Optimizing the methodology for accurate and accessible slip length measurement with atomic force microscopy
Bibliographic record
Abstract
The remarkable performance of cooling devices employing nano- and microscale channels has drawn considerable interest, highlighting the need for surfaces with large slip lengths to improve their efficiency. However, the large errors in slip length associated with existing techniques hinder a clear understanding of slip phenomena. In this paper, we evaluate existing analytical methods for slip length measurement with atomic force microscopy (AFM) and propose a new reliable method. We performed force curve measurements on mica, silica and highly ordered pyrolytic graphite surfaces in water using an AFM equipped with a colloidal probe. The obtained force curves were analyzed through three methods: two commonly utilized procedures, namely the recursive and intercept methods, and a novel one called the two-parameter method which we developed. Our analyses showed that the recursive method yielded slip lengths with relatively large errors, fluctuation of ±5.8 nm, which were due to inaccuracies in the cantilever's spring constant and the fluid viscosity. On the other hand, it was found that the intercept method leads to restrictions on the choice of fitting range because of the simplified formula for viscous drag. As a result, by altering the data range, the calculated slip length shows significant variations within the ranges of ±27.5 nm. The two-parameter method, unlike the standard ones, overcome these drawbacks. This method requires no pre-measured parameters, and the slip length fluctuation is independent of the fitting range and only ±3.6 nm, which is around 2/3 of that observed in the recursive method and 1/8 of that in the intercept method. Our study optimizes existing analytical protocols and offers a new way for accessible and reliable calculations of slip lengths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".