Melia Azedarach impregnated Co and Ni zero-valent metal nanoparticles for organic pollutants degradation: validation of experiments through statistical analysis

Ahmad, Zubair; Shah, Sher Ali; Khattak, Irum; Ullah, Himayat; Khan, Amir; Shah, Ruidar Ali; Khan, Shahid Ali; Khan, Sher Bahadar

doi:10.1007/s10854-020-04250-5

Cited by 9 publications

(4 citation statements)

References 51 publications

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“…These bioactive molecules act as photosensitizers, allowing NPs to be excited with less energy and thus aid the photodegradation process. [147] This pragmatic essentiality of TON and TOF as good benchmarks for determining the instantaneous efficiency of a catalyst was in parallel with one expounded in various other dye degradation research [146,177,[231][232][233][234][235] using biogenic (plant) NPs, and in our own view, the earnest use of TON and TOF together with degradation/adsorption capacity or percentage needs to be more advocated.…”

Section: Aumentioning

confidence: 93%

Phytogenically bioengineered metal nanoarchitecture for degradation of refractory dye water pollutants: A pragmatic minireview

Emmanuel

Adesibikan

Saliu

2022

Applied Organom Chemis

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The present scenario of water insecurity (poor availability and accessibility to clean and healthy water when needed) denotes the preponderance of environmental damage caused by effluents or waste runoff from industries, such as the dye and textile industries. The polluted waters have led to terrible and sonorous negative impacts on the ecosystem and econetworking, thus, urging for a sustainably suitable substitute to tackle and annul the noxious environmental constraint posse by the hazardous dye effluent. Several chemical and physical methods for treating dye effluent are now in use, but they are time‐consuming, expensive, and inefficient. Interestingly, nanoparticles have egressed as a superior answer for efficient dye removal and degradation, because of their chemical reactivity and exceptional surface characteristics. As a result, the use of metal nanomaterials in the treatment of dye runoffs has been thoroughly investigated. The study used major scientific databases such as SciFinder, Scopus, PubMed, Google Scholar, and Science Direct to conduct a comprehensive review of publicly available literature. Degradation, dye effluent, green metal nanoparticles, and water pollution were the keywords used to find scholarly papers. Efforts were made to figure out and explain the mechanism of dye effluent degradation, how long the degradation will take and how effective the use of plant biosynthesized metal nanoparticles for dye removal is. In addition, the role of bimetallic nanoparticles has also been investigated with remarkable feat from literature. The current level of knowledge about the mechanism and the use of plant biogenic metal nanoparticles in common dye effluent treatment is summarized in this paper.

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Section: Aumentioning

confidence: 93%

Phytogenically bioengineered metal nanoarchitecture for degradation of refractory dye water pollutants: A pragmatic minireview

Emmanuel

Adesibikan

Saliu

2022

Applied Organom Chemis

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“…The following equation indicated the conversion of Co (II) ions to their Co 0 NPs [ 33 ]. Co +2 + BH 4 − + 3H 2 O + 2e − → Co 0 + H 3 BO 3 + 7H + 3Co 0 + 6H + → 3Co +2 + 3H 2 …”

Section: Methodsmentioning

confidence: 99%

“…Both Co +2 and Co 0 were supported on Alg/FP, Alg-CoNi1/FP, and Alg-CoNi2/FP solid substrate. The following equation indicated the conversion of Co (II) ions to their Co 0 NPs [33].…”

Section: Preparation Of Alg-coni1/fp@co and Alg-coni2/fp@co Npsmentioning

confidence: 99%

Antibacterial Films of Alginate-CoNi-Coated Cellulose Paper Stabilized Co NPs for Dyes and Nitrophenol Degradation

et al. 2021

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The development of a solid substrate for the support and stabilization of zero-valent metal nanoparticles (NPs) is the heart of the catalyst system. In the current embodiment, we have prepared solid support comprise of alginate-coated cellulose filter paper (Alg/FP) for the synthesis and stabilization of Co nanoparticles (NPs) named as Alg/FP@Co NPs. Furthermore, Alginate polymer was blended with 1 and 2 weight percent of CoNi NPs to make Alg-CoNi1/FP and Alg-CoNi2/FP, respectively. All these stabilizing matrixes were used as dip-catalyst for the degradation of azo dyes and reduction of 4-nitrophenol (4NP). The effect of initial dye concentration, amount of NaBH4, and catalyst dosage was assessed for the degradation of Congo red (CR) dye by using Alg-CoNi2/FP@Co NPs. Results indicated that the highest kapp value (3.63 × 10−1 min−1) was exhibited by Alg-CoNi2/FP@Co NPs and lowest by Alg/FP@Co NPs against the discoloration of CR dye. Furthermore, it was concluded that Alg-CoNi2/FP@Co NPs exhibited strong catalyst activity against CR, and methyl orange dye (MO) degradation as well as 4NP reduction. Antibacterial activity of the prepared composites was also investigated and the highest l activity was shown by Alg-CoNi2/FP@Co NPs, which inhibit 2.5 cm zone of bacteria compared to other catalysts.

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“…Understanding the potential impact of climate change on the suitable habitat of M. azedarach is of great significance to its cultivation and conservation in China. Studies conducted on M. azedarach were mainly focused on tree and stand productivity, extraction of active ingredients, and pest resistance potential 3 , 18 . Research on M. azedarach potential distribution as affected by climate change is lacking and thus, the present study is aimed at exploring the above-mentioned seven data mining techniques to establish climate-based distribution prediction models and select the best model in predictions of the species future suitable habitat.…”

Section: Introductionmentioning

confidence: 99%

Predicting suitable habitats of Melia azedarach L. in China using data mining

Feng

Tian

El‐Kassaby

et al. 2022

Sci Rep

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Melia azedarach L. is an important economic tree widely distributed in tropical and subtropical regions of China and some other countries. However, it is unclear how the species’ suitable habitat will respond to future climate changes. We aimed to select the most accurate one among seven data mining models to predict the current and future suitable habitats for M. azedarach in China. These models include: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), random forest (RF), naive bayesian model (NBM), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). A total of 906 M. azedarach locations were identified, and sixteen climate predictors were used for model building. The models’ validity was assessed using three measures (Area Under the Curves (AUC), kappa, and overall accuracy (OA)). We found that the RF provided the most outstanding performance in prediction power and generalization capacity. The top climate factors affecting the species’ suitable habitats were mean coldest month temperature (MCMT), followed by the number of frost-free days (NFFD), degree-days above 18 °C (DD > 18), temperature difference between MWMT and MCMT, or continentality (TD), mean annual precipitation (MAP), and degree-days below 18 °C (DD < 18). We projected that future suitable habitat of this species would increase under both the RCP4.5 and RCP8.5 scenarios for the 2011–2040 (2020s), 2041–2070 (2050s), and 2071–2100 (2080s). Our findings are expected to assist in better understanding the impact of climate change on the species and provide scientific basis for its planting and conservation.

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Melia Azedarach impregnated Co and Ni zero-valent metal nanoparticles for organic pollutants degradation: validation of experiments through statistical analysis

Cited by 9 publications

References 51 publications

Phytogenically bioengineered metal nanoarchitecture for degradation of refractory dye water pollutants: A pragmatic minireview

Phytogenically bioengineered metal nanoarchitecture for degradation of refractory dye water pollutants: A pragmatic minireview

Antibacterial Films of Alginate-CoNi-Coated Cellulose Paper Stabilized Co NPs for Dyes and Nitrophenol Degradation

Predicting suitable habitats of Melia azedarach L. in China using data mining

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