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Wen X, Cao F, Yang C, Gao Z, Tian H, Zhao X, Guo L, Ma S, Dong D. Simple and sensitive determination of Cr (III), Cu (II) and Pb (II) in tea infusions using AgNPs-modified resin combined with laser-induced breakdown spectroscopy. Food Chem 2024; 448:139210. [PMID: 38569408 DOI: 10.1016/j.foodchem.2024.139210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/26/2024] [Accepted: 03/30/2024] [Indexed: 04/05/2024]
Abstract
The detection of heavy metals in tea infusions is important because of the potential health risks associated with their consumption. Existing highly sensitive detection methods pose challenges because they are complicated and time-consuming. In this study, we developed an innovative and simple method using Ag nanoparticles-modified resin (AgNPs-MR) for pre-enrichment prior to laser-induced breakdown spectroscopy for the simultaneous analysis of Cr (III), Cu (II), and Pb (II) in tea infusions. Signal enhancement using AgNPs-MR resulted in amplification with limits of detection of 0.22 μg L-1 for Cr (III), 0.33 μg L-1 for Cu (II), and 1.25 μg L-1 for Pb (II). Quantitative analyses of these ions in infusions of black tea from various brands yielded recoveries ranging from 83.3% to 114.5%. This method is effective as a direct and highly sensitive technique for precisely quantifying trace concentrations of heavy metals in tea infusions.
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Affiliation(s)
- Xuelin Wen
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
| | - Fengjing Cao
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Chongshan Yang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Zhen Gao
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Hongwu Tian
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Xiande Zhao
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Lianbo Guo
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
| | - Shixiang Ma
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China.
| | - Daming Dong
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
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2
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Abimbola I, McAfee M, Creedon L, Gharbia S. In-situ detection of microplastics in the aquatic environment: A systematic literature review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173111. [PMID: 38740219 DOI: 10.1016/j.scitotenv.2024.173111] [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: 03/28/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.
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Affiliation(s)
- Ismaila Abimbola
- Department of Environmental Science, Faculty of Science, Atlantic Technological University, Sligo, Ireland.
| | - Marion McAfee
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, Sligo, Ireland
| | - Leo Creedon
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, Sligo, Ireland
| | - Salem Gharbia
- Department of Environmental Science, Faculty of Science, Atlantic Technological University, Sligo, Ireland
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Wu L, Liu C, Yao T, Shi Y, Shen J, Gao X, Qin K. Structural and Compositional Changes in Two Marine Shell Traditional Chinese Medicines: A Comparative Analysis Pre- and Post-Calcination. J AOAC Int 2024; 107:704-713. [PMID: 38492563 DOI: 10.1093/jaoacint/qsae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/01/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Arcae concha and Meretricis concha cyclinae concha are two marine shellfish herbs with similar composition and efficacy, which are usually calcined and used clinically. OBJECTIVE This study investigated variations in the inorganic and organic components of Arcae concha and Meretricis concha cyclinae concha from different production regions, both Arcae concha and Meretricis concha cyclinae concha. The aim was to enhance the understanding of these two types of marine shell traditional Chinese medicine (msTCM) and provide a foundation for their future development and application. METHOD Spectroscopic techniques, including infrared spectroscopy, X-ray spectroscopy, and X-ray fluorescence spectroscopy, were used to analyze the calcium carbonate (CaCO3) crystal and trace elements. Thermogravimetric analysis was used to investigate the decomposition process during heating. The proteins were quantified using the BCA protein assay kit. Principal component analysis (PCA) was used to classify inorganic elements in the two marine shellfish traditional Chinese medicines. RESULTS No significant differences were found among the various production regions. The crystal structure of CaCO3 in the raw products was aragonite, but it transformed into calcite after calcination. The contents of Ca, Na, Sr, and other inorganic elements were highest. The protein content was significantly reduced after calcination. Therefore, these factors cannot accurately reflect the internal quality of TCM, rendering qualitative identification challenging. CaCO3 dissolution in the decoction of Arcae concha and Meretricis concha cyclinae concha increased after calcination, aligning with the clinical application of calcined shell TCM. PCA revealed the inorganic elements in them, indicating that the variation in trace element composition among different drugs leads to differences in their therapeutic focus, which should be considered during usage. CONCLUSIONS This study clarifies the composition and structure changes of corrugated and clam shell before and after calcining, and it lays the foundation for the comprehensive utilization of marine traditional Chinese medicine. HIGHLIGHTS These technical representations reveal the differences between raw materials and processed products, which will provide support for the quality control of other shellfish TCM.
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Affiliation(s)
- Lizhu Wu
- Jiangsu Ocean University, School of Pharmacy, Lianyungang 222005, PR China
| | - Chenlu Liu
- Jiangsu Ocean University, School of Pharmacy, Lianyungang 222005, PR China
| | - Tao Yao
- Qinghai Xinda Biological Technology Co, Ltd, Xining 810100, PR China
| | - Yun Shi
- Jiangsu Ocean University, School of Pharmacy, Lianyungang 222005, PR China
| | - Jinyang Shen
- Jiangsu Ocean University, School of Pharmacy, Lianyungang 222005, PR China
| | - Xun Gao
- Jiangsu Ocean University, School of Pharmacy, Lianyungang 222005, PR China
| | - Kunming Qin
- Jiangsu Ocean University, School of Pharmacy, Lianyungang 222005, PR China
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4
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Moodley T, Abunama T, Kumari S, Amoah D, Seyam M. Applications of mathematical modelling for assessing microplastic transport and fate in water environments: a comparative review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:667. [PMID: 38935176 PMCID: PMC11211188 DOI: 10.1007/s10661-024-12731-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics may act as vectors for the dissemination of other pollutants and the transmission of microorganisms into the water environment. The currently available literature reviews focus on analysing the occurrence, environmental effects and methods of microplastic detection, however lacking a wide-scale systematic review and classification of the mathematical microplastic modelling applications. Thus, the current review provides a global overview of the modelling methodologies used for microplastic transport and fate in water environments. This review consolidates, classifies and analyses the methods, model inputs and results of 61 microplastic modelling studies in the last decade (2012-2022). It thoroughly discusses their strengths, weaknesses and common gaps in their modelling framework. Five main modelling types were classified as follows: hydrodynamic, process-based, statistical, mass-balance and machine learning models. Further, categorisations based on the water environments, location and published year of these applications were also adopted. It is concluded that addressed modelling types resulted in relatively reliable outcomes, yet each modelling framework has its strengths and weaknesses. However, common issues were found such as inputs being unrealistically assumed, especially biological processes, and the lack of sufficient field data for model calibration and validation. For future research, it is recommended to incorporate macroplastics' degradation rates, particles of different shapes and sizes and vertical mixing due to biofouling and turbulent conditions and also more experimental data to obtain precise model inputs and standardised sampling methods for surface and column waters.
