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Author: search.filters.author.Jayanetti, MSubject: search.filters.subject.Nanoparticles

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    Assessing the Efficacy of Machine Learning Algorithms in Predicting Critical Properties of Gold Nanoparticles for Pharmaceutical Applications
    (Springer, 2025-07-08) Fernando, H; Mohottala, S; Jayanetti, M; Thambiliyagodage, C
    Au nanoparticles are increasingly used in pharmaceuticals, but their synthesis is costly and time-intensive. Machine Learning can help optimize this process. In this research, eight distinct Machine Learning models were implemented and optimized on a dataset comprising 3000 records of gold nanoparticles. The performance of these models was assessed using four accuracy metrics and the time required for training and inference. The results are promising, with all seven models demonstrating high accuracy and low time requirements. Notably, the XGBoost and Artificial Neural Network models exhibited exceptional performance, with Mean Squared Error values of 0.0235 and 0.0098, Mean Absolute Error values of 0.1021 and 0.0674, Mean Absolute Percentage Deviation values of 0.4945 and 0.3590, R2 scores of 0.9995 and 0.9998, and inference times of 0.0029 and 0.4299 s, respectively. The Explainable Artificial Intelligence analysis of the resulting models revealed some interesting insights into how the models make the predictions and what factors heavily contribute to the nanoparticle AVG_R, allowing chemists to optimize the synthesis for gold nanoparticles better. The key contributions of the research include the design and development of eight Machine Learning models using industry-standard frameworks, the training, tuning, and evaluation of these eight models using five different metrics, and further assessment of these trained models using Explainable Artificial Intelligence. The findings indicate a substantial potential for applying neural networks in the design phase of nanoparticle synthesis, which could lead to significant reductions in both the time and cost required for synthesizing Au nanoparticles for pharmaceutical applications.
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    PublicationEmbargo
    Assessing the Efficacy of Machine Learning Algorithms in Predicting Critical Properties of Gold Nanoparticles for Pharmaceutical Applications
    (Springer Nature Link, 2025-07-08) Fernando, H; Mohottala, S; Jayanetti, M; Thambiliyagodage, C
    Au nanoparticles are increasingly used in pharmaceuticals, but their synthesis is costly and time-intensive. Machine Learning can help optimize this process. In this research, eight distinct Machine Learning models were implemented and optimized on a dataset comprising 3000 records of gold nanoparticles. The performance of these models was assessed using four accuracy metrics and the time required for training and inference. The results are promising, with all seven models demonstrating high accuracy and low time requirements. Notably, the XGBoost and Artificial Neural Network models exhibited exceptional performance, with Mean Squared Error values of 0.0235 and 0.0098, Mean Absolute Error values of 0.1021 and 0.0674, Mean Absolute Percentage Deviation values of 0.4945 and 0.3590, R2 scores of 0.9995 and 0.9998, and inference times of 0.0029 and 0.4299 s, respectively. The Explainable Artificial Intelligence analysis of the resulting models revealed some interesting insights into how the models make the predictions and what factors heavily contribute to the nanoparticle AVG_R, allowing chemists to optimize the synthesis for gold nanoparticles better. The key contributions of the research include the design and development of eight Machine Learning models using industry-standard frameworks, the training, tuning, and evaluation of these eight models using five different metrics, and further assessment of these trained models using Explainable Artificial Intelligence. The findings indicate a substantial potential for applying neural networks in the design phase of nanoparticle synthesis, which could lead to significant reductions in both the time and cost required for synthesizing Au nanoparticles for pharmaceutical applications.
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    PublicationOpen Access
    Antibacterial Activity of Zn Decorated TiO2 Nanocomposites
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Kumarasinghe, N.M.A,; Thambiliyagodage, C; Jayanetti, M; Liyanaarachchi, H
    Bacterial infections have a significant public health impact. Infections are caused by bacteria in animals, plants as well as humans. Pathogenic bacteria can produce toxins, which are chemical poisons that interfere with cell function such as digestion of normal human enzymes, evasion of infection-fighting white blood cells, and immune clearance. Antibiotic prophylaxis is used to prevent bacterial infection. Antibiotic resistance is one of the most serious concerns in world health. Antibacterial nanoparticles are one possible answer to antimicrobial resistance. These nanomaterials not only kill antibiotic-resistant bacteria through various modes of action but, they can also be employed in conjunction with existing clinically relevant antibiotics to help overcome antimicrobial resistance mechanisms. In this study, anodized titanium dioxide (TiO2) nanorods were treated hydrothermally with zinc oxide (ZnO) nanoparticles to give titanium (Ti) antibacterial properties. The antibacterial activity of synthesized samples was investigated by Agar Well Diffusion method at 40 mg/ml concentration, against gram negative Klebsiella pneumoniae. To determine the antibacterial activity, the diameter of the zone of inhibition was measured, and the resulting data were statistically analyzed. Zn/TiO2 nano particles were characterized by using X-ray diffraction (XRD) Analysis.
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    PublicationOpen Access
    Antibacterial Activity of Cu Decorated TiO2 Nanorods
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Nilaweera, G; Thambiliyagodage, C; Jayanetti, M; Liyanaarachchi, H
    Global public health is seriously threatened by the spread of infectious illnesses in general, particularly by the appearance of bacterial strains that are resistant to antibiotics. New antibacterial drugs are likely a result of recent developments in the field of nanobiotechnologies, particularly the ability to make metal oxide nanomaterials with specific morphologies. Using antibiotics for a long time period will show antibiotic resistance in host cells, which means the drug does not kill the pathogen anymore. As a solution to this problem, nanoparticles can be used. Researchers may find nanoparticles with high antibacterial activity which can kill the pathogen. This research shows the antibacterial activity of Cu decorated TiO2 nanoparticles against Klebsiella pneumoniae. In here the nanoparticles were synthesized in three weight ratios with TiO2 and CuO using hydrothermal method. Pure CuO and TiO2 were synthesized as controls. Then antibacterial activity was checked by the well diffusion method. After incubation the inhibition zones were measured, and the results were recorded. The antibacterial effect can be determined with the size of the inhibition zone. The synthesized nanoparticles were characterized using XRD to analyze physical properties such as phase composition, crystal structure. The value for inhibition zone of the best performing sample which the sample concentration is 40mg/ml is 13.17±1.53 mm which contains TiO2 : CuO (1:2) weight ratio. Therefore it can be determined that the best performing sample which has the highest antibacterial activity against Klebsiella pneumoniae is G3 which contains TiO2 : CuO (1:2) weight ratio.