Simulation Assisted Nanoscale Imaging of Single Live Cells with Scanning Electrochemical Microscopy
Bibliographic record
Abstract
Abstract Nanoelectrodes have become an area of significant interest in recent years, which provide a number of advantages for imaging with scanning electrochemical microscopy (SECM). Since the resolution of SECM imaging is directly dependent on the size of the electrode probe, the reduced surface area of nanoelectrodes allows for the imaging of smaller sample features, or more localized electrochemical reactivity. Nanoelectrodes with a radius of 130 nm are employed to image the surface of single live cells. The use of nanoscale imaging, however, introduces additional complexity into the simulation modeling of the cell surface geometry and electrochemical reactivity. The creation of tailored simulation models accounting for these specific physical conditions is utilized to overcome the additional challenges to the characterization of the electrochemical system. Methodologies for the experimental mapping and creation of 3D simulation models of single live cells have been well developed, which are presented herein. These developments include characterization of cell surface topography, tip‐to‐cell distance, as well as cell membrane permeability quantification. The advanced quantification of the complex nanoscale imaging of single live cells assisted by theoretical simulations provides increased versatility to SECM as an already powerful bioanalytical tool.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".