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Affiliation(s)
- Tyrone Moodley
- Department of Civil Engineering and Geomatics, Durban University of Technology, Durban, 4001, South Africa
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa
| | - Taher Abunama
- Research Center for Treatment and Management of Water (CEBEDEAU), 4031, Liege, Belgium
| | - Sheena Kumari
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa
| | - Dennis Amoah
- Department of Environmental Science, University of Arizona, Tucson, 85721, USA
| | - Mohammed Seyam
- Department of Civil Engineering and Geomatics, Durban University of Technology, Durban, 4001, South Africa.
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5
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Guo P, Wang Y, Moghaddamfard P, Meng W, Wu S, Bao Y. Artificial intelligence-empowered collection and characterization of microplastics: A review. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134405. [PMID: 38678715 DOI: 10.1016/j.jhazmat.2024.134405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.
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Affiliation(s)
- Pengwei Guo
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Yuhuan Wang
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Parastoo Moghaddamfard
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Weina Meng
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shenghua Wu
- Department of Civil, Coastal, and Environmental Engineering, University of South Alabama, Mobile, AL 36688, United States
| | - Yi Bao
- Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
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6
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Martinez-Hernandez U, West G, Assaf T. Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor. SENSORS (BASEL, SWITZERLAND) 2024; 24:2821. [PMID: 38732925 PMCID: PMC11086069 DOI: 10.3390/s24092821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024]
Abstract
This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.
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Affiliation(s)
- Uriel Martinez-Hernandez
- Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
- Multimodal Interaction and Robot Active Perception (Inte-R-Action) Lab, University of Bath, Bath BA2 7AY, UK
| | - Gregory West
- Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
- Multimodal Interaction and Robot Active Perception (Inte-R-Action) Lab, University of Bath, Bath BA2 7AY, UK
| | - Tareq Assaf
- Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
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7
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Koshelev DS. Expert System for Fourier Transform Infrared Spectra Recognition Based on a Convolutional Neural Network With Multiclass Classification. APPLIED SPECTROSCOPY 2024; 78:387-397. [PMID: 38281905 DOI: 10.1177/00037028241226732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Fourier transform infrared spectroscopy (FT-IR) is a widely used spectroscopic method for routine analysis of substances and compounds. Spectral interpretation of spectra is a labor-intensive process that provides important information about functional groups or bonds present in compounds and complex substances. In this paper, based on deep learning methods of convolutional neural networks, models were developed to determine the presence of 17 classes of functional groups or 72 classes of coupling oscillations in the FT-IR spectra. Using web scanning, the spectra of 14 361 FT-IR spectra of organic molecules were obtained. Several different variants of model architectures with different sizes of feature maps have been tested. Based on the Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (GradCAM) methods, visualization tools have been developed for visualizing and highlighting the areas of absorption bands manifestation for corresponding functional groups or bonds in the spectrum. To determine 17 and 72 classes, the F1-weighted metric, which is the harmonic mean of the class' precision and class' recall weighted by class' fraction, reached 93 and 88%, respectively, when using data on the position of absorption maxima in the spectrum as an additional source layer. The resulting model can be used to facilitate the routine analysis of spectra for all areas such as organic chemistry, materials science, and biology, as well as to facilitate the preparation of the obtained experimental data for publication.
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Affiliation(s)
- Daniil S Koshelev
- Faculty of the Material Science, Lomonosov Moscow State University, Moscow, Russian Federation
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russian Federation
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8
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Ko K, Lee J, Baumann P, Kim J, Chung H. Analysis of micro(nano)plastics based on automated data interpretation and modeling: A review. NANOIMPACT 2024; 34:100509. [PMID: 38734308 DOI: 10.1016/j.impact.2024.100509] [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: 02/19/2024] [Revised: 04/11/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024]
Abstract
The widespread presence of micro(nano)plastics (MNPs) in the environment threatens ecosystem integrity, and thus, it is necessary to determine and assess the occurrence, characteristics, and transport of MNPs between ecological components. However, most analytical approaches are cost- and time-inefficient in providing quantitative information with sufficient detail, and interpreting results can be difficult. Alternative analyses integrating novel measurements by imaging or proximal sensing with signal processing and machine learning may supplement these approaches. In this review, we examined published research on methods used for the automated data interpretation of MNPs found in the environment or those artificially prepared by fragmenting bulk plastics. We critically reviewed the primary areas of the integrated analytical process, which include sampling, data acquisition, processing, and modeling, applied in identifying, classifying, and quantifying MNPs in soil, sediment, water, and biological samples. We also provide a comprehensive discussion regarding model uncertainties related to estimating MNPs in the environment. In the future, the development of routinely applicable and efficient methods is expected to significantly contribute to the successful establishment of automated MNP monitoring systems.
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Affiliation(s)
- Kwanyoung Ko
- Department of Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Juhwan Lee
- Department of Smart Agro-industry, Gyeongsang National University, Jinju 52725, Republic of Korea
| | | | - Jaeho Kim
- Department of Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Haegeun Chung
- Department of Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea.
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9
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Wang D, Yu L, Li X, Lu Y, Niu C, Fan P, Zhu H, Chen B, Wang S. Intelligent quantitative recognition of sulfide using machine learning-based ratiometric fluorescence probe of metal-organic framework UiO-66-NH 2/Ppix. JOURNAL OF HAZARDOUS MATERIALS 2024; 464:132950. [PMID: 37952335 DOI: 10.1016/j.jhazmat.2023.132950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/23/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Sulfides possess either high toxicity or play crucial physiological role such as gas transmitter dependent upon dosage, hence the significant for their rapid sensitive and selective concentration determination. Herein, a machine learning enhanced ratiometric fluorescence sensor was engineered for sulfide determination by incorporating the nanometal-organic framework (UiO-66-NH2) along with protoporphyrin IX (Ppix). The blue fluorescence at 431 nm originated from the moiety of UiO-66-NH2 by 365 nm excitation serves as an internal calibration reference signal, while the red fluorescence at 629 nm from the moiety of Ppix serves as the analytical signal, and the intensity is correlated to the amount of sulfides. The fluorescence color of the sensor gradually varies from blue to red upon sequential addition of copper and sulfide ions, resulting in RGB (Red, Green, Blue) feature values for corresponding sulfide concentrations, which facilities the advanced data processing techniques using machine learning algorithms. On the basis of fluorescence image fingerprint extraction and machine learning algorithms, an online data analysis model was developed to improve the precision and accuracy of sulfide determination. The established model employed Linear Discriminant Analysis (LDA) and was subjected to rigorous cross-validation to ensure its robustness. By analyzing the correlation between RGB feature values and sulfide concentrations, the study highlighted a significant positive relationship between the red feature values and sulfide concentrations. The application of machine learning techniques on the ratiometric fluorescence signal of the UiO-66-NH2/Ppix probe demonstrated its potential for intelligent quantitative determination of sulfides, offering a valuable and efficient tool for pollution detection and real-time rapid environmental monitoring.
