Deconvolution of dissipative pathways for the interpretation of tapping-mode atomic force microscopy from phase-contrast
Bibliographic record
Abstract
Abstract Phase-contrast in tapping-mode atomic force microscopy (TM-AFM) results from dynamic tip-surface interaction losses which allow soft and hard nanoscale features to be distinguished. So far, phase-contrast in TM-AFM has been interpreted using homogeneous Boltzmann-like loss distributions that ignore fluctuations. Here, we revisit the origin of phase-contrast in TM-AFM by considering the role of fluctuation-driven transitions and heterogeneous loss. At ultra-light tapping amplitudes <3 nm, a unique amplitude dependent two-stage distribution response is revealed, alluding to metastable viscous relaxations that originate from tapping-induced surface perturbations. The elastic and viscous coefficients are also quantitatively estimated from the resulting strain rate at the fixed tapping frequency. The transitional heterogeneous losses emerge as the dominant loss mechanism outweighing homogeneous losses at smaller amplitudes for a soft-material. Analogous fluctuation mediated phase-contrast is also apparent in contact resonance enhanced AFM-IR (infrared), showing promise in decoupling competing thermal loss mechanisms via radiative and non-radiative pathways. Understanding the loss pathways can provide insights on the bio-physical origins of heterogeneities in soft-bio-matter e.g., single cancer cell, tumors, and soft-tissues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".