Validation of a μ-volume sample holder for non-destructive and contactless assessment of the mechanical properties in soft biomaterials
Bibliographic record
Abstract
The mechanical characterization of soft biomaterials, like hydrogels, plays a key role for the successful development of new biomimetic constructs for numerous bioengineering and biomedical fields. Conventionally, mechanical properties are assessed using destructive approaches, including rheometer, which are weakly adapted to biomedical samples, mainly due to the lack of possibility to carry out in situ tests to follow gelation process. Recently, a non-destructive and contactless approach, named viscoelastic testing of bilayered materials (VeTBiM), was reported to measure the viscoelastic properties of soft hydrogels using low amplitude vibration. However, the high volumes required to obtain reliable results is considered a drawback for those applications in regenerative medicine demanding rare, highly purified and high-cost biomolecules. In this work, a new μ-volume sample holder is presented as cost-effective and time-efficient tool for the assessment of the mechanical properties of biomolecules-rich gels involving complex and time-consuming preparation. The new sample holder has been validated by comparing the data obtained on various gelatin and Pluronic F-127 hydrogels with the data from the conventional rotational rheometer. The results show the potential of the new sample holder in the characterization of viscoelastic materials through a contactless approach. Furthermore, this study contributes to confirm the versatility of VeTBiM for the future development of soft hydrogels for tissue engineering and regenerative medicine applications.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".