Adhesive force between graphene nanoscale flakes and living biological cells
Bibliographic record
Abstract
We report on a measurement technique that quantifies the adhesive force between multi-layers of graphene flakes and the cell wall of live Escherichia coli cells using atomic force microscopy (AFM) in-fluid Peak Force- Quantitative Nanomechanical Mapping mode. To measure the adhesive force, we made use of the negative charge of E. coli cells to allow them to stick to positively charged surfaces, such as glass or silicon, that were covered by poly-L-Lysine. With this approach, cells were held in place for AFM characterization. Both pristine graphene (PG) flakes and functionalized graphene (FG) flakes were put on the E. coli cells and measurements of lateral size, flake thickness, and adhesion were made. Using this approach, the measured values of the adhesive force between multi-layers of graphene flakes (total thickness of 50 nm) and E. coli was determined to be equal or greater than 431 ± 65pN for (PG) and 694 ± 98pN for the (FG). More interestingly, the adhesive force of a graphene flake (thickness 1.3 nm) with the cell is determined to be equal or greater than 38.2 ± 16.4pN for the (PG) and 34.8 ± 15.3pN for the (FG). These interaction values can play an important role in determining and understanding the possible toxicity of graphene flakes. Copyright © 2017 John Wiley & Sons, Ltd.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".