Genetically engineered microorganisms for environmental remediation

Chemosphere, Год журнала: 2022, Номер 310, С. 136751 - 136751

Опубликована: Окт. 6, 2022

In the recent era, increasing persistence of hazardous contaminants is badly affecting globe in many ways. Due to high environmental contamination, almost every second species on earth facing worst issue their survival. Advances newer remediation approaches may help enhance bioremediation's quality, while conventional procedures have failed remove compounds from environment. Chemical and physical waste cleanup been used current circumstances; however, these methods are costly harmful Thus, there has a rise use bioremediation due an increase which led development genetically engineered microbes (GEMs). It safer more cost-effective microorganisms rather than alternative methods. GEMs created by introducing stronger protein into bacteria through biotechnology or genetic engineering desired trait. Biodegradation oil spills, halobenzoates naphthalenes, toluenes, trichloroethylene, octanes, xylenes etc. accomplished using such bacteria, fungus, algae. Biotechnologically induced powerful naturally occurring ones degrade faster because they can quickly adapt new pollutants encounter co-metabolize. Genetic worthy process that will benefit environment ultimately health our people.

Язык: Английский

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MOF-based sensor platforms for rapid detection of pesticides to maintain food quality and safety

Food and Chemical Toxicology, Год журнала: 2022, Номер 165, С. 113176 - 113176

Опубликована: Май 26, 2022

Язык: Английский

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Construction of co-immobilized laccase and mediator based on MOFs membrane for enhancing organic pollutants removal

Chemical Engineering Journal, Год журнала: 2022, Номер 451, С. 138080 - 138080

Опубликована: Июль 13, 2022

Язык: Английский

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Semiconductor Nanomaterial Photocatalysts for Water-Splitting Hydrogen Production: The Holy Grail of Converting Solar Energy to Fuel

Nanomaterials, Год журнала: 2023, Номер 13(3), С. 546 - 546

Опубликована: Янв. 29, 2023

Nanomaterials have attracted attention for application in photocatalytic hydrogen production because of their beneficial properties such as high specific surface area, attractive morphology, and light absorption. Furthermore, is a clean green source energy that may help to resolve the existing crisis increasing environmental pollution caused by consumption fossil fuels. Among various methods, water splitting most significant it utilizes solar light, freely available throughout world, activated via semiconductor nanomaterial catalysts. Various types photocatalysts are developed this purpose, including carbon-based transition-metal-based photocatalysts, each has its advantages disadvantages. The present review highlights basic principle techniques thermochemical process, electrocatalytic direct enhance production. Moreover, modification strategies band gap engineering, alloys, multiphoton been reviewed. Z- S-schemes heterojunction were also Ultimately, developing efficient, practical, highly novel visible-light-harvesting will be discussed, addition challenges involved. This can provide researchers with reference current state affairs, motivate them develop new materials generation.

Язык: Английский

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Fe3O4@MIL-100(Fe) modified ZnS nanoparticles with enhanced sonocatalytic degradation of tetracycline antibiotic in water

Ultrasonics Sonochemistry, Год журнала: 2023, Номер 95, С. 106409 - 106409

Опубликована: Апрель 18, 2023

Sonocatalysis has attracted excellent research attention to eradicate hazardous pollutants from the environment effectively. This work synthesised an organic/inorganic hybrid composite catalyst by coupling Fe3O4@MIL-100(Fe) (FM) with ZnS nanoparticles using solvothermal evaporation method. Remarkably, material delivered significantly enhanced sonocatalytic efficiency for removing tetracycline (TC) antibiotics in presence of H2O2 compared bare nanoparticles. By adjusting different parameters such as TC concentration, dosage and amount, optimized (20 %Fe3O4@MIL-100(Fe)/ZnS) removed 78.25% antibiotic 20 min at cost 1 mL H2O2. These much superior activities are attributed efficient interface contact, effective charge transfer, accelerated transport capabilities strong redox potential acoustic catalytic performance FM/ZnS systems. Based on various characterization, free radical capture experiments energy band structures, we proposed a mechanism degradation based S-scheme heterojunctions Fenton like reactions. will provide important reference developing ZnS-based nanomaterials study sonodegradation pollutants.

Язык: Английский

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Progress in copper-based supported heterogeneous electro-Fenton catalysts

Chemical Engineering Journal, Год журнала: 2024, Номер 486, С. 150217 - 150217

Опубликована: Март 7, 2024

Язык: Английский

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Bioremediation techniques for soil organic pollution: Mechanisms, microorganisms, and technologies - A comprehensive review

Ecological Engineering, Год журнала: 2024, Номер 207, С. 107338 - 107338

Опубликована: Июль 31, 2024

Язык: Английский

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Recent advances in the synthesis and environmental catalytic applications of layered double hydroxides-based materials for degradation of emerging pollutants through advanced oxidation processes

Materials Research Bulletin, Год журнала: 2022, Номер 154, С. 111924 - 111924

Опубликована: Июнь 7, 2022

Язык: Английский

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Dissecting π-conjugated covalent-coupling over conductive MOFs toward efficient two-electron oxygen reduction

Applied Catalysis B Environment and Energy, Год журнала: 2022, Номер 317, С. 121706 - 121706

Опубликована: Июль 8, 2022

Язык: Английский

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Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Сен. 30, 2022

Abstract Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, eliminating highly necessary. In this study, the ability of different novel robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Boosting, Decision Tree, Random Forest was implemented predict adsorptive removal arsenate [As(V)] from wastewater over 13 metal–organic frameworks (MOFs). A large experimental dataset collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, surface area, temperature, solution pH, presence anions were considered as input variables, As(V) selected output models. developed models evaluated using statistical criteria. obtained results indicated that LightGBM model provided most accurate reliable response adsorption by MOFs possesses R 2 , RMSE, STD, AAPRE (%) 0.9958, 2.0688, 0.0628, 2.88, respectively. expected trends with increasing coexistence predicted reasonably model. Sensitivity analysis revealed process adversely relates concentration directly depends on area dosage. This study proves ML approaches are capable manage complicated problems datasets can be affordable alternatives expensive time-consuming treatment processes.

Язык: Английский

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