MbrlCatalogueTitleDetail

Do you wish to reserve the book?
Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity
Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity
Hey, we have placed the reservation for you!
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity
Oops! Something went wrong.
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Title added to your shelf!
Title added to your shelf!
View what I already have on My Shelf.
Oops! Something went wrong.
Oops! Something went wrong.
While trying to add the title to your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity
Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity

Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
How would you like to get it?
We have requested the book for you! Sorry the robot delivery is not available at the moment
We have requested the book for you!
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity
Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity
Journal Article

Machine learning driven forward-reverse design of Ag–ZnO–PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity

2026
Request Book From Autostore and Choose the Collection Method
Overview
At present, the realm of nanobionics has garnered significant attention for its potential applications in microalgal systems, offering innovative strategies to augment growth, productivity, and metabolic performance. Present study influences nanotechnology to explore the multifaceted effects of novel biocompatible nanocomposite Ag–ZnO–PEEK (silver-zinc oxide- Polyether Ether Ketone) on isolated microalgae Chlorella vulgaris_ AK, with a focus on improving the biomass production, mitigating oxidative stress, and enhancing the lipid biosynthesis. The morphometric demonstrations of Ag–ZnO–PEEK nanocomposite were characterized by Scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. Different concentrations of Ag–ZnO–PEEK (10, 20, 40, 80, and 160 ppm) were applied to the microalgae for observing the biomass enhancement and lipid yield. Among all the applied concentrations, 40 ppm exhibited the suitable one for high biomass and lipid yield of 4.25 g/L and 3.31 g/L respectively. Machine learning integrating forward prediction and E-UCB-based inverse design was employed to optimize microalgal growth conditions. Gradient boosting achieved the highest R 2 of 0.9794, while ensemble uncertainty enabled reliable identification of high-performing unsampled conditions. Additionally, the effect of the as synthesized nanocomposite was also investigated as a potential antibacterial candidate against Bacillus sp. Hence, these advancements not only elevate the microalgae biomass production but also support the sustainable generation of biofuels and bioproducts from microalgae. Therefore, this study provides a scalable framework for integrating nanotechnology into renewable energy by maintaining circular bio-economy.