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Bhaumik S, Chakraborty P. Interactions between microplastics (MPs) and trace/toxic metals in marine environments: implications and insights-a comprehensive review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-34960-w. [PMID: 39365535 DOI: 10.1007/s11356-024-34960-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 09/07/2024] [Indexed: 10/05/2024]
Abstract
Microplastics (MP) pollution is a pressing concern in today's marine environments. MPs can significantly affect marine ecosystems by altering nutrient and pollutant dynamics. This review analyses the existing literature to investigate interactions between MPs and micronutrients/pollutants, specifically trace and toxic metals in marine environments. It explores the adsorption of metals onto MP surfaces, emphasizing kinetics, isotherms, and underlying mechanisms of the process. The review highlights the potential consequences of MPs on the biogeochemical cycles of trace and toxic metals, emphasizing disruptions that could result in metal toxicity, metal limitations, reduced bioavailability, and adverse effects on primary productivity in marine ecosystems. It further underscores the need for future research to unravel the wide-ranging implications of MPs on trace and toxic metal cycling in marine ecosystems and their broader environmental impacts.
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Affiliation(s)
- Swastika Bhaumik
- Marine Trace Metal Biogeochemistry Laboratory, Centre for Ocean, River, Atmosphere and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India
| | - Parthasarathi Chakraborty
- Marine Trace Metal Biogeochemistry Laboratory, Centre for Ocean, River, Atmosphere and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
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Zhang D, Jiang J, Shi H, Lu L, Zhang M, Lin J, Lü T, Huang J, Zhong Z, Zhao H. Nonionic surfactant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic concentration-dependent performance, mechanism, and machine learning prediction. ENVIRONMENTAL RESEARCH 2024; 251:118670. [PMID: 38493849 DOI: 10.1016/j.envres.2024.118670] [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: 12/26/2023] [Revised: 03/06/2024] [Accepted: 03/09/2024] [Indexed: 03/19/2024]
Abstract
The surfactant-enhanced bioremediation (SEBR) of organic-contaminated soil is a promising soil remediation technology, in which surfactants not only mobilize pollutants, but also alter the mobility of bacteria. However, the bacterial response and underlying mechanisms remain unclear. In this study, the effects and mechanisms of action of a selected nonionic surfactant (Tween 80) on Pseudomonas aeruginosa transport in soil and quartz sand were investigated. The results showed that bacterial migration in both quartz sand and soil was significantly enhanced with increasing Tween 80 concentration, and the greatest migration occurred at a critical micelle concentration (CMC) of 4 for quartz sand and 30 for soil, with increases of 185.2% and 27.3%, respectively. The experimental results and theoretical analysis indicated that Tween 80-facilitated bacterial migration could be mainly attributed to competition for soil/sand surface sorption sites between Tween 80 and bacteria. The prior sorption of Tween 80 onto sand/soil could diminish the available sorption sites for P. aeruginosa, resulting in significant decreases in deposition parameters (70.8% and 33.3% decrease in KD in sand and soil systems, respectively), thereby increasing bacterial transport. In the bacterial post-sorption scenario, the subsequent injection of Tween 80 washed out 69.8% of the bacteria retained in the quartz sand owing to the competition of Tween 80 with pre-sorbed bacteria, as compared with almost no bacteria being eluted by NaCl solution. Several machine learning models have been employed to predict Tween 80-faciliated bacterial transport. The results showed that back-propagation neural network (BPNN)-based machine learning could predict the transport of P. aeruginosa through quartz sand with Tween 80 in-sample (2 CMC) and out-of-sample (10 CMC) with errors of 0.79% and 3.77%, respectively. This study sheds light on the full understanding of SEBR from the viewpoint of degrader facilitation.
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Affiliation(s)
- Dong Zhang
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jiacheng Jiang
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Huading Shi
- Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China.
| | - Li Lu
- Zhejiang Provincial Key Laboratory of Solid Waste Treatment and Recycling, School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, 310018, Zhejiang, China
| | - Ming Zhang
- Department of Environmental Science and Engineering, China Jiliang University, Hangzhou, 310018, Zhejiang, China
| | - Jun Lin
- Institute of Carbon Neutrality and New Energy, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Ting Lü
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jingang Huang
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Zhishun Zhong
- Guangdong Jiandi Agriculture Technology Co. Ltd., Foshan, Guangdong, 528200, China
| | - Hongting Zhao
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
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Ma Y, Qiang T, Gao M, Liang J, Jiang Y. Quantitative, Temperature-Calibrated and Real-Time Glucose Biosensor Based on Symmetrical-Meandering-Type Resistor and Intertwined Capacitor Structure. BIOSENSORS 2021; 11:484. [PMID: 34940241 PMCID: PMC8699495 DOI: 10.3390/bios11120484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 11/17/2022]
Abstract
Here, we propose a glucose biosensor with the advantages of quantification, excellent linearity, temperature-calibration function, and real-time detection based on a resistor and capacitor, in which the resistor works as a temperature sensor and the capacitor works as a biosensor. The resistor has a symmetrical meandering type structure that increases the contact area, leading to variations in resistance and effective temperature monitoring of a glucose solution. The capacitor is designed with an intertwined structure that fully contacts the glucose solution, so that capacitance is sensitively varied, and high sensitivity monitoring can be realized. Moreover, a polydimethylsiloxane microfluidic channel is applied to achieve a fixed shape, a fixed point, and quantitative measurements, which can eliminate influences caused by fluidity, shape, and thickness of the glucose sample. The glucose solution in a temperature range of 25-100 °C is measured with variations of 0.2716 Ω/°C and a linearity response of 0.9993, ensuring that the capacitor sensor can have reference temperature information before detecting the glucose concentration, achieving the purpose of temperature calibration. The proposed capacitor-based biosensor demonstrates sensitivities of 0.413 nF/mg·dL-1, 0.048 nF/mg·dL-1, and 0.011 pF/mg·dL-1; linearity responses of 0.96039, 0.91547, and 0.97835; and response times less than 1 second, respectively, at DC, 1 kHz, and 1 MHz for a glucose solution with a concentration range of 25-1000 mg/dL.
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Affiliation(s)
| | - Tian Qiang
- Department of Electronic Engineering, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; (Y.M.); (M.G.); (J.L.); (Y.J.)
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