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Ferreira DS, Pereira FMV, Olivieri AC, Pereira-Filho ER. Electronic waste analysis using laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF): Critical evaluation of data fusion for the determination of Al, Cu and Fe. Anal Chim Acta 2024; 1303:342522. [PMID: 38609264 DOI: 10.1016/j.aca.2024.342522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Electronic waste (e-waste) proliferation and its implications underscore the imperative for advanced analytical methods to mitigate its environmental impact. It is estimated that e-waste production stands at a staggering 20-50 million tons yearly, of which merely 20-25% undergo formal recycling. The e-waste samples evaluated contain computers, laptops, smartphones, and tablets. RESULTS Forty-one samples were processed, involving the disassembly and separation of components. Subsequently, two analytical techniques, laser-induced breakdown spectroscopy (LIBS) and energy dispersive X-ray fluorescence (ED-XRF), were applied to quantify aluminum (Al), copper (Cu), and iron (Fe) in the e-waste samples. The samples were then analyzed after acid mineralization with 50% v v-1 aqua regia in a digester block and finally by ICP OES. A solid residue composed of Si and Ti was observed after the digestion of the samples. Multivariate calibration strategies such as partial least-squares regression (PLS), principal component regression (PCR), maximum likelihood principal component regression (MLPCR), and error covariance penalized regression (ECPR) were used for calibration. Finally, the figures of merit were calculated to verify the most suitable models. The results revealed robust models with notable sensitivity, varying from 8.98 to 35.04 Signal (a.u.)(% w w-1) -1, low Limits of Detection (LoD) within the range of 0.001-0.2 % w w-1, and remarkable relative errors ranging from 2% to 33%, particularly for Cu and Fe. SIGNIFICANCE Notably, the models for Al faced inherent challenges, thus highlighting the complexities associated with its quantification in e-waste samples. In conclusion, this research represents an important step toward a more sustainable and efficient future for electronic waste recycling, signifying its relevance to global environmental welfare and resource conservation.
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Affiliation(s)
- Dennis S Ferreira
- Group of Applied Instrumental Analysis (GAIA), Department of Chemistry, Federal University of São Carlos (UFSCar), P.O. Box 676, São Carlos, São Paulo State, 13565-905, Brazil; Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina; Instituto de Química Rosario (CONICET-UNR), 27 de Febrero 210 Bis, 2000, Rosario, Argentina
| | - Fabiola M V Pereira
- Group of Alternative Analytical Approaches (GAAA), Bioenergy Research Institute (IPBEN), Institute of Chemistry, São Paulo State University (UNESP), Araraquara, São Paulo, 14800-060, Brazil
| | - Alejandro C Olivieri
- Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina; Instituto de Química Rosario (CONICET-UNR), 27 de Febrero 210 Bis, 2000, Rosario, Argentina
| | - Edenir R Pereira-Filho
- Group of Applied Instrumental Analysis (GAIA), Department of Chemistry, Federal University of São Carlos (UFSCar), P.O. Box 676, São Carlos, São Paulo State, 13565-905, Brazil.
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Shi C, Zuo X, Yan B. Selective recovery of nickel from stainless steel pickling sludge with NH 3-(NH 4) 2CO 3 leaching system. ENVIRONMENTAL TECHNOLOGY 2023; 44:3249-3262. [PMID: 35319346 DOI: 10.1080/09593330.2022.2056085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
The recovery of valuable metals from stainless steel pickling sludge(SSPS) has great economic and environmental benefits. In this study, a new method is proposed for selective recovery of nickel from SSPS by NH3-(NH4)2CO3 ammonia leaching system. The Eh-pH diagram was used to analyze Ni, Fe, Cr leaching behavior during the ammonia leaching process. Nickel can be leached as the complex [Ni(NH3)n]2+, whereas Fe and Cr remain as precipitates in the leaching slag. The effects of NH3·H2O concentration, liquid-solid ratio, reaction temperature, and reaction time on the leaching efficiency of nickel in the ammonia leaching system were analyzed and optimized by single-factor study and response surface analysis, and the kinetics were analyzed. The optimal conditions for Ni leaching were found to be 28.28 min, 54.07 °C, a liquid-solid ratio of 23.7:1, and NH3·H2O concentration of 5.10 mol/L. Each factor had a greater effect on the rate of Ni leaching in the following order: liquid-solid ratio > NH3·H2O concentration > leaching time > leaching temperature. The ammonia leaching recovery system was controlled by chemical reaction and the activation energy was 58.17 KJ/mol. The results of scanning electron microscopy-energy dispersion spectrum (SEM-EDS), x-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) show that the leaching slag was in granular form with agglomerated particles and particle size of approximately 2.8 μm The major components of the leaching slag were Fe(OH)3, Fe2O3, Fe(OH)2, Cr(OH)3, and Cr2O3. Therefore, this study provides a new and effective way of using the resources of SSPS.