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Affiliation(s)
- Degui Wang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, People's Republic of China; School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, People's Republic of China
| | - Long Yu
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, People's Republic of China.
| | - Xin Li
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, People's Republic of China
| | - Yunfei Lu
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, People's Republic of China
| | - Chaoqun Niu
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, People's Republic of China
| | - Penghui Fan
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, People's Republic of China
| | - Houjuan Zhu
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, People's Republic of China; Institute of Materials Research and Engineering, A⁎STAR (Agency for Science, Technology and Research), 138634, Singapore
| | - Bing Chen
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, People's Republic of China.
| | - Suhua Wang
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, People's Republic of China.
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10
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Ren T, Li Y, Wang X, Deng Y, Zheng C. Portable Pyrolysis-Point Discharge Optical Spectrometer for In Situ Plastic Polymer Identification by Coupling with Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:2554-2563. [PMID: 38266240 DOI: 10.1021/acs.est.3c08019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Rapid and in situ identification of specific polymers is a challenging and crucial step in plastic recycling. However, conventional techniques continue to exhibit significant limitations in the rapid and field classification of plastic products, especially with the wide range of commercially available color polymers because of their large size, high energy consumption, and slow and complicated analysis procedures. In this work, a simple analytical system integrating a miniature and low power consumption (22.3 W) pyrolyzer (Pyr) and a low temperature, atmospheric pressure point discharge optical emission spectrometer (μPD-OES) was fabricated for rapidly identifying polymer types. Plastic debris is decomposed in the portable pyrolyzer to yield volatile products, which are then swept into the μPD-OES instrument for monitoring the optical emission patterns of the thermal pyrolysis products. With machine learning, five extensively used raw polymers and their consumer plastics were classified with an accuracy of ≥97.8%. Furthermore, the proposed method was applied to the identification of the aged polymers and plastic samples collected from a garbage recycling station, indicating its great potential for identification of environmentally weathered plastics. This portable Pyr-μPD-OES system provides a cost-effective tool for rapid and field identification of polymer types of recycled plastic for proper management and resource recycling.
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Affiliation(s)
- Tian Ren
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064 ,China
| | - Yuanyuan Li
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064 ,China
| | - Xi Wang
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064 ,China
| | - Yurong Deng
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064 ,China
| | - Chengbin Zheng
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064 ,China
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11
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Lu Y, Li X, Yu L, Zhang S, Wang D, Hao X, Sun M, Wang S. Machine Learning Algorithms for Intelligent Decision Recognition and Quantification of Cr(III) in Chromium Speciation. Anal Chem 2023; 95:18635-18643. [PMID: 38064655 DOI: 10.1021/acs.analchem.3c04878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Cr(III) is a common oxidation state of chromium, and its presence in the environment can occur naturally or as a result of human activities, such as industrial processes, mining, and waste disposal. This article explores the application of machine learning algorithms for the intelligent decision recognition and quantification of Cr(III) in chromium speciation. Three different machine learning models, namely, the Decision Tree (DT) model, the PCA-SVM (Principal Component Analysis-Support Vector Machine) model, and the LDA (Linear Discriminant Analysis) model, were employed and evaluated for accurate and efficient classification of chromium concentrations based on their fluorescence responses. Furthermore, stepwise multiple linear regression analysis was utilized to achieve a more precise quantification of trivalent chromium concentrations through fluorescence visualization. The results demonstrate the potential of machine learning algorithms in accurately detecting and quantifying Cr(III) in chromium speciation with implications for environmental and industrial applications in chromium detection and quantification. The findings from this research pave the way for further exploration and implementation of these models in real-world scenarios, offering valuable insights into various environmental and industrial contexts.
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Affiliation(s)
- Yunfei Lu
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
- Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, School of Material Sciences and Technology, China University of Geosciences, Beijing 100083, China
| | - Xin Li
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Long Yu
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Songlin Zhang
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Degui Wang
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Xiangyang Hao
- Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, School of Material Sciences and Technology, China University of Geosciences, Beijing 100083, China
| | - Mingtai Sun
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Suhua Wang
- School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
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12
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Yan X, Cao Z, Murphy A, Ye Y, Wang X, Qiao Y. FRDA: Fingerprint Region based Data Augmentation using explainable AI for FTIR based microplastics classification. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165340. [PMID: 37414174 DOI: 10.1016/j.scitotenv.2023.165340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Marine microplastics (MPs) contamination has become an enormous hazard to aquatic creatures and human life. For MP identification, many Machine learning (ML) based approaches have been proposed using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR). One major challenge for training MP identification models now is the imbalanced and inadequate samples in MP datasets, especially when these conditions are combined with copolymers and mixtures. To improve the ML performance in identifying MPs, data augmentation method is an effective approach. This work utilizes Explainable Artificial Intelligence (XAI) and Gaussian Mixture Models (GMM) to reveal the influence of FTIR spectral regions in identifying each type of MPs. Based on the identified regions, this work proposes a Fingerprint Region based Data Augmentation (FRDA) method to generate new FTIR data to supplement MP datasets. The evaluation results show that FRDA outperforms the existing spectral data augmentation approaches.
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Affiliation(s)
- Xinyu Yan
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland; Luoyang Institute of Science and Technology, China.
| | - Zhi Cao
- PRISM Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Alan Murphy
- PRISM Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Yuhang Ye
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland.
| | - Xinwu Wang
- International Union Laboratory of New Civil Engineering Structure of Henan Province, China.
| | - Yuansong Qiao
- Software Research Institute, Technological University of the Shannon: Midlands, Ireland.
