set was 99.56%. Therefore, it is concluded that the model has superior generalization capacity and portability. The result of this work illustrates that LIBS technology can be adopted for the rapid identification of aviation alloys, which is of great significance for on-site quality control and efficiency improvement of aerospace parts manufacturing. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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10 pages, 2040 KiB

Open AccessArticle

Estimation of Grain Size in Randomly Packed Granular Material Using Laser-Induced Breakdown Spectroscopy

by Songting Li, Yaju Li, Xiaolong Li, Liangwen Chen, Dongbin Qian, Shaofeng Zhang and Xinwen Ma

Chemosensors 2022, 10(4), 144; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors10040144 - 13 Apr 2022

Cited by 4 | Viewed by 1884

Abstract

Grain size is one of the most important physical parameters for randomly packed granular (RPG) materials. Its estimation, especially in situ, plays a key role in many natural and industrial processes. Here, the application of laser-induced breakdown spectroscopy (LIBS) was investigated experimentally to estimate the grain size in RPG materials. The experiment was performed by taking sieved copper microspheres with discrete median diameters ranging from 53 to 357 μm as examples and by measuring the plasma emissions induced by 1064 nm laser pulses with a duration of 7 ns in an air environment. It was found that the plasma emission measurements were successful in estimating the grain median diameter via monitoring the variations in plasma temperature (electron density) at the range of median diameter below (above) a critical value. In addition, it was demonstrated that, when plasma temperature serves as an indicator of grain size, the intensity ratio between two spectral lines from different upper energy levels of the same emitting species can be used as an alternative indicator with higher sensitivity. The results show the potential of using LIBS for in situ estimation of grain size in RPG materials for the first time. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy)

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