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Affiliation(s)
- Chunhong Shi
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, People's Republic of China
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants School of Water Resources and Environment, Beijing, People's Republic of China
| | - Xiangmeng Zuo
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, People's Republic of China
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants School of Water Resources and Environment, Beijing, People's Republic of China
| | - Bo Yan
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, People's Republic of China
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants School of Water Resources and Environment, Beijing, People's Republic of China
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Wei L, Ding Y, Chen J, Yang L, Wei J, Shi Y, Ma Z, Wang Z, Chen W, Zhao X. Quantitative analysis of fertilizer using laser-induced breakdown spectroscopy combined with random forest algorithm. Front Chem 2023; 11:1123003. [PMID: 36711235 PMCID: PMC9880321 DOI: 10.3389/fchem.2023.1123003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 01/03/2023] [Indexed: 01/15/2023] Open
Abstract
Chemical fertilizers are important for effectively improving soil fertility, promoting crop growth, and increasing grain yield. Therefore, methods that can quickly and accurately measure the amount of fertilizer in the soil should be developed. In this study, 20 groups of soil samples were analyzed using laser-induced breakdown spectroscopy, and partial least squares (PLS) and random forest (RF) models were established. The prediction performances of the models for the chemical fertilizer content and pH were analyzed as well. The experimental results showed that the R 2 and root mean square error (RMSE) of the chemical fertilizer content in the soil obtained using the full-spectrum PLS model were .7852 and 2.2700 respectively. The predicted R 2 for soil pH was .7290, and RMSE was .2364. At the same time, the full-spectrum RF model showed R 2 of .9471 (an increase of 21%) and RMSE of .3021 (a decrease of 87%) for fertilizer content. R 2 for the soil pH under the RF model was .9517 (an increase of 31%), whereas RMSE was .0298 (a decrease of 87%). Therefore, the RF model showed better prediction performance than the PLS model. The results of this study show that the combination of laser-induced breakdown spectroscopy with RF algorithm is a feasible method for rapid determination of soil fertilizer content.
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Affiliation(s)
- Lai Wei
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yu Ding
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China,*Correspondence: Yu Ding,
| | - Jing Chen
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Linyu Yang
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Jinyu Wei
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yinan Shi
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Zigao Ma
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Zhiying Wang
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Wenjie Chen
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Xingqiang Zhao
- Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China,Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China,School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
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Andrade DF, Castro JP, Garcia JA, Machado RC, Pereira-Filho ER, Amarasiriwardena D. Analytical and reclamation technologies for identification and recycling of precious materials from waste computer and mobile phones. CHEMOSPHERE 2022; 286:131739. [PMID: 34371353 DOI: 10.1016/j.chemosphere.2021.131739] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 07/20/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
Waste electrical and electronic equipment (WEEE) is one of the world's fastest-growing class of waste. WEEE contain a large amount of precious materials that have aroused the interest to develop new recycling technologies. Hence, effective recycling strategies are extremely necessary to promote the proper handling of these materials as well as for environmentally sound recovery of secondary raw resource. This paper reviews important existing methods and emerging technologies in WEEE management, with special emphasis in characterization, extraction and reclamation of precious materials from waste computer and mobile phones. Traditional pyrometallurgical and hydrometallurgical technologies still play a central role in the recovery of metals. More recently, emerging greener recycling technologies using microorganisms (i.e. biometallurgical), plasma arc fusion method and pretreatments (i.e. ultrasound and mechanochemical technologies) combined with other recycling methods (e.g. hydrometallurgical), and using less toxic solvents such as ionic liquids (ILs) and deep eutectic solvents (DESs) have also been attempted to recycle metals from computer and mobile phone scrap. The role of analytical method development, especially using spectroanalytical methods for chemical inspection and e-waste sorting process at industrial applications is also discussed. This confirmed that most direct sampling techniques such as laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XFR) have several advantages over traditional sorting methods including rapid analytical response, without use of chemical reagents or waste generation, and greater reclamation of precious and critical materials in the WEEE stream.
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Affiliation(s)
- Daniel Fernandes Andrade
- Group of Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, Rod Washington Luiz, km 235, 13565905, São Carlos, SP, Brazil; School of Natural Science, Hampshire College, 01002, Amherst, MA, USA
| | - Jeyne Pricylla Castro
- Group of Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, Rod Washington Luiz, km 235, 13565905, São Carlos, SP, Brazil
| | - José Augusto Garcia
- Group of Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, Rod Washington Luiz, km 235, 13565905, São Carlos, SP, Brazil; SG Soluções Científicas, 13560660, São Carlos, SP, Brazil
| | - Raquel Cardoso Machado
- Group of Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, Rod Washington Luiz, km 235, 13565905, São Carlos, SP, Brazil
| | - Edenir Rodrigues Pereira-Filho
- Group of Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, Rod Washington Luiz, km 235, 13565905, São Carlos, SP, Brazil
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Review of Element Analysis of Industrial Materials by In-Line Laser—Induced Breakdown Spectroscopy (LIBS). APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11199274] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Laser-induced breakdown spectroscopy (LIBS) is a rapidly developing technique for chemical materials analysis. LIBS is applied for fundamental investigations, e.g., the laser plasma matter interaction, for element, molecule, and isotope analysis, and for various technical applications, e.g., minimal destructive materials inspection, the monitoring of production processes, and remote analysis of materials in hostile environment. In this review, we focus on the element analysis of industrial materials and the in-line chemical sensing in industrial production. After a brief introduction we discuss the optical emission of chemical elements in laser-induced plasma and the capability of LIBS for multi-element detection. An overview of the various classes of industrial materials analyzed by LIBS is given. This includes so-called Technology materials that are essential for the functionality of modern high-tech devices (smartphones, computers, cars, etc.). The LIBS technique enables unique applications for rapid element analysis under harsh conditions where other techniques are not available. We present several examples of LIBS-based sensors that are applied in-line and at-line of industrial production processes.
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Fast detection of harmful trace elements in glycyrrhiza using standard addition and internal standard method – Laser-induced breakdown spectroscopy (SAIS-LIBS). Microchem J 2021. [DOI: 10.1016/j.microc.2021.106408] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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