Super-resolution imaging with nanopipettes
Bibliographic record
Abstract
Uncovering structural information of biological systems at the nanoscale is vital for understanding their dynamics and function. Nanoscale imaging techniques that obtain structural information down to the single-molecule level under physiologically relevant conditions and without affecting the fragile structure of biomaterials are limited. Thus, the realization of such techniques is highly attractive, especially within the biological sciences. Nanopipette-based imaging using scanning ion conductance microscopy (SICM) fulfills these requirements, but resolution limitations and artefact formation hinder obtaining accurate structural information on the scale comparable to the pipette tip. Here, we present a novel technique, super-resolution SICM (SR-SICM), based on image deconvolution using simulated pipette point-spread functions. The technique is demonstrated on different types of nanostructures, where it surpasses the lateral resolution limit of SICM and mitigates imaging artefacts considerably. SR-SICM is applicable to any SICM dataset through user-friendly downloadable software promoting the possibility of single-molecule studies on a routine basis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".