Data driven approach for topological data of polyelectrolytes assembly

Le résumé fourni par la source

Layer-by-layer (LbL) assembly of polyelectrolytes is a universal method for controlling composite coatings. When combined with atomic force microscopy (AFM), it is useful strategy to analyze morphology. Optimisation of the LbL technique is bottlenecked by manual sample preparation, and there is also a lack of standardised analytical pipelines to convert characterisation data into machine-readable descriptors. We address both bottlenecks by combining a collaborative robotic dip-coating platform with an automated feature-extraction backend for AFM topography. The robotic arm handles the substrate (Si wafer), immerses it in polyelectrolyte solution, and prepares samples for AFM study without operator intervention. Resulting AFM scans are transferred into a reproducible pipeline that applies a logged preprocessing chain and computes twelve groups of physically interpretable descriptors per sample: ISO 25,178 areal roughness, height-distribution statistics, radial power spectrum with Hurst and fractal exponents, two-dimensional autocorrelation, local-patch minima/maxima distributions, persistent homology of patch point clouds, gliding-box lacunarity, and a recipe-derived block of layer composition, sequence k-grams and RDKit monomer descriptors. Every descriptor is stored with the originating method version and parameters, supporting idempotent recomputation and side-by-side methodological evolution. The pipeline was applied to polyethyleneimine (PEI)/polystyrene sulfonate (PSS) assemblies on Si substrates, and an unsupervised principal component projection of the scalar descriptors already separates samples by the number of deposited layers, demonstrating that the resulting feature vectors carry coating-state information at a level useful for downstream machine learning. This work establishes a traceable, data‑driven bridge from synthesis instructions to high‑dimensional surface descriptors, demonstrating for the first time an end‑to‑end pipeline where robotic LbL assembly, versioned AFM processing, and hybrid topological‑chemical feature extraction converge into a single machine‑learning‑ready dataset.

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