Customization of an atomic force microscope for multidimensional measurements under environmental conditions
Bibliographic record
Abstract
Atomic force microscopy (AFM) is an analytical surface characterization tool that reveals the surface topography at a nanometer length scale while probing local chemical, mechanical, and even electronic sample properties. Both contact (performed with a constant deflection of the cantilever probe) and dynamic operation modes (enabled by demodulation of the oscillation signal under tip-sample interaction) can be employed to conduct AFM-based measurements. Although surface topography is accessible regardless of the operation mode, the resolution and the availability of the quantified surface properties depend on the mode of operation. However, advanced imaging techniques, such as frequency modulation, to achieve high resolution, quantitative surface properties are not implemented in many commercial systems. Here, we show the step-by-step customization of an atomic force microscope. The original system was capable of surface topography and basic force spectroscopy measurements while employing environmental control, such as temperature variation of the sample/tip, etc. We upgraded this original setup with additional hardware (e.g., a lock-in amplifier with phase-locked loop capacity, a high-voltage amplifier, and a new controller) and software integration while utilizing its environmental control features. We show the capabilities of the customized system with frequency modulation-based topography experiments and automated voltage and/or distance spectroscopy, time-resolved AFM, and two-dimensional force spectroscopy measurements under ambient conditions. We also illustrate the enhanced stability of the setup with active topography and frequency drift corrections. We believe that our methodology can be useful for the customization and automation of other scanning probe systems.
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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".