Probing the Relation Between Force—Lifetime—and Chemistry in Single Molecular Bonds
Bibliographic record
Abstract
On laboratory time scales, the energy landscape of a weak bond along a dissociation pathway is fully explored through Brownian-thermal excitations, and energy barriers become encoded in a dissociation time that varies with applied force. Probed with ramps of force over an enormous range of rates (force/time), this kinetic profile is transformed into a dynamic spectrum of bond rupture force as a function of loading rate. On a logarithmic scale in loading rate, the force spectrum provides an easy-to-read map of the prominent energy barriers traversed along the force-driven pathway and exposes the differences in energy between barriers. In this way, the method of dynamic force spectroscopy (DFS) is being used to probe the complex relation between force-lifetime-and chemistry in single molecular bonds. Most important, DFS probes the inner world of molecular interactions to reveal barriers that are difficult or impossible to detect in assays of near equilibrium dissociation but that determine bond lifetime and strength under rapid detachment. To use an ultrasensitive force probe as a spectroscopic tool, we need to understand the physics of bond dissociation under force, the impact of experimental technique on the measurement of detachment force (bond strength), the consequences of complex interactions in macromolecular bonds, and effects of multiply-bonded attachments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".