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Luo Y, Su W, Xu D, Wang Z, Wu H, Chen B, Wu J. Component identification for the SERS spectra of microplastics mixture with convolutional neural network. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 895:165138. [PMID: 37379925 DOI: 10.1016/j.scitotenv.2023.165138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/16/2023] [Accepted: 06/24/2023] [Indexed: 06/30/2023]
Abstract
With the increasing interest in microplastics (MPs) pollutants, relevant detection technologies are also developing. In MPs analysis, vibrational spectroscopy represented by surface-enhanced Raman spectroscopy (SERS) is widely used because they can provide unique fingerprint characteristics of chemical components. However, it is still a challenge to separate various chemical components from the SERS spectra of MPs mixture. In this study, it is innovatively proposed to combine the convolutional neural networks (CNN) model to simultaneously identify and analyze each component in the SERS spectra of six common MPs mixture. Different from the traditional method, which requires a series of spectral preprocessing such as baseline correction, smoothing and filtering, the average identification accuracy of MP components is as high as 99.54 % after the unpreprocessed spectral data is trained by CNN, which is better than other classical algorithms such as support vector machine (SVM), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), Random Forest (RF), and K Near Neighbor (KNN), with or without spectral preprocessing. The high accuracy shows that CNN can be used to quickly identify MPs mixture with unpreprocessed SERS spectra data.
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Affiliation(s)
- Yinlong Luo
- College of Science, Hohai University, Changzhou 213022, China
| | - Wei Su
- College of Science, Hohai University, Changzhou 213022, China.
| | - Dewen Xu
- College of Science, Hohai University, Changzhou 213022, China
| | - Zhenfeng Wang
- College of Science, Hohai University, Changzhou 213022, China
| | - Hong Wu
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Bingyan Chen
- College of Science, Hohai University, Changzhou 213022, China
| | - Jian Wu
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410003, China
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14
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Booth H, Ma W, Karakuş O. High-precision density mapping of marine debris and floating plastics via satellite imagery. Sci Rep 2023; 13:6822. [PMID: 37100793 PMCID: PMC10133222 DOI: 10.1038/s41598-023-33612-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/15/2023] [Indexed: 04/28/2023] Open
Abstract
The last couple of years has been ground-breaking for marine pollution monitoring purposes. It has been suggested that combining multi-spectral satellite information and machine learning approaches are effective to monitor plastic pollutants in the ocean environment. Recent research has made theoretical progress in identifying marine debris and suspected plastic (MD&SP) through machine learning whereas no study has fully explored the application of these methods for mapping and monitoring marine debris density. Therefore, this article consists of three main components: (1) the development and validation of a supervised machine learning marine debris detection model, (2) to map the MD&SP density into an automated tool called MAP-Mapper and finally (3) evaluation of the entire system for out-of-distribution (OOD) test locations. Developed MAP-Mapper architectures provide users with options to achieve high precision (abbv. -HP) or optimum precision-recall (abbv. -Opt) values in terms of training/test dataset. Our MAP-Mapper-HP model greatly increases the MD&SP detection precision to 95%, while the MAP-Mapper-Opt achieves 87-88% precision-recall pair. To efficiently measure density mapping findings at OOD test locations, we propose the Marine Debris Map (MDM) index, which combines the average probability of a pixel belonging to the MD&SP class and the number of detections in a given time frame. The high MDM findings of the proposed approach are found to be consistent with existing marine litter and plastic pollution areas, and these are presented with available evidence citing literature and field studies.
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Affiliation(s)
- Henry Booth
- School of Computer Science and Informatics, Cardiff University, Abacws, Cardiff, CF24 4AG, UK
- Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, UK
| | - Wanli Ma
- School of Computer Science and Informatics, Cardiff University, Abacws, Cardiff, CF24 4AG, UK
| | - Oktay Karakuş
- School of Computer Science and Informatics, Cardiff University, Abacws, Cardiff, CF24 4AG, UK.
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15
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Bougueroua S, Bricage M, Aboulfath Y, Barth D, Gaigeot MP. Algorithmic Graph Theory, Reinforcement Learning and Game Theory in MD Simulations: From 3D Structures to Topological 2D-Molecular Graphs (2D-MolGraphs) and Vice Versa. Molecules 2023; 28:molecules28072892. [PMID: 37049654 PMCID: PMC10096312 DOI: 10.3390/molecules28072892] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/17/2023] [Accepted: 03/18/2023] [Indexed: 04/14/2023] Open
Abstract
This paper reviews graph-theory-based methods that were recently developed in our group for post-processing molecular dynamics trajectories. We show that the use of algorithmic graph theory not only provides a direct and fast methodology to identify conformers sampled over time but also allows to follow the interconversions between the conformers through graphs of transitions in time. Examples of gas phase molecules and inhomogeneous aqueous solid interfaces are presented to demonstrate the power of topological 2D graphs and their versatility for post-processing molecular dynamics trajectories. An even more complex challenge is to predict 3D structures from topological 2D graphs. Our first attempts to tackle such a challenge are presented with the development of game theory and reinforcement learning methods for predicting the 3D structure of a gas-phase peptide.
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Affiliation(s)
- Sana Bougueroua
- Université Paris-Saclay, University Evry, CY Cergy Paris Université, CNRS, LAMBE UMR8587, 91025 Evry-Courcouronnes, France
| | - Marie Bricage
- Université Paris-Saclay, University Versailles Saint Quentin, DAVID, 78000 Versailles, France
| | - Ylène Aboulfath
- Université Paris-Saclay, University Versailles Saint Quentin, DAVID, 78000 Versailles, France
| | - Dominique Barth
- Université Paris-Saclay, University Versailles Saint Quentin, DAVID, 78000 Versailles, France
| | - Marie-Pierre Gaigeot
- Université Paris-Saclay, University Evry, CY Cergy Paris Université, CNRS, LAMBE UMR8587, 91025 Evry-Courcouronnes, France
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16
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Lan DY, He PJ, Qi YP, Wu TW, Xian HY, Wang RH, Lü F, Zhang H. Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste. Anal Chem 2023; 95:4412-4420. [PMID: 36820858 DOI: 10.1021/acs.analchem.2c04940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.
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Affiliation(s)
- Dong-Ying Lan
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Pin-Jing He
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Ya-Ping Qi
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Ting-Wei Wu
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Hao-Yang Xian
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Rui-Heng Wang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Fan Lü
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
| | - Hua Zhang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.,Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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17
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Höppener EM, Shahmohammadi M(S, Parker LA, Henke S, Urbanus JH. Classification of (micro)plastics using cathodoluminescence and machine learning. Talanta 2023. [DOI: 10.1016/j.talanta.2022.123985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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18
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Pořízka P, Brunnbauer L, Porkert M, Rozman U, Marolt G, Holub D, Kizovský M, Benešová M, Samek O, Limbeck A, Kaiser J, Kalčíková G. Laser-based techniques: Novel tools for the identification and characterization of aged microplastics with developed biofilm. CHEMOSPHERE 2023; 313:137373. [PMID: 36435319 DOI: 10.1016/j.chemosphere.2022.137373] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 06/16/2023]
Abstract
Microplastics found in the environment are often covered with a biofilm, which makes their analysis difficult. Therefore, the biofilm is usually removed before analysis, which may affect the microplastic particles or lead to their loss during the procedure. In this work, we used laser-based analytical techniques and evaluated their performance in detecting, characterizing, and classifying pristine and aged microplastics with a developed biofilm. Five types of microplastics from different polymers were selected (polyamide, polyethylene, polyethylene terephthalate, polypropylene, and polyvinyl chloride) and aged under controlled conditions in freshwater and wastewater. The development of biofilm and the changes in the properties of the microplastic were evaluated. The pristine and aged microplastics were characterized by standard methods (e.g., optical and scanning electron microscopy, and Raman spectroscopy), and then laser-induced breakdown spectroscopy (LIBS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were used. The results show that LIBS could identify different types of plastics regardless of the ageing and major biotic elements of the biofilm layer. LA-ICP-MS showed a high sensitivity to metals, which can be used as markers for various plastics. In addition, LA-ICP-MS can be employed in studies to monitor the adsorption and desorption (leaching) of metals during the ageing of microplastics. The use of these laser-based analytical techniques was found to be beneficial in the study of environmentally relevant microplastics.
