Inactivation of fungal spores from clinical environment by silver bio-nanoparticles; optimization, artificial neural network model and mechanism
The present study aimed to assess the efficiency of silver bio-nanoparticles (Ag-NPs) in inactivating of the Aspergillus fumigatus, A. parasiticus and A. flavus var. columnaris and A. aculeatus spores. The AgNPs were syn�thesized in secondary metabolic products of Penicillium pedernalens 604 EAN....
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6883/1/J13392_33f2f46a617a3d0d9ad5ed7358186f0a.pdf |
| _version_ | 1825710271985876992 |
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| author | Nomand, Efaq Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Talip, Balkis Othman, Norzila Hossain, Sohrab N. Vo, Dai-Viet Alduais, Nayef |
| author_facet | Nomand, Efaq Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Talip, Balkis Othman, Norzila Hossain, Sohrab N. Vo, Dai-Viet Alduais, Nayef |
| author_sort | Nomand, Efaq |
| collection | UTHM |
| description | The present study aimed to assess the efficiency of silver bio-nanoparticles (Ag-NPs) in inactivating of the
Aspergillus fumigatus, A. parasiticus and A. flavus var. columnaris and A. aculeatus spores. The AgNPs were syn�thesized in secondary metabolic products of Penicillium pedernalens 604 EAN. The inactivation process was
optimized by response surface methodology (RSM) as a function of Ag NPs volume (1–10 μL/mL); time (10–120
min); pH (5–8); initial fungal concentrations (log10) (3–6). The artificial neural network (ANN) model was used
to understand the behavior of spores for the factors affecting inactivation process. The best conditions to ach�ieved SAL 10− 6 of the fungal spores were recorded with 3.46 μl/mL of AgNPs, after 120 min at pH 5 and with 6
log of initial fungal spore concentrations, at which 5.99 vs. 6.09 (SAL 10− 6
) log reduction was recorded in actual
and predicted results respectively with coefficient of 87.00%. The ANN revealed that the timehas major
contribution in the inactivation process compare to Ag NPs volume. The fungal spores were totally inactivated
(SAL 10− 6
, 6 log reduction with 99.9999%) after 110 min of the inactivation process, 10 min more was required
to insure the irreversible inactivation of the fungal spores. The absence of protease and cellulase enzymes pro�duction confirm the total inactivation of the fungal spores. FESEM analysis revealed that the AgNPs which
penetrated the fungal spores leading to damage and deform the fungal spore morphology. The AFM analysis
confirmed the total spore surface damage. The bands in the range of the Raman spectroscopy from 1300 to 1600
cm− 1 in the inactivated spores indicate the presence of CH3, CH2 and the deformation of lipids released outside
the spore cytoplasm. These finding indicate that the AgNPs has high potential as a green alternative inactivation
process for the airborne fungal spores. |
| first_indexed | 2024-03-05T21:55:05Z |
| format | Article |
| id | uthm.eprints-6883 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:55:05Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | uthm.eprints-68832022-04-07T02:24:41Z http://eprints.uthm.edu.my/6883/ Inactivation of fungal spores from clinical environment by silver bio-nanoparticles; optimization, artificial neural network model and mechanism Nomand, Efaq Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Talip, Balkis Othman, Norzila Hossain, Sohrab N. Vo, Dai-Viet Alduais, Nayef TP Chemical technology The present study aimed to assess the efficiency of silver bio-nanoparticles (Ag-NPs) in inactivating of the Aspergillus fumigatus, A. parasiticus and A. flavus var. columnaris and A. aculeatus spores. The AgNPs were syn�thesized in secondary metabolic products of Penicillium pedernalens 604 EAN. The inactivation process was optimized by response surface methodology (RSM) as a function of Ag NPs volume (1–10 μL/mL); time (10–120 min); pH (5–8); initial fungal concentrations (log10) (3–6). The artificial neural network (ANN) model was used to understand the behavior of spores for the factors affecting inactivation process. The best conditions to ach�ieved SAL 10− 6 of the fungal spores were recorded with 3.46 μl/mL of AgNPs, after 120 min at pH 5 and with 6 log of initial fungal spore concentrations, at which 5.99 vs. 6.09 (SAL 10− 6 ) log reduction was recorded in actual and predicted results respectively with coefficient of 87.00%. The ANN revealed that the timehas major contribution in the inactivation process compare to Ag NPs volume. The fungal spores were totally inactivated (SAL 10− 6 , 6 log reduction with 99.9999%) after 110 min of the inactivation process, 10 min more was required to insure the irreversible inactivation of the fungal spores. The absence of protease and cellulase enzymes pro�duction confirm the total inactivation of the fungal spores. FESEM analysis revealed that the AgNPs which penetrated the fungal spores leading to damage and deform the fungal spore morphology. The AFM analysis confirmed the total spore surface damage. The bands in the range of the Raman spectroscopy from 1300 to 1600 cm− 1 in the inactivated spores indicate the presence of CH3, CH2 and the deformation of lipids released outside the spore cytoplasm. These finding indicate that the AgNPs has high potential as a green alternative inactivation process for the airborne fungal spores. Elsevier 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/6883/1/J13392_33f2f46a617a3d0d9ad5ed7358186f0a.pdf Nomand, Efaq and Al-Gheethi, Adel and Radin Mohamed, Radin Maya Saphira and Talip, Balkis and Othman, Norzila and Hossain, Sohrab and N. Vo, Dai-Viet and Alduais, Nayef (2022) Inactivation of fungal spores from clinical environment by silver bio-nanoparticles; optimization, artificial neural network model and mechanism. Environmental Research, 204. pp. 1-15. https://doi.org/10.1016/j.envres.2021.111926 |
| spellingShingle | TP Chemical technology Nomand, Efaq Al-Gheethi, Adel Radin Mohamed, Radin Maya Saphira Talip, Balkis Othman, Norzila Hossain, Sohrab N. Vo, Dai-Viet Alduais, Nayef Inactivation of fungal spores from clinical environment by silver bio-nanoparticles; optimization, artificial neural network model and mechanism |
| title | Inactivation of fungal spores from clinical environment by silver
bio-nanoparticles; optimization, artificial neural network model
and mechanism |
| title_full | Inactivation of fungal spores from clinical environment by silver
bio-nanoparticles; optimization, artificial neural network model
and mechanism |
| title_fullStr | Inactivation of fungal spores from clinical environment by silver
bio-nanoparticles; optimization, artificial neural network model
and mechanism |
| title_full_unstemmed | Inactivation of fungal spores from clinical environment by silver
bio-nanoparticles; optimization, artificial neural network model
and mechanism |
| title_short | Inactivation of fungal spores from clinical environment by silver
bio-nanoparticles; optimization, artificial neural network model
and mechanism |
| title_sort | inactivation of fungal spores from clinical environment by silver bio nanoparticles optimization artificial neural network model and mechanism |
| topic | TP Chemical technology |
| url | http://eprints.uthm.edu.my/6883/1/J13392_33f2f46a617a3d0d9ad5ed7358186f0a.pdf |
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