ArticleJournal of microscopy2026
A drift-aware framework for reliability assessment and image-based interpretation of atomic force microscopy measurements.
Article in Journal of microscopy, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Drift is one of the primary sources of uncertainty limiting the accuracy, repeatability, and reliability of scanning probe microscopy (SPM) measurements, with a particularly significant effect on the comparison of consecutive surface topography images. In this study, a two-module hybrid physical-computational framework is presented for the drift-aware interpretation and reliability assessment of consecutive AFM topography measurements. The first module integrates deep learning (DL)-based inter-frame correspondence extraction, geometric validation, physical coordinate reconstruction, scan-order-based acquisition-time assignment, inter-scan delay correction, and temperature synchronisation to generate a time-labelled drift representation. The second module uses this structured representation for comparative data-driven modelling and evaluates drift velocity (
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