Enhanced electrical and magnetic properties of (Co, Yb) co-doped ZnO memristor for neuromorphic computing

Elboughdiri, Noureddine; Iqbal, Shahid; Abdullaev, Sherzod; Aljohani, Mohammed; Safeen, Akif; Althubeiti, Khaled; Khan, Rajwali

doi:10.1039/d3ra06853f

Cited by 8 publications

(1 citation statement)

References 59 publications

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“…The c/a ratio or packing factor for an ideal stoichiometric structure is 1.6333. An R (degree of distortion) value greater or less than unity indicates the presence of zinc and oxygen vacancies [ 36 ]. The average crystallite size ( D ) was calculated from the Scherrer equation [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles

Navarro-López,

Perfecto-Avalos,

Zavala

et al. 2024

Antibiotics

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The rise in antibiotic-resistant bacteria is a global health challenge. Due to their unique properties, metal oxide nanoparticles show promise in addressing this issue. However, optimizing these properties requires a deep understanding of complex interactions. This study incorporated data-driven machine learning to predict bacterial survival against lanthanum-doped ZnO nanoparticles. The effect of incorporation of lanthanum ions on ZnO was analyzed. Even with high lanthanum concentration, no significant variations in structural, morphological, and optical properties were observed. The antibacterial activity of La-doped ZnO nanoparticles against Gram-positive and Gram-negative bacteria was qualitatively and quantitatively evaluated. Nanoparticles induce 60%, 95%, and 55% bacterial death against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, respectively. Algorithms such as Multilayer Perceptron, K-Nearest Neighbors, Gradient Boosting, and Extremely Random Trees were used to predict the bacterial survival percentage. Extremely Random Trees performed the best among these models with 95.08% accuracy. A feature relevance analysis extracted the most significant attributes to predict the bacterial survival percentage. Lanthanum content and particle size were irrelevant, despite what can be assumed. This approach offers a promising avenue for developing effective and tailored strategies to reduce the time and cost of developing antimicrobial nanoparticles.

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Section: Resultsmentioning

confidence: 99%