Advanced AFM & Fractal/Texture Analytics for Biointerfaces

Designing high-performance biointerfaces, surfaces that interact with cells, proteins, microbes, and fluids, requires descriptors that capture structure across nano- to microscale. Conventional metrics (e.g., average roughness) compress rich topographies into single numbers and often fail to predict adhesion, mechanotransduction, biofouling, wetting, antimicrobial action, or sensing responses. Atomic force microscopy (AFM) and 3D profilometry now provide high-fidelity height maps and force data, enabling quantitative analysis of surface complexity. Fractal and texture analytics, such as multifractal spectra, lacunarity, spatial autocorrelation indices, topographical entropy, and power spectral density slopes—offer multiscale, orientation-aware fingerprints that better relate morphology to function. Yet, challenges remain: artifact control, scale selection, reproducible pipelines, and standardized reporting. This Research Topic addresses these gaps by uniting methods and applications that link advanced AFM/texture descriptors to measurable biointerface performance in biomedical, biotechnological, and environmental contexts.

This Research Topic aims to close the gap between descriptive surface metrics and actionable design rules for high-performance biointerfaces. The central problem is that conventional roughness parameters and ad hoc image processing rarely predict biological outcomes such as cell adhesion, biofilm formation, wetting, antimicrobial efficacy, or sensing performance. We seek contributions that (i) establish robust, multiscale descriptors, multifractal spectra, lacunarity, spatial autocorrelation indices, topographical entropy, PSD slopes, and (ii) demonstrate causal or strongly correlative links to function using rigorous experiments and statistics. Building on recent advances in AFM (high-speed, multifrequency, force-mapping, in situ liquid imaging) and open computational toolchains, we encourage studies that standardize preprocessing, quantify uncertainty, and validate on independent datasets or reference surfaces. Targeted themes include multimodal fusion (AFM with spectroscopy/electrochemistry/microfluidics), interpretable machine learning for feature selection, and benchmarking frameworks that enable cross-study comparability. By promoting reproducible pipelines, FAIR data, and clear reporting standards, this Topic will translate advanced fractal/texture analytics into reliable predictors and controllers of biointerface performance in biomedical, biotechnological, and environmental settings.

This Research Topic welcomes contributions that couple AFM/3D profilometry with advanced fractal and texture analytics to explain and predict biointerface performance. Themes include:

• Multiscale descriptors (multifractal spectra, lacunarity, Moran’s index, topographical entropy, PSD/variogram analysis, GLCM features, fractal succolarity)
• Methodology and metrology (tip-convolution control, filtering/segmentation choices, calibration, uncertainty quantification, reporting standards)
• Multimodal integration (AFM with mechanical/electrical/optical assays, microfluidics, in situ/operando conditions)
• Data-centric pipelines (feature selection, interpretable ML, open datasets/benchmarks, FAIR practices)
• Application studies on antimicrobial/antifouling coatings, tissue-engineering scaffolds, biosensing/bioelectronics, and bio-relevant catalytic/photocatalytic surfaces.