Experimental methods in chemical engineering: Atomic force microscopy − AFM
Bibliographic record
Abstract
Abstract Atomic force microscopy (AFM), part of the scanning probe microscopy family, exploits the local interaction forces between the sharp tip of a scanning mechanical probe and a material sample to profile its surface topography or map mechanical and tribological properties over nanometre to micron length scales. AFM images the topography at a sub‐angstrom resolution in height and sub‐nanometre to nanometre lateral resolution for diverse materials spanning extremely soft biological samples to hard metals in air, fluid, or vacuum. The accuracy of dimensional metrology measurements depends on the probe tip radius and geometry, calibration of the piezoelectric scanner movement, and applied force. A number of experimental aspects must be considered to ensure imaging reproducibility and maximize imaging resolution. These considerations include sample preparation, imaging environment, choice of AFM mode and probe, and imaging parameters. AFM offers specialized modes to characterize materials properties such as surface potential, electrochemical reactivity, and electrical and magnetic properties. Recent advances combine AFM and infrared spectroscopy to simultaneously map the surface topography and distribution of chemical species. High‐speed or fast‐scanning AFM captures dynamic structural changes and (bio)molecular processes occurring on the millisecond time scale. Every year, Web of Science indexes over 3200 articles that appear when adding AFM and microscopy in the search field topic. A bibliometric analysis grouped AFM research into five clusters: (1) mechanical properties, membranes, and adhesion, (2) morphology, microstructure, and roughness, (3) spectroscopy, nanocomposite, and oxide, (4) adsorption and steel, and (5) polymer and wettability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".