Image Analysis of Structured Surfaces for Quantitative Topographical Characterization
Bibliographic record
Abstract
In the fields of functional materials, interfacial chemistry, and microscale devices, surface structuring provides an opportunity to engineer materials with unique tunable properties such as wettability, anti-fouling, crack propagation, and specific surface area. Often, the resulting properties are related to the feature sizes of the structured surfaces and therefore, it is necessary to accurately quantify these topographies. This work presents a step-by-step description of a method for the quantification of the size of periodic structures using 2D discrete Fourier Transform analysis coupled with data filtering techniques to optimize feature size extraction and reduce user bias and error. The method is validated using artificial images of periodic patterns as well as scanning electron microscopy images of gold films that are structured on different substrates. While image Fourier Transform has been used previously and is a built-in feature in some commercial and open-source image analysis software, this work details image pre-processing and feature extraction steps, and how to best apply them, which has not been described in detail elsewhere. This method can analyze engineered or natural periodic topographies (e.g., wrinkles) to enable the design of patterned materials for applications including photovoltaics, biosensors, tissue engineering, flexible electronics, and thin film metrology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".