Dextran and Its Derivatives: Biopolymer Additives for the Modulation of Vaterite CaCO<sub>3</sub> Crystal Morphology and Adhesion to Cells
Bibliographic record
Abstract
Abstract Nowadays, a great demand for the development of novel drug delivery systems with high potential for bench‐to‐market transition attracts scientific attention toward materials that are already approved for biomedical use. Here, controlled fabrication of hybrid organic inorganic mesoporous crystals is realized in physiologically relevant conditions by co‐synthesis of vaterite CaCO3 in the presence of dextran (DEX) or its functional derivatives. The effects of DEX molecular weight and chemical structure on morphology, porosity, and stability of the hybrids are investigated. Molecular weight of DEX does not affect the crystal growth but leads to the partial blocking of crystal pores. Co‐synthesis of DEX functionalized with either carboxymethyl (CM) or diethylaminoethyl (DEAE) groups drastically increased crystal porosity without influencing crystal size. pH‐dependent vaterite‐to‐calcite recrystallization is significantly suppressed by inclusion of carboxymethyl‐dextran (CM‐DEX), making vaterite crystals stable in acidic medium, whereas the incorporation of diethylaminoethyl‐dextran (DEAE‐DEX) has no effect. The hybrids prepared with charged DEX derivatives possess stronger adhesion to normal human dermal fibroblasts: three times higher crystal adherence compared to pristine crystals. These results provide fundamental physical–chemical insights into the crystallization of DEX/vaterite hybrids and are discussed in view of the potential of these functional delivery carriers for biomedical and other 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 | 0.000 | 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) | 0.000 | 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".