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Affiliation(s)
- Pavel Pořízka
- Central European Institute of Technology, Brno University of Technology, Purkyňova 656/123, 61200, Brno, Czech Republic; Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 61669, Brno, Czech Republic
| | - Lukas Brunnbauer
- TU Wien, Institute of Chemical Technologies and Analytics, Getreidemarkt 9/164-I(2)AC, 1060, Vienna, Austria
| | - Michaela Porkert
- TU Wien, Institute of Chemical Technologies and Analytics, Getreidemarkt 9/164-I(2)AC, 1060, Vienna, Austria
| | - Ula Rozman
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna Pot 113, 1000, Ljubljana, Slovenia
| | - Gregor Marolt
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna Pot 113, 1000, Ljubljana, Slovenia
| | - Daniel Holub
- Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 61669, Brno, Czech Republic
| | - Martin Kizovský
- Institute of Scientific Instruments, Czech Academy of Sciences, Královopolská 147, 612 64, Brno, Czech Republic
| | - Markéta Benešová
- Institute of Scientific Instruments, Czech Academy of Sciences, Královopolská 147, 612 64, Brno, Czech Republic
| | - Ota Samek
- Institute of Scientific Instruments, Czech Academy of Sciences, Královopolská 147, 612 64, Brno, Czech Republic
| | - Andreas Limbeck
- TU Wien, Institute of Chemical Technologies and Analytics, Getreidemarkt 9/164-I(2)AC, 1060, Vienna, Austria
| | - Jozef Kaiser
- Central European Institute of Technology, Brno University of Technology, Purkyňova 656/123, 61200, Brno, Czech Republic; Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 61669, Brno, Czech Republic
| | - Gabriela Kalčíková
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna Pot 113, 1000, Ljubljana, Slovenia.
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19
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Bougueroua S, Aboulfath Y, Barth D, Gaigeot MP. Algorithmic graph theory for post-processing molecular dynamics trajectories. Mol Phys 2023. [DOI: 10.1080/00268976.2022.2162456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- Sana Bougueroua
- Université Paris-Saclay, Univ Evry, CNRS, LAMBE UMR8587, Evry-Courcouronnes, France
| | - Ylène Aboulfath
- Université Paris-Saclay, Univ Versailles SQ, DAVID, Versailles, France
| | - Dominique Barth
- Université Paris-Saclay, Univ Versailles SQ, DAVID, Versailles, France
| | - Marie-Pierre Gaigeot
- Université Paris-Saclay, Univ Evry, CNRS, LAMBE UMR8587, Evry-Courcouronnes, France
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20
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Zhang Y, Zhang D, Zhang Z. A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1150. [PMID: 36673905 PMCID: PMC9859244 DOI: 10.3390/ijerph20021150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/25/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelligence-based microplastic-imaging field from the Web of Science (2019-2022). By text mining and visualization in the scientific literature we emphasized some opportunities to bring forward further explication and analysis by (i) exploring efficient and low-cost automatic quantification methods in the appearance properties of microplastics, such as shape, size, volume, and topology, (ii) investigating microplastics water-soluble synthetic polymers and interaction with other soil and water ecology environments via artificial intelligence technologies, (iii) advancing efficient artificial intelligence algorithms and models, even including intelligent robot technology, (iv) seeking to create and share robust data sets, such as spectral libraries and toxicity database and co-operation mechanism, (v) optimizing the existing deep learning models based on the readily available data set to balance the related algorithm performance and interpretability, (vi) facilitating Unmanned Aerial Vehicle technology coupled with artificial intelligence technologies and data sets in the mass quantities of microplastics. Our major findings were that the research of artificial intelligence methods to revolutionize environmental science was progressing toward multiple cross-cutting areas, dramatically increasing aspects of the ecology of plastisphere, microplastics toxicity, rapid identification, and volume assessment of microplastics. The above findings can not only determine the characteristics and track of scientific development, but also help to find suitable research opportunities to carry out more in-depth research with many problems remaining.
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Affiliation(s)
- Yan Zhang
- School of Materials and Environmental Engineering, Fujian Polytechnic Normal University, Fuzhou 350300, China
| | - Dan Zhang
- School of Big Data and Artificial Intelligence, Fujian Polytechnic Normal University, Fuzhou 350300, China
- Fujian Provincial Key Laboratory of Coastal Basin Environment, Fujian Polytechnic Normal University, Fuzhou 350300, China
| | - Zhenchang Zhang
- College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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21
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Adarsh UK, Bhoje Gowd E, Bankapur A, Kartha VB, Chidangil S, Unnikrishnan VK. Development of an inter-confirmatory plastic characterization system using spectroscopic techniques for waste management. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 150:339-351. [PMID: 35907331 DOI: 10.1016/j.wasman.2022.07.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 07/13/2022] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
Ever-accumulating amounts of plastic waste raises alarming concern over environmental and public health. A practical solution for addressing this threat is recycling, and the success of an industry-oriented plastic recycling system relies greatly on the accuracy of the waste sorting technique adapted. We propose a multi-modal spectroscopic sensor which combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy in a single optical platform for characterizing plastics based on elemental and molecular information to assist the plastic identification-sorting process in recycling industries. The unique geometry of the system makes it compact and cost-effective for dual spectroscopy. The performance of the system in classifying industrially important plastic classes counting PP, PC, PLA, Nylon-1 1, and PMMA is evaluated, followed by the application of the same in real-world plastics comprising PET, HDPE, and PP in different chemical-physical conditions where the system consumes less than 30 ms for acquiring LIBS-Raman signals. The evaluation of the system in characterizing commuting samples shows promising results to be applied in industrial conditions in future. The study on effect of physical-chemical conditions of plastic wastes in characterizing them using the system shows the necessity for combining multiple techniques together. The proposal is not to determine the paramount methodology to characterize and sort plastics, but to demonstrate the advantages of dual-spectroscopy sensors in such applications. The outcomes of the study suggest that the system developed herein has the potential of emerging as an industrial-level plastic waste sorting sensor.
