ArticleScientific reports2026
Artificial intelligence tools for AgNP-SSB-SN and AgNP-CSS-SN biosynthesis from Synechococcus PCC 11901 and Chlorella sorokiniana MSP1 for hazardous dyes remediation.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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4 authors.
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Abstract
In response to concerns of environmental pollution caused by hazardous dyes, remediating these dyes is a challenge. Herein, we developed a genetic algorithm-artificial neural network (GA-ANN) based optimization process for phyco-synthesis of silver nanoparticles (AgNPs) from fast growing Synechococcus sp. PCC 11901 and Chlorella sorokiniana MSP1 bioextract (named SSB-SN and CSS-SN). The developed GA-ANN model predicted the most suitable process variables with excellent correlation coefficients of 0.97 and 0.98 for SSB-SN and CSS-SN, respectively. The existence of the potential functional groups and compositional aspects of AgNPs were studied using fourier transform infrared spectroscopy and field-emission scanning electron microscopy-energy-dispersive x-ray spectroscopy. Further, the Transmission electron microscopy analysis revealed the average size of 10.66 and 26.03 nm of SSB-SN and CSS-SN, respectively. Thermogravimetry analysis and X-ray diffraction analysis revealed a higher thermal stability and crystallinity of the phyco-synthesised AgNPs. The SSB-SN and CSS-SN nanoparticles showed 99.79 ± 1.18% and 73.13 ± 0.82% of Orange-II dye degradation. Whereas, 98.17 ± 0.07% and 97.76 ± 0.08% for Sudan black dye. These results followed pseudo-second-order kinetics. Finally, the present findings reveal that efficient phyco-synthesis process for AgNPs, offering a promising solution for hazardous dyes remediation.
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