Development of a Low-Cost Surface Characterization Prototype for Solid and Liquid Samples
Bibliographic record
Abstract
Atomic force microscopy (AFM) and electron microscopy (EM) are widely used for solid surface characterization at micro and nano scale, but their high cost, operational maintenance, and sometimes destructive nature limit accessibility, especially in resource-constrained environments. These limitations highlight the need for affordable, portable, and easy to maintain alternatives. Moreover, liquid sample surface analysis is also an emerging necessity in fields like water quality assessments and studies. Optical techniques combined with image-based analysis offer accurate measurements without the high costs of AFM or EM. By enabling reflectance-based imaging, such systems can also support environmental monitoring, providing non-invasive, quantitative analysis of pollutants. In this work, we present a low-cost optical prototype for surface characterization based on Fresnel interference analysis. The system integrates a diode laser, a CCD sensor, and an automated XY scanning platform to perform multi-angle reflectance measurements. The proposed non-invasive method was experimentally validated on TiO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> thin films, and its potential application for monitoring contaminants in water bodies is discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".