Microbial fuel cell soft sensor for real-time toxicity detection and monitoring

Adekunle, Ademola; Vidales, Abraham Gomez; Woodward, Lyne; Tartakovsky, B.

doi:10.1007/s11356-020-11245-6

Cited by 22 publications

(6 citation statements)

References 47 publications

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“…The removal of HMs from wastewater and soil has been the subject of extensive research utilizing MFC-based techniques [ 164 , 165 , 166 ]. Given the limited power output of MFCs, it is advisable to employ them primarily as sensors for detecting the toxicity of HMs [ 167 ].…”

Section: Bioremediation: Interaction Of Microorganisms With Hmsmentioning

confidence: 99%

Toxicity of Heavy Metals and Recent Advances in Their Removal: A Review

Abd Elnabi,

Elkaliny,

Elyazied

et al. 2023

Toxics

154

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Natural and anthropogenic sources of metals in the ecosystem are perpetually increasing; consequently, heavy metal (HM) accumulation has become a major environmental concern. Human exposure to HMs has increased dramatically due to the industrial activities of the 20th century. Mercury, arsenic lead, chrome, and cadmium have been the most prevalent HMs that have caused human toxicity. Poisonings can be acute or chronic following exposure via water, air, or food. The bioaccumulation of these HMs results in a variety of toxic effects on various tissues and organs. Comparing the mechanisms of action reveals that these metals induce toxicity via similar pathways, including the production of reactive oxygen species, the inactivation of enzymes, and oxidative stress. The conventional techniques employed for the elimination of HMs are deemed inadequate when the HM concentration is less than 100 mg/L. In addition, these methods exhibit certain limitations, including the production of secondary pollutants, a high demand for energy and chemicals, and reduced cost-effectiveness. As a result, the employment of microbial bioremediation for the purpose of HM detoxification has emerged as a viable solution, given that microorganisms, including fungi and bacteria, exhibit superior biosorption and bio-accumulation capabilities. This review deals with HM uptake and toxicity mechanisms associated with HMs, and will increase our knowledge on their toxic effects on the body organs, leading to better management of metal poisoning. This review aims to enhance comprehension and offer sources for the judicious selection of microbial remediation technology for the detoxification of HMs. Microbial-based solutions that are sustainable could potentially offer crucial and cost-effective methods for reducing the toxicity of HMs.

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Section: Bioremediation: Interaction Of Microorganisms With Hmsmentioning

confidence: 99%

Toxicity of Heavy Metals and Recent Advances in Their Removal: A Review

Abd Elnabi,

Elkaliny,

Elyazied

et al. 2023

Toxics

154

View full text Add to dashboard Cite

show abstract

“…Among them, the luminescent bacterial assay is a mature method . Other methods, such as colorimetric, bioelectrochemical, − and mediator-based microbial biosensors, − are also used to evaluate toxicity in water. These biosensors transform the biological response under conditions of toxic exposure into a measurable signal output, such as current, potential, and indicator color, to characterize the toxic effects of pollutants on organisms and visually evaluate the safety of water quality.…”

Section: Introductionmentioning

confidence: 99%

Label-Free Surface-Enhanced Raman Scattering-Based Microbial Whole-Cell Biosensor for Acute Toxicity Assessment in Water

Ma,

Liu,

Zhou

et al. 2024

ACS EST Water

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Aquatic toxicity assessment is extremely important to ensuring water safety. Typically, the evaluation was accomplished by introducing bioluminescent bacteria, where the weakened luminescence was used to calculate the acute toxicity. Herein, a new microbial wholecell biosensor was proposed for aquatic toxicity evaluation using a labelfree surface-enhanced Raman spectroscopy (SERS) technique with Escherichia coli (E. coli) as a model organism. The proposed strategy required neither special bacteria strains nor mediators. The dosedependent SERS signal of the exposed model bacteria can be directly acquired. For the model toxic chemical Cu 2+ , the half-inhibitory concentration (IC 50 ) was found to be 3.87 mg/L. The method also helps understand the mechanism of toxicity of bacterial cells after exposure to a toxic environment, providing a multiparameter view of the changes in bacterial physiological metabolism induced by toxicants.

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“…Authors are Presently, the world's overall economy is innately based mostly on the competent methods of electric power generation, relevant administration as well as , distribution. The classic solutions of energy development hold a considerable complication on the universal climate and climate transformations [1,2]. Corresponding to lately publicized information by the International Energy Agency (IEA) Energyrelated greenhouse gas (GHG) exhausts may tweaked to substantial climate deterioration because of an average 6 °C global warming [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Chemical and Physical Properties of Sargasso Bloom for Renewable Energy Generation With Artificial Intelligence Modeling

2023

ecb

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The harmful effects of various algae blooms on human and marine life are well-known globally. It is necessary to invent the utilization of such algae bloom for betterment of society and hence proposed research provided multiple benefits. As a supportive tool, Artificial Intelligence (AI) is becoming the high potential area for the fast analysis and predictions in almost all research areas. Also, a microbial fuel cell (MFC) is a relatively new type of bioreactor for wastewater treatment. As there is a need for invention of new renewable energy sources, we invented Sargassum as a new media for energy generation and therefore this research is the first time using Sargassum samples in a single-chambered MFC. This paper presents the development of new deep learning algorithm SargassoCNN to identify the various types of Sargassum bloom samples which further can be analyzed for selection of sample for best electricity generation medium using deep learning image processing algorithm.

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Microbial fuel cell soft sensor for real-time toxicity detection and monitoring

Cited by 22 publications

References 47 publications

Toxicity of Heavy Metals and Recent Advances in Their Removal: A Review

Toxicity of Heavy Metals and Recent Advances in Their Removal: A Review

Label-Free Surface-Enhanced Raman Scattering-Based Microbial Whole-Cell Biosensor for Acute Toxicity Assessment in Water

Analysis of Chemical and Physical Properties of Sargasso Bloom for Renewable Energy Generation With Artificial Intelligence Modeling

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