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Affiliation(s)
- U K Adarsh
- Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - E Bhoje Gowd
- Material Sciences and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram 695 019, Kerala, India
| | - Aseefhali Bankapur
- Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India; Centre of Excellence for Biophotonics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - V B Kartha
- Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India; Centre of Excellence for Biophotonics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Santhosh Chidangil
- Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India; Centre of Excellence for Biophotonics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - V K Unnikrishnan
- Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India; Centre of Excellence for Biophotonics, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.
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22
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Kroell N, Chen X, Greiff K, Feil A. Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 149:259-290. [PMID: 35760014 DOI: 10.1016/j.wasman.2022.05.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/17/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could enable new sensor applications such as adaptive plant control, improved sensor-based sorting (SBS), and more far-reaching data utilizations along the value chain. This review aims to expedite research on SBMC by (i) providing a comprehensive overview of existing SBMC publications, (ii) summarizing existing SBMC methods, and (iii) identifying future research potentials in SBMC. By conducting a systematic literature search covering the period 2000 - 2021, we identified 198 peer-reviewed journal articles on SBMC applications based on optical sensors and machine learning algorithms for dry-mechanical recycling of non-hazardous waste. The review shows that SBMC has received increasing attention in recent years, with more than half of the reviewed publications published between 2019 and 2021. While applications were initially focused solely on SBS, the last decade has seen a trend toward new applications, including sensor-based material flow monitoring, quality control, and process monitoring/control. However, SBMC at the material flow and process level remains largely unexplored, and significant potential exists in upscaling investigations from laboratory to plant scale. Future research will benefit from a broader application of deep learning methods, increased use of low-cost sensors and new sensor technologies, and the use of data streams from existing SBS equipment. These advancements could significantly improve the performance of future-generation sorting and processing plants, keep more materials in closed loops, and help paving the way towards circular economy.
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Affiliation(s)
- Nils Kroell
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany.
| | - Xiaozheng Chen
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany
| | - Kathrin Greiff
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany
| | - Alexander Feil
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany
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23
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Qin Y, Dong L, Lu H, Zhan L, Xu Z. Debromination process of Br-containing PS of E-wastes and reuse with virgin PS. JOURNAL OF HAZARDOUS MATERIALS 2022; 431:128526. [PMID: 35217346 DOI: 10.1016/j.jhazmat.2022.128526] [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: 01/02/2022] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 06/14/2023]
Abstract
High Br-containing polystyrene (PS) plastics are generated in large quantities during the dismantling of waste CRT TVs. Debromination and reuse of Br-containing PS plastics is a critical technical challenge. Here, we demonstrate a method for combining alkaline hydrothermal debromination and co-blending granulation to achieve the regeneration of high Br-containing PS plastics. The results show the bromine concentration in PS was reduced from 49,300 mg/kg to 7420 mg/kg and from 169,000 mg/kg to 9340 mg/kg, with a removal efficiency of 84.95% at least. Then, we co-blended the debromination PS products (1 part) with qualified normal PS plastics (9 parts) for granulation. Compared to the qualified normal PS, the physical properties of the co-blended plastics remained stable in terms of the melt index, tensile strength, flexural strength andflexural modulus, which made it have good application prospects. Meanwhile, the Br concentration of co-blended PS plastics were further reduced to less than 1000 mg/kg. In summary, we provide a promising outlook of alkaline hydrothermal and co-blending (1 +9) granulation as an efficient approach for Br-containing PS plastics upgrading recycling. NOVELTY STATEMENT: This study provides a novel method for combining alkaline hydrothermal treatment and co-blending modification granulation process to achieve the upgrading recycling of Br-containing waste plastics. The results show the Br concentration in PS was reduced from 169,013 ppm to 9344 ppm, with a removal efficiency of 94.47%. The debromination PS products (1 part) were blended with qualified normal PS plastics (9 parts) for granulation. Compared to the qualified normal PS, the physical properties of the co-blended plastics remained basically stable, which made it have good application prospects. Also, the reuse of waste plastic can make contribution for the carbon reduction.
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Affiliation(s)
- Yufei Qin
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Lipeng Dong
- Jiangxi Green Recycling Co., Ltd., Fengcheng 331100, Jiangxi, China
| | - Huaixing Lu
- Jiangxi Green Recycling Co., Ltd., Fengcheng 331100, Jiangxi, China
| | - Lu Zhan
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
| | - Zhenming Xu
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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24
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An Intelligent Deep Learning Model for Adsorption Prediction. ADSORPT SCI TECHNOL 2022. [DOI: 10.1155/2022/8136302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In this paper, we propose a supervised deep learning neural network (D-CNN) approach to predict CO2 adsorption form the textural and compositional features of biomass porous carbon waste and adsorption features. Both the textural and compositional features of biomass porous carbon waste are utilized as inputs for the D-CNN architecture. A deep learning neural network (D-CNN) is proposed to predict the adsorption rate of
on zeolites. The adsorbed amount will be classified and predicted by the D-CNN. Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. A feature importance metric was proposed using feature permutation, and the effect of each feature on the target output variable was investigated. The important extracted features from the three employed model were fused and used as the fusion feature set in our proposed model: fusion matrix deep learning model (FMDL). A dataset of 1400 data items, on adsorbent type and various adsorption pressure, is used as inputs for the D-CNN model. Comparison of the proposed model is done against the three tree models, which utilizes a single training layer. The error measure of the D-CNN and the tree model architectures utilize the mean square error confirming the efficiency of 0.00003 for our model, 0.00062 for the SBT, 0.00091 for the GDM, and 0.00098 for the GVD, after 150 epochs. The produced weight matrix was able to predict the
adsorption under diverse process settings with high accuracy of 96.4%.
