Kinetics of photocatalytic degradation of organic compounds: a mini-review and new approach

Tran, Hai D.; Nguyen, Dinh Quan; Phương, Trần Thị Thu; Tran, Uyen P. N.

doi:10.1039/d3ra01970e

Cited by 20 publications

(7 citation statements)

References 98 publications

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“…To get a better insight into the photocatalytic degradation process, degradation kinetics was studied. The most commonly used equation to describe the photocatalytic degradation kinetics is the Langmuir–Hinshelwood (L–H) kinetic model, and according to this model, the degradation equation can be presented as , ln 0.25em C 0 C = k t where k represents the pseudo-first-order reaction rate constant in min –1 . Figure B represents the variation ln 0.25em C 0 C with t .…”

Section: Resultsmentioning

confidence: 99%

“…To get a better insight into the photocatalytic degradation process, degradation kinetics was studied. The most commonly used equation to describe the photocatalytic degradation kinetics is the Langmuir–Hinshelwood (L–H) kinetic model, and according to this model, the degradation equation can be presented as , where k represents the pseudo-first-order reaction rate constant in min –1 . Figure B represents the variation with t .…”

Section: Resultsmentioning

confidence: 99%

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Reduced Graphene Oxide–SnSe Nanocomposite Photocatalyst with High Apparent Quantum Yield for the Photodegradation of Norfloxacin

Kar,

Pal,

Ghosh

2024

ACS Appl. Nano Mater.

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A visible-light-responsive photocatalyst, comprising reduced graphene oxide (RGO) modified tin selenide (SnSe), was fabricated via a cost-effective, one-pot, easy-to-achieve, single-step solvothermal method. The Brunauer−Emmett−Teller analysis shows SnSe with a specific surface area of about 0.3 m 2 g −1 , while RGO−SnSe significantly increases it to 8.44 m 2 g −1 . The photocatalytic kinetics of the RGO−SnSe composite revealed a pseudo-first-order reaction mechanism during the degradation of norfloxacin antibiotics in an aqueous environment. Results indicated that an increase in RGO content in the composites led to enhanced degradation efficiency and apparent quantum yield, reaching maximum values of 90.7 and 11.37%, respectively, at a specific RGO loading of 37 wt %. These values were found to be 3.6 times higher than pure SnSe. However, further augmentation of RGO resulted in a decline in both efficiency and yield. The enhanced catalytic performance can be attributed to the improved synergy between RGO and SnSe. The electrical energy per order was calculated to assess the energy efficiency of the photocatalytic degradation process, yielding a low value of 6.7 kW h m −3 /order for the RGO−SnSe composite with a loading of 0.75 mg/L RGO−SnSe catalyst. The photocatalytic activity of the composite remained nearly unchanged for at least four repeated cycles without any degradation of the catalyst. This outcome underscores the efficacy of the RGO−SnSe composite as an efficient and sustainable technique for antibiotic degradation with a high apparent quantum yield and low energy consumption.

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Section: Resultsmentioning

confidence: 99%

“…To get a better insight into the photocatalytic degradation process, degradation kinetics was studied. The most commonly used equation to describe the photocatalytic degradation kinetics is the Langmuir–Hinshelwood (L–H) kinetic model, and according to this model, the degradation equation can be presented as , where k represents the pseudo-first-order reaction rate constant in min –1 . Figure B represents the variation with t .…”

Section: Resultsmentioning

confidence: 99%

Reduced Graphene Oxide–SnSe Nanocomposite Photocatalyst with High Apparent Quantum Yield for the Photodegradation of Norfloxacin

Kar,

Pal,

Ghosh

2024

ACS Appl. Nano Mater.

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“…12 b (see F1-SI for the detailed mechanism). The reduction of CV and MB by the FeNP catalyst in the presence of NaBH 4 can be elucidated using the Langmuir–Hinshelwood model 59 . According to this model, the initially adsorbed BH 4 − ions donate electrons to the FeNPs (as shown in Eq.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, a negatively charged layer develops around the FeNPs 60 . The reduction reaction at the FeNP surface is followed by the transfer of electrons from the FeNPs to cationic dye molecules through electrostatic interactions 59 . Previous work on PNP reduction by Ag/SiO 2 NC catalysts has also shown that these catalysts adhere to the Langmuir–Hinshelwood mechanism 61 .…”

Section: Resultsmentioning

confidence: 99%

Highly efficient catalytic degradation of organic dyes using iron nanoparticles synthesized with Vernonia Amygdalina leaf extract

Jara,

Mekiso,

Washe

2024

Sci Rep

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Today, nanoscience explores the potential of nanoparticles due to their extraordinary properties compared to bulk materials. The synthesis of metal nanoparticles using plant extracts is a very promising method for environmental remediation, which gets global attention due to pollution-led global warming. In the present study, iron nanoparticles (FeNPs) were successfully synthesized by the green method using Vernonia amygdalina plant leaf extract as a natural reducing and capping agent. Biosynthesized FeNPs were characterized with different analytical techniques such as UV–visible, FT-IR, XRD, and SEM. The analysis revealed the formation of amorphous FeNPs with an irregular morphology and non-uniform distribution in size and shape. The average particle size was approximately 2.31 µm. According to the catalytic degradation investigation, the FeNPs produced via the green approach are highly effective in breaking down both CV and MB into non-toxic products, with a maximum degradation efficiency of 97.47% and 94.22%, respectively, when the right conditions are met. The kinetics study exhibited a high correlation coefficient close to unity (0.999) and (0.995) for the degradation of MB and CV, respectively, for the zero-order pseudo-kinetics model, which describes the model as highly suitable for the degradation of both dyes by FeNPs compared to other models. The reusability and stability of biosynthesized nano-catalysts were studied and successfully used as efficient catalysts with a slight decrease in the degradation rate more than four times. The results from this study illustrate that green synthesized FeNPs offer a cost-effective, environmentally friendly, and efficient means for the catalytic degradation of organic dyes.

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“…Furthermore, according to recent works [66,67], the proposed model should be validated by experimental data, even though it was designed to be independent of the specific pollutant and photocatalyst.…”

Section: Accuracy Of Measurementsmentioning

confidence: 99%

Depth dependence of the photocatalytic reaction rate. kinetic model generalization

et al. 2023

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Dependence of photocatalytic reaction rate on the depth of the solution in case of small depths was studied. To the best of our knowledge, there are no reports on depth dependence of the photocatalytic reaction rate in literature. The study of reaction rate on the solution depth can play an important role in the development of systems for photodecomposition of organic pollutants. The model for photodegradation with a modified rate constant, which was developed in our previous work was generalized for the application of the catalyst in both plate and powder form. The applicability of the model was proved on the methylene blue discoloration by ZnO photocatalyst powder and plates.

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Kinetics of photocatalytic degradation of organic compounds: a mini-review and new approach

Abstract: A new approach for kinetics study of photocatalytic degradation of organic compounds.

Cited by 20 publications

References 98 publications

Reduced Graphene Oxide–SnSe Nanocomposite Photocatalyst with High Apparent Quantum Yield for the Photodegradation of Norfloxacin

Reduced Graphene Oxide–SnSe Nanocomposite Photocatalyst with High Apparent Quantum Yield for the Photodegradation of Norfloxacin

Highly efficient catalytic degradation of organic dyes using iron nanoparticles synthesized with Vernonia Amygdalina leaf extract

Depth dependence of the photocatalytic reaction rate. kinetic model generalization

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