Improved Single Molecule Force Spectroscopy Using Micromachined Cantilevers
Bibliographic record
Abstract
Enhancing the short-term force precision of atomic force microscopy (AFM) while maintaining excellent long-term force stability would result in improved performance across multiple AFM modalities, including single molecule force spectroscopy (SMFS). SMFS is a powerful method to probe the nanometer-scale dynamics and energetics of biomolecules (DNA, RNA, and proteins). The folding and unfolding rates of such macromolecules are sensitive to sub-pN changes in force. Recently, we demonstrated sub-pN stability over a broad bandwidth (Δf = 0.01-16 Hz) by removing the gold coating from a 100 μm long cantilever. However, this stability came at the cost of increased short-term force noise, decreased temporal response, and poor sensitivity. Here, we avoided these compromises while retaining excellent force stability by modifying a short (L = 40 μm) cantilever with a focused ion beam. Our process led to a ∼10-fold reduction in both a cantilever's stiffness and its hydrodynamic drag near a surface. We also preserved the benefits of a highly reflective cantilever while mitigating gold-coating induced long-term drift. As a result, we extended AFM's sub-pN bandwidth by a factor of ∼50 to span five decades of bandwidth (Δf ≈ 0.01-1000 Hz). Measurements of mechanically stretching individual proteins showed improved force precision coupled with state-of-the-art force stability and no significant loss in temporal resolution compared to the stiffer, unmodified cantilever. Finally, these cantilevers were robust and were reused for SFMS over multiple days. Hence, we expect these responsive, yet stable, cantilevers to broadly benefit diverse AFM-based studies.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".