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Angeyo H, Gari S. Direct rapid quality assurance analysis of complex matrix materials: A chemometrics enabled energy dispersive X-ray fluorescence and scattering spectrometry application. Appl Radiat Isot 2022; 186:110274. [DOI: 10.1016/j.apradiso.2022.110274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/13/2022] [Accepted: 05/03/2022] [Indexed: 11/28/2022]
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Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy. Sci Rep 2022; 12:5017. [PMID: 35322097 PMCID: PMC8943100 DOI: 10.1038/s41598-022-08862-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/15/2022] [Indexed: 12/23/2022] Open
Abstract
Non-destructive spectroscopic analysis combined with machine learning rapidly provides information on the identity and content of plasticizers in PVC objects of heritage value. For the first time, a large and diverse collection of more than 100 PVC objects in different degradation stages and of diverse chemical compositions was analysed by chromatographic and spectroscopic techniques to create a dataset used to construct classification and regression models. Accounting for this variety makes the model more robust and reliable for the analysis of objects in museum collections. Six different machine learning classification algorithms were compared to determine the algorithm with the highest classification accuracy of the most common plasticizers, based solely on the spectroscopic data. A classification model capable of the identification of di(2-ethylhexyl) phthalate, di(2-ethylhexyl) terephthalate, diisononyl phthalate, diisodecyl phthalate, a mixture of diisononyl phthalate and diisodecyl phthalate, and unplasticized PVC was constructed. Additionally, regression models for quantification of di(2-ethylhexyl) phthalate and di(2-ethylhexyl) terephthalate in PVC were built. This study of real-life objects demonstrates that classification and quantification of plasticizers in a general collection of degraded PVC objects is possible, providing valuable data to collection managers.
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The identification of microplastics based on vibrational spectroscopy data – a critical review of data analysis routines. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Jiang S, Xu Z, Kamran M, Zinchik S, Paheding S, McDonald AG, Bar-Ziv E, Zavala VM. Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107547] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Huang R, Ma C, Ma J, Huangfu X, He Q. Machine learning in natural and engineered water systems. WATER RESEARCH 2021; 205:117666. [PMID: 34560616 DOI: 10.1016/j.watres.2021.117666] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/01/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.
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Affiliation(s)
- Ruixing Huang
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Chengxue Ma
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China; State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Jun Ma
- State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Xiaoliu Huangfu
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China.
| | - Qiang He
- Key Laboratory of Eco-environments in the Three Gorges Reservoir Region, Ministry of Education, College of Environmental and Ecology, Chongqing University, Chongqing 400044, China
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Turner A, Filella M. Polyvinyl chloride in consumer and environmental plastics, with a particular focus on metal-based additives. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2021; 23:1376-1384. [PMID: 34368828 DOI: 10.1039/d1em00213a] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Polyvinyl chloride (PVC) is one of the most widely used thermoplastics but is also a material of concern because of the generation and release of harmful chemicals during its life cycle. Amongst the chemicals added to PVC are metal-based stabilisers and Sb-based halogenated flame retardant synergists. However, very little quantitative information exists on these additives, and in particular in PVC lost to the environment. In this study, the distribution of PVC amongst consumer plastics in societal circulation and plastics retrieved from marine and lacustrine beaches and agricultural soils are compared, along with the presence and concentrations of Ba, Cd, Pb, Sb, Sn and Zn as proxies for common metal-based additives and determined by X-ray fluorescence spectrometry. About 10% of consumer plastics and 2% of environmental plastics were constructed of PVC, with the discrepancy attributed to the long service lives and managed disposal of PVC used in the construction sector and the propensity of the plastic to sink in aquatic systems and evade detection. Metal-based additives, defined as having a metal concentration >1000 mg kg-1, were present in about 75% of consumer and environmental PVC, with Ba and Pb most abundant and Cd and Zn least abundant in both types of sample, and median concentrations statistically different only for Ba. Metals also appeared to be present as contaminants (defined as concentrations <1000 mg kg-1) arising from manufacturing or recycling. Metals in PVC are believed to pose little risk when the material is in use, but experimental evidence in the literature suggests that significant mobilisation and exposure may occur from PVC microplastics when ingested by wildlife.
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Affiliation(s)
- Andrew Turner
- School of Geography, Earth and Environmental Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK.
| | - Montserrat Filella
- Department F.-A. Forel, University of Geneva, Boulevard Carl-Vogt 66, CH-1205 Geneva, Switzerland.
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Vidal C, Pasquini C. A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 285:117251. [PMID: 33957518 DOI: 10.1016/j.envpol.2021.117251] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 06/12/2023]
Abstract
Microplastic pollution is a global concern theme, and there is still the need for less laborious and faster analytical methods aiming at microplastics detection. This article describes a high throughput screening method based on near-infrared hyperspectral imaging (HSI-NIR) to identify microplastics in beach sand automatically with minimum sample preparation. The method operates directly in the entire sample or on its retained fraction (150 μm-5 mm) after sieving. Small colorless microplastics (<600 μm) that would probably be imperceptible as a microplastic by visual inspection, or missed during manual pick up, can be easily detected. No spectroscopic subsampling was performed due to the high-speed analysis of line-scan instrumentation, allowing multiple microplastics to be assessed simultaneously (video available). This characteristic is an advantage over conventional infrared (IR) spectrometers. A 75 cm2 scan area was probed in less than 1 min at a pixel size of 156 × 156 μm. An in-house comprehensive spectral dataset, including weathered microplastics, was used to build multivariate supervised soft independent modelling of class analogy (SIMCA) classification models. The chemometric models were validated for hundreds of microplastics (primary and secondary) collected in the environment. The effect of particle size, color and weathering are discussed. Models' sensitivity and specificity for polyethylene (PE), polypropylene (PP), polyamide-6 (PA), polyethylene terephthalate (PET) and polystyrene (PS) were over 99% at the defined statistical threshold. The method was applied to a sand sample, identifying 803 particles without prior visual sorting, showing automatic identification was robust and reliable even for weathered microplastics analyzed together with other matrix constituents. The HSI-NIR-SIMCA described is also applicable for microplastics extracted from other matrices after sample preparation. The HSI-NIR principals were compared to other common techniques used to microplastic chemical characterization. The results show the potential to use HSI-NIR combined with classification models as a comprehensive microplastic-type characterization screening.
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Affiliation(s)
- Cristiane Vidal
- Department of Analytical Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), PO BOX 6154, CEP 13083-970, Campinas, SP, Brazil.
| | - Celio Pasquini
- Department of Analytical Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), PO BOX 6154, CEP 13083-970, Campinas, SP, Brazil.
