Single-Molecule Bonds Characterized by Solid-State Nanopore Force Spectroscopy
Bibliographic record
Abstract
Weak molecular interactions drive processes at the core of living systems, such as enzyme-substrate interactions, receptor-ligand binding, and nucleic acid replication. Single-molecule force spectroscopy is a remarkable tool for revealing molecular scale energy landscapes of noncovalent bonds, by exerting a mechanical force directly on an individual molecular complex and tracking its survival as a function of time and applied force. In principle, force spectroscopy methods can also be used for highly specific molecular recognition assays, by directly characterizing the strength of bonds between probe and target molecules. However, complexity and low throughput of conventional force spectroscopy techniques render such biosensing applications impractical. Here we demonstrate a straightforward single-molecule approach, suitable for both biophysical studies and molecular recognition assays, in which a approximately 3 nm silicon nitride nanopore is used to determine the bond lifetime spectrum of the biotin-neutravidin complex. Thousands of individual molecular complexes are captured and dissociated in the solid-state nanopore under constant applied forces, ranging from 400 to 900 mV, allowing us to extract the location of the energy barrier that governs the interaction, mapped at Deltax approximately 0.5 nm. These results highlight the capacity of a solid-state nanopore to detect and characterize intermolecular interactions and demonstrate how this could be applied to rapid, highly specific molecular detection assays.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".