ArticleMaterials (Basel, Switzerland)2026
Predictive Neural Network Modeling of Nanoporous Anodic Alumina for Controlled Drug Release Implants: An Integrated Machine Learning Approach.
Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundNanoporous anodic alumina (NAA) has emerged as a promising platform for localized drug delivery in biomedical implants owing to its tunable nanoscale pore structure and biocompatibility. However, achieving the desired pore characteristics currently relies on time-consuming trial-and-error adjustments of anodization parameters.
methodsWe developed a comprehensive data-driven machine learning framework using a feed-forward artificial neural network (ANN) with three hidden layers (64-32-16 neurons) trained on 77 samples from a compiled dataset of 99 anodization experiments spanning 1995-2025. The model predicts the NAA pore diameter based on anodization conditions (electrolyte type, concentration, voltage, temperature, and time).
resultsThe ANN achieved R
conclusionsThis data-driven approach offers a powerful tool to reduce experimental iteration and accelerate the development of advanced drug-delivery implants by enabling the rational design of NAA pore structures for optimized drug loading and release kinetics.
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