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Yuan X, Suvarna M, Low S, Dissanayake PD, Lee KB, Li J, Wang X, Ok YS. Applied Machine Learning for Prediction of CO 2 Adsorption on Biomass Waste-Derived Porous Carbons. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:11925-11936. [PMID: 34291911 DOI: 10.1021/acs.est.1c01849] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Biomass waste-derived porous carbons (BWDPCs) are a class of complex materials that are widely used in sustainable waste management and carbon capture. However, their diverse textural properties, the presence of various functional groups, and the varied temperatures and pressures to which they are subjected during CO2 adsorption make it challenging to understand the underlying mechanism of CO2 adsorption. Here, we compiled a data set including 527 data points collected from peer-reviewed publications and applied machine learning to systematically map CO2 adsorption as a function of the textural and compositional properties of BWDPCs and adsorption parameters. Various tree-based models were devised, where the gradient boosting decision trees (GBDTs) had the best predictive performance with R2 of 0.98 and 0.84 on the training and test data, respectively. Further, the BWDPCs in the compiled data set were classified into regular porous carbons (RPCs) and heteroatom-doped porous carbons (HDPCs), where again the GBDT model had R2 of 0.99 and 0.98 on the training and 0.86 and 0.79 on the test data for the RPCs and HDPCs, respectively. Feature importance revealed the significance of adsorption parameters, textural properties, and compositional properties in the order of precedence for BWDPC-based CO2 adsorption, effectively guiding the synthesis of porous carbons for CO2 adsorption applications.
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Affiliation(s)
- Xiangzhou Yuan
- Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
- R&D Centre, Sun Brand Industrial Inc., Jeollanam-do 57248, Republic of Korea
| | - Manu Suvarna
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Sean Low
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Pavani Dulanja Dissanayake
- Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Ki Bong Lee
- Department of Chemical & Biological Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Jie Li
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Xiaonan Wang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore
| | - Yong Sik Ok
- Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
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Enders AA, North NM, Fensore CM, Velez-Alvarez J, Allen HC. Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models. Anal Chem 2021; 93:9711-9718. [PMID: 34190551 DOI: 10.1021/acs.analchem.1c00867] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Fourier transform infrared spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using convolutional neural networks (CNNs) to identify the presence of functional groups in gas-phase FTIR spectra. The ML models reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas-phase organic molecules within the NIST spectral database and transform the data into spectral images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that infer in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.
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Affiliation(s)
- Abigail A Enders
- Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Nicole M North
- Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Chase M Fensore
- Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Juan Velez-Alvarez
- Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Heather C Allen
- Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
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Blevins MG, Allen HL, Colson BC, Cook AM, Greenbaum AZ, Hemami SS, Hollmann J, Kim E, LaRocca AA, Markoski KA, Miraglia P, Mott VL, Robberson WM, Santos JA, Sprachman MM, Swierk P, Tate S, Witinski MF, Kratchman LB, Michel APM. Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques. SENSORS (BASEL, SWITZERLAND) 2021; 21:3532. [PMID: 34069517 PMCID: PMC8160859 DOI: 10.3390/s21103532] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 11/28/2022]
Abstract
Microplastics (MPs) have been found in aqueous environments ranging from rural ponds and lakes to the deep ocean. Despite the ubiquity of MPs, our ability to characterize MPs in the environment is limited by the lack of technologies for rapidly and accurately identifying and quantifying MPs. Although standards exist for MP sample collection and preparation, methods of MP analysis vary considerably and produce data with a broad range of data content and quality. The need for extensive analysis-specific sample preparation in current technology approaches has hindered the emergence of a single technique which can operate on aqueous samples in the field, rather than on dried laboratory preparations. In this perspective, we consider MP measurement technologies with a focus on both their eventual field-deployability and their respective data products (e.g., MP particle count, size, and/or polymer type). We present preliminary demonstrations of several prospective MP measurement techniques, with an eye towards developing a solution or solutions that can transition from the laboratory to the field. Specifically, experimental results are presented from multiple prototype systems that measure various physical properties of MPs: pyrolysis-differential mobility spectroscopy, short-wave infrared imaging, aqueous Nile Red labeling and counting, acoustophoresis, ultrasound, impedance spectroscopy, and dielectrophoresis.
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Affiliation(s)
- Morgan G. Blevins
- MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering, Cambridge and Woods Hole, MA 02543, USA; (M.G.B.); (B.C.C.)
- Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Harry L. Allen
- Emergency Response Office, Superfund Division, U.S. EPA Region 9, San Francisco, CA 94105, USA;
| | - Beckett C. Colson
- MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering, Cambridge and Woods Hole, MA 02543, USA; (M.G.B.); (B.C.C.)
- Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Anna-Marie Cook
- Kamilo, Inc., Former U.S. EPA Region 9, San Francisco, CA 94108, USA;
| | - Alexandra Z. Greenbaum
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Sheila S. Hemami
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA;
| | - Joseph Hollmann
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Ernest Kim
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Ava A. LaRocca
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Kenneth A. Markoski
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Peter Miraglia
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Vienna L. Mott
- Draper, Bioengineering Division, Cambridge, MA 02139, USA;
| | | | - Jose A. Santos
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Melissa M. Sprachman
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Patricia Swierk
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Steven Tate
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Mark F. Witinski
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Louis B. Kratchman
- The Charles Stark Draper Laboratory Inc., Cambridge, MA 02139, USA; (A.Z.G.); (J.H.); (E.K.); (A.A.L.); (K.A.M.); (P.M.); (J.A.S.); (M.M.S.); (P.S.); (S.T.); (M.F.W.)
| | - Anna P. M. Michel
- Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
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White HK, Morrison AE, Dhoonmoon C, Caballero-Gomez H, Luu M, Samuels C, Marx CT, Michel APM. Identification of persistent oil residues in Prince William Sound, Alaska using rapid spectroscopic techniques. MARINE POLLUTION BULLETIN 2020; 161:111718. [PMID: 33038711 DOI: 10.1016/j.marpolbul.2020.111718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/21/2020] [Accepted: 09/22/2020] [Indexed: 06/11/2023]
Abstract
Spectroscopic techniques including X-ray fluorescence (XRF) and attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) are used to examine oil residues persisting on shorelines in Prince William Sound that originate from the 1989 Exxon Valdez oil spill and oil released as a consequence of the 1964 Great Alaska earthquake. When coupled to classification models, ATR-FTIR and XRF spectral data can be used to distinguish between the two sources of oil with 92% and 86% success rates for the two techniques respectively. Models indicate that the ATR-FTIR data used to determine oil source includes the CO stretch, the twisting-scissoring of the CH2 group, and the CC stretch. For XRF data, decision tree models primarily utilize the abundance of nickel and zinc present in the oil as a means to classify source. This approach highlights the utility of rapid, field-based spectroscopic techniques to distinguish different inputs of oil to coastal environments.
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Affiliation(s)
- Helen K White
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA.
| | - Alexandra E Morrison
- Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, 266 Woods Hole Rd, Woods Hole, MA 02543, USA
| | - Charvanaa Dhoonmoon
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA
| | - Hasibe Caballero-Gomez
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA
| | - Michelle Luu
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA
| | - Camille Samuels
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA
| | - Charles T Marx
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA
| | - Anna P M Michel
- Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, 266 Woods Hole Rd, Woods Hole, MA 02543, USA
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