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Huang R, Liu T, Peng H, Liu J, Liu X, Ding L, Fang Y. Molecular design and architectonics towards film-based fluorescent sensing. Chem Soc Rev 2024; 53:6960-6991. [PMID: 38836431 DOI: 10.1039/d4cs00347k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The past few decades have witnessed encouraging progress in the development of high-performance film-based fluorescent sensors (FFSs) for detecting explosives, illicit drugs, chemical warfare agents (CWAs), and hazardous volatile organic chemicals (VOCs), among others. Several FFSs have transitioned from laboratory research to real-world applications, demonstrating their practical relevance. At the heart of FFS technology lies the sensing films, which play a crucial role in determining the analytes and the resulting signals. The selection of sensing fluorophores and the fabrication strategies employed in film construction are key factors that influence the fluorescence properties, active-layer structures, and overall sensing behaviors of these films. This review examines the progress and innovations in the research field of FFSs over the past two decades, focusing on advancements in fluorophore design and active-layer structural engineering. It underscores popular sensing fluorophore scaffolds and the dynamics of excited state processes. Additionally, it delves into six distinct categories of film fabrication technologies and strategies, providing insights into their advantages and limitations. This review further addresses important considerations such as photostability and substrate effects. Concluding with an overview of the field's challenges and prospects, it sheds light on the potential for further development in this burgeoning area.
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Affiliation(s)
- Rongrong Huang
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, West Chang'an Street, Xi'an, Shaanxi 710062, P. R. China.
- Fluorescence Research Group, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Taihong Liu
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, West Chang'an Street, Xi'an, Shaanxi 710062, P. R. China.
| | - Haonan Peng
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, West Chang'an Street, Xi'an, Shaanxi 710062, P. R. China.
| | - Jing Liu
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, West Chang'an Street, Xi'an, Shaanxi 710062, P. R. China.
| | - Xiaogang Liu
- Fluorescence Research Group, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Liping Ding
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, West Chang'an Street, Xi'an, Shaanxi 710062, P. R. China.
| | - Yu Fang
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, West Chang'an Street, Xi'an, Shaanxi 710062, P. R. China.
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2
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Hu J, Chen GJ, Xue C, Liang P, Xiang Y, Zhang C, Chi X, Liu G, Ye Y, Cui D, Zhang D, Yu X, Dang H, Zhang W, Chen J, Tang Q, Guo P, Ho HP, Li Y, Cong L, Shum PP. RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization. LIGHT, SCIENCE & APPLICATIONS 2024; 13:52. [PMID: 38374161 PMCID: PMC10876988 DOI: 10.1038/s41377-024-01394-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/02/2024] [Accepted: 01/23/2024] [Indexed: 02/21/2024]
Abstract
Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.
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Affiliation(s)
- Jiaqi Hu
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Gina Jinna Chen
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Chenlong Xue
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Yanqun Xiang
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Chuanlun Zhang
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xiaokeng Chi
- Department of Nephrology, Chaozhou People's Hospital, Chaozhou, 521011, China
| | - Guoying Liu
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Yanfang Ye
- Clinical Research Design Division, Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, 510120, China
| | - Dongyu Cui
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - De Zhang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Xiaojun Yu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China
| | - Hong Dang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Wen Zhang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Junfan Chen
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Quan Tang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Penglai Guo
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Ho-Pui Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Yuchao Li
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Longqing Cong
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Perry Ping Shum
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China.
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Nam NN, Do HDK, Trinh KTL, Lee NY. Recent Progress in Nanotechnology-Based Approaches for Food Monitoring. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:nano12234116. [PMID: 36500739 PMCID: PMC9740597 DOI: 10.3390/nano12234116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/17/2022] [Accepted: 11/20/2022] [Indexed: 05/10/2023]
Abstract
Throughout the food supply chain, including production, storage, and distribution, food can be contaminated by harmful chemicals and microorganisms, resulting in a severe threat to human health. In recent years, the rapid advancement and development of nanotechnology proposed revolutionary solutions to solve several problems in scientific and industrial areas, including food monitoring. Nanotechnology can be incorporated into chemical and biological sensors to improve analytical performance, such as response time, sensitivity, selectivity, reliability, and accuracy. Based on the characteristics of the contaminants and the detection methods, nanotechnology can be applied in different ways in order to improve conventional techniques. Nanomaterials such as nanoparticles, nanorods, nanosheets, nanocomposites, nanotubes, and nanowires provide various functions for the immobilization and labeling of contaminants in electrochemical and optical detection. This review summarizes the recent advances in nanotechnology for detecting chemical and biological contaminations in the food supply chain.
