Effect of ionic strength on shear-thinning nanoclay–polymer composite hydrogels
Bibliographic record
Abstract
Nanoclay-polymer shear-thinning composites are designed for a broad range of biomedical applications, including tissue engineering, drug delivery, and additive biomanufacturing. Despite the advances in clay-polymer injectable nanocomposites, colloidal properties of layered silicates are not fully considered in evaluating the in vitro performance of shear-thinning biomaterials (STBs). Here, as a model system, we investigate the effect of ions on the rheological properties and injectability of nanoclay-gelatin hydrogels to understand their behavior when prepared in physiological media. In particular, we study the effect of sodium chloride (NaCl) and calcium chloride (CaCl2), common salts in phosphate buffered saline (PBS) and cell culture media (e.g., Dulbecco's Modified Eagle's Medium, DMEM), on the structural organization of nanoclay (LAPONITE® XLG-XR, a hydrous lithium magnesium sodium silicate)-polymer composites, responsible for the shear-thinning properties and injectability of STBs. We show that the formation of nanoclay-polymer aggregates due to the ion-induced shrinkage of the diffuse double layer and eventually the liquid-solid phase separation decrease the resistance of STB against elastic deformation, decreasing the yield stress. Accordingly, the stress corresponding to the onset of structural breakdown (yield zone) is regulated by the ion type and concentration. These results are independent of the STB composition and can directly be translated into the physiological conditions. The exfoliated nanoclay undergoes visually undetectable aggregation upon mixing with gelatin in physiological media, resulting in heterogeneous hydrogels that phase separate under stress. This work provides fundamental insights into nanoclay-polymer interactions in physiological environments, paving the way for designing clay-based injectable biomaterials.
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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) | 0.001 | 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".