Correlating topography and viscoelastic properties of Elastin-Like Polypeptide scaffolds probed at the nanoscale: Intermodulation Atomic Force Microscopy
Bibliographic record
Abstract
The synthesis and property characterization of soft biomaterials has taken precedence in recent years. Although bulk physical-chemical properties are well known for these bio-materials, nanoscale properties still need to be probed and evaluated to fine tune the bio-compatibility (structural as well as functional) with natural tissues for regenerative medicine, prosthetics and other biological applications. In this study, we focus on a popular soft biomaterial, ELastin-like polypeptide (ELP) which has been prepared under different pH conditions. We explore the topographical features of the ELP at the nanoscale using Atomic Force Microscopy (AFM). Additionally, we employ a non linear mode of AFM called Intermodulation-AFM (ImAFM) to correlate the elastic properties (Young's modulus) of ELP probed at the nanoscale with the topographical features which gives us a deep insight into the mechanical properties offered by ELP when the structural features are altered by change in the ELP synthesis conditions. The noteworthy point is that we measure theses properties at a spatial resolution of 0.9 nm. Finally, we explain the change in the structural features of ELP with varying pH through atomistic Molecular Dynamics Simulations. We follow the interaction mechanisms of the amino acid sequences and crosslinkers with proteins as they form the backbone and sidechain of the ELP at different pH.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".