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Affiliation(s)
- Nguyen Nhat Nam
- Biotechnology Center, School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh City 87000, Vietnam
| | - Hoang Dang Khoa Do
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ward 13, District 04, Ho Chi Minh City 70000, Vietnam
| | - Kieu The Loan Trinh
- Department of Industrial Environmental Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea
- Correspondence: (K.T.L.T.); (N.Y.L.)
| | - Nae Yoon Lee
- Department of BioNano Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea
- Correspondence: (K.T.L.T.); (N.Y.L.)
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Liu B, Liu K, Wang N, Ta K, Liang P, Yin H, Li B. Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria. Talanta 2022; 244:123383. [DOI: 10.1016/j.talanta.2022.123383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 03/05/2022] [Accepted: 03/11/2022] [Indexed: 10/18/2022]
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A Quantum Weak Signal Detection Method for Strengthening Target Signal Features under Strong White Gaussian Noise. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
As the noise power increases, the target signal features become less obvious, which leads to the failure of weak signal detection methods. To address this problem, a quantum weak signal detection method, Local Semi-Classical Signal Analysis-Singular Value Decomposition (LSCSA-SVD), for strengthening target signal features under strong white Gaussian noise is proposed. Firstly, the time domain weak signal is quantized by the Schrodinger operator and its discrete spectrum formula. Then, in the quantum domain, the later eigenvalues are used to reconstruct the time domain signal, which can protect and enhance the target signal features. Finally, the difference between signal and noise in the singular value vector is used to further extract the reconstruction signal features. In simulation, the LSCSA-SVD can accurately extract target signals from white Gaussian noise signals with a signal-to-noise ratio (SNR) of −30 dB, which is better than the comparison methods. In the experiment, the weak acceleration sensor signal and the weak signal of the test circuit are successfully extracted. The results show that the LSCSA-SVD can suppress strong noise and improve the SNR.
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Xu S, Deng X, Ji S, Chen L, Zhao T, Luo F, Qiu B, Lin Z, Guo L. An algorithm-assisted automated identification and enumeration system for sensitive hydrogen sulfide sensing under dark field microscopy. Analyst 2022; 147:1492-1498. [DOI: 10.1039/d2an00149g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A sensitive H2S sensing strategy has been developed based on the automated identification and enumeration algorithm.
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Affiliation(s)
- Shaohua Xu
- Jiangxi Engineering Research Centre for Translational Cancer Technology, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, 330004, China
- Jiaxing Key Laboratory of Molecular Recognition and Sensing; College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing 314001, China
| | - Xiaoyu Deng
- Ministry of Education Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, 330004, China
| | - Shuyi Ji
- Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350116, China
| | - Lifen Chen
- Jiaxing Key Laboratory of Molecular Recognition and Sensing; College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing 314001, China
| | - Tiesong Zhao
- Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350116, China
| | - Fang Luo
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian, 350116, China
| | - Bin Qiu
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian, 350116, China
| | - Zhenyu Lin
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian, 350116, China
| | - Longhua Guo
- Jiaxing Key Laboratory of Molecular Recognition and Sensing; College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing 314001, China
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, Fujian, 350116, China
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Liu W, Jing C, Liu X, Du J. 3D imaging of single bacterial cells using surface-enhanced Raman spectroscopy with a multivariate curve resolution model. Analyst 2021; 147:223-229. [PMID: 34877945 DOI: 10.1039/d1an01879e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Imaging biomolecules within a single bacterial cell is crucial for understanding cellular genetic mechanisms. Herein, we exploited a surface-enhanced Raman spectroscopy (SERS) imaging strategy for single cell analysis. Cellular biosynthesized Ag nanoparticles (NPs) provided the necessary enhancement for SERS imaging. Multiple complementary techniques, including high-resolution transmission electron microscopy (HR-TEM), high-angle annular dark-field (HAADF)-scanning transmission electron microscopy (STEM), and energy-dispersive X-ray spectroscopy (EDX), were used to characterize the biogenic Ag NPs in cells. Three-dimensional SERS imaging maps displayed spectral information of biomolecules within the single cell. The multivariate curve resolution (MCR) model and principal component analysis (PCA) model were used to analyze the cellular SERS imaging maps. The MCR model, with a specific constraint of non-negativity, resulted in meaningful identification of biomolecules associated with Ag reduction. Focusing on the molecular level reveals that Pantoea sp. IMH utilizes several mechanisms to synthesize Ag NPs, including cytoplasm reduction by glucose or nicotinamide adenine dinucleotide (NADH)-dependent reductase, and extracellular reduction by an electron transfer chain containing quinone and cytochrome C. Our results shed new light on the Ag NP biosynthesis mechanism and single cell Raman analysis.
