Tip treatment for subnanoscale atomic force microscopy in liquid by atomic layer deposition Al2O3 coating
Bibliographic record
Abstract
Atomic force microscopy (AFM) allows direct imaging of atomic- or molecular-scale surface structures in liquid. However, such subnanoscale measurements are often sensitive to the AFM tip properties. To overcome this problem, 30 nm Si-sputter coating was proposed, and its effectiveness in improving stability and reproducibility has been demonstrated in atomic-scale imaging of various materials. However, this method involves tip blunting, enhancing the tip-induced dilation effect. As an alternative method, here we investigate atomic layer deposition (ALD) Al2O3-coating, where the film thickness is atomically well-controlled. Our transmission electron microscopy, contact angle and force curve measurements consistently suggest that as-purchased tips are covered with organic contaminants, and the initial 20 cycles gradually remove them, reducing the tip radius (Rt) and hydrophobicity. Further deposition increases Rt and hydrophilicity and forms an intact Al2O3 film over 50 cycles. We compared 50-cycle ALD-coated tips with 30 nm Si-sputter-coated tips in imaging mica and chitin nanocrystals (NCs). On mica, ALD coating gives slightly less stability and reproducibility in hydration force measurements than the Si sputter coating, yet they are sufficient in atomic-scale imaging. In imaging chitin NCs, ALD-coated tips give a less tip-induced dilation effect while maintaining molecular-scale imaging capability. We also found that 10-cycle-ALD coated tips covered with carbon give a better resolution and reproducibility in observing subnanoscale features at chitin NC surfaces. This result and our experience empirically suggest carbon-coated tips' effectiveness in observing carbon-based materials.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".