Tuning the Nanotopography and Chemical Functionality of 3D Printed Scaffolds through Cellulose Nanocrystal Coatings
Bibliographic record
Abstract
In nature, cells exist in three-dimensional (3D) microenvironments with topography, stiffness, surface chemistry, and biological factors that strongly dictate their phenotype and behavior. The cellular microenvironment is an organized structure or scaffold that, together with the cells that live within it, make up living tissue. To mimic these systems and understand how the different properties of a scaffold, such as adhesion, proliferation, or function, influence cell behavior, we need to be able to fabricate cellular microenvironments with tunable properties. In this work, the nanotopography and functionality of scaffolds for cell culture were modified by coating 3D printed materials (DS3000 and poly(ethylene glycol)diacrylate, PEG-DA) with cellulose nanocrystals (CNCs). This general approach was demonstrated on a variety of structures designed to incorporate macro- and microscale features fabricated using photopolymerization and 3D printing techniques. Atomic force microscopy was used to characterize the CNC coatings and showed the ability to tune their density and in turn the surface nanoroughness from isolated nanoparticles to dense surface coverage. The ability to tune the density of CNCs on 3D printed structures could be leveraged to control the attachment and morphology of prostate cancer cells. It was also possible to introduce functionalization onto the surface of these scaffolds, either by directly coating them with CNCs grafted with the functionality of interest or with a more general approach of functionalizing the CNCs after coating using biotin–streptavidin coupling. The ability to carefully tune the nanostructure and functionalization of different 3D-printable materials is a step forward to creating in vitro scaffolds that mimic the nanoscale features of natural microenvironments, which are key to understanding their impact on cells and developing artificial tissues.
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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) | 0.001 | 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".