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Affiliation(s)
- Wenjing Liu
- Key Laboratory for Environmental Factors Control of Agro-product Quality Safety, Ministry of Agriculture and Rural Affairs, Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
| | | | - Xiaowei Liu
- Key Laboratory for Environmental Factors Control of Agro-product Quality Safety, Ministry of Agriculture and Rural Affairs, Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
| | - Jingjing Du
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
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Luo SH, Wang X, Chen GY, Xie Y, Zhang WH, Zhou ZF, Zhang ZM, Ren B, Liu GK, Tian ZQ. Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy. Anal Chem 2021; 93:8408-8413. [PMID: 34110787 DOI: 10.1021/acs.analchem.0c05391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.
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Affiliation(s)
- Si-Heng Luo
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.,State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Xin Wang
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, Fujian 361102, China
| | - Gan-Yu Chen
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Yi Xie
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Wen-Han Zhang
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Zhi-Fan Zhou
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhi-Min Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan 410083, China
| | - Bin Ren
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhong-Qun Tian
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
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Fang C, Sobhani Z, Zhang X, McCourt L, Routley B, Gibson CT, Naidu R. Identification and visualisation of microplastics / nanoplastics by Raman imaging (iii): algorithm to cross-check multi-images. WATER RESEARCH 2021; 194:116913. [PMID: 33601233 DOI: 10.1016/j.watres.2021.116913] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/12/2020] [Accepted: 02/04/2021] [Indexed: 06/12/2023]
Abstract
We recently developed the Raman mapping image to visualise and identify microplastics / nanoplastics (Fang et al. 2020, Sobhani et al. 2020). However, when the Raman signal is low and weak, the mapping uncertainty from the individual Raman peak intensity increases and may lead to images with false positive or negative features. For real samples, even the Raman signal is high, a low signal-noise ratio still occurs and leads to the mapping uncertainty due to the high spectrum background when: the target plastic is dispersed within another material with interfering Raman peaks; materials are present that exhibit broad Raman peaks; or, materials are present that fluoresce when exposed to the excitation laser. In this study, in order to increase the mapping certainty, we advance the algorithm to combine and merge multi-images that have been simultaneously mapped at the different characteristic peaks from the Raman spectra, akin imaging via different mapping channels simultaneously. These multi-images are merged into one image via algorithms, including colour off-setting to collect signal with a higher ratio of signal-noise, logic-OR to pick up more signal, logic-AND to eliminate noise, and logic-SUBTRACT to remove image background. Specifically, two or more Raman images can act as "parent images", to merge and generate a "daughter image" via a selected algorithm, to a "granddaughter image" via a further selected algorithm, and to an "offspring image" etc. More interestingly, to validate this algorithm approach, we analyse microplastics / nanoplastics that might be generated by a laser printer in our office or home. Depending on the toner and the printer, we might print and generate millions of microplastics and nanoplastics when we print a single A4 document.
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Affiliation(s)
- Cheng Fang
- Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan NSW 2308, Australia.
| | - Zahra Sobhani
- Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan NSW 2308, Australia
| | - Xian Zhang
- Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Luke McCourt
- School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Ben Routley
- School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Christopher T Gibson
- Flinders Institute for NanoScale Science and Technology, College of Science and Engineering, Flinders University, South Australia 5042, Australia; Flinders Microscopy and Microanalysis, College of Science and Engineering, Flinders University, Bedford Park 5042, Australia
| | - Ravi Naidu
- Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan NSW 2308, Australia
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