Glass, Ceramic, Polymeric, and Composite Scaffolds with Multiscale Porosity for Bone Tissue Engineering
Bibliographic record
Abstract
Porosity affects performance of scaffolds for bone tissue engineering both in vitro and in vivo. Macropores (i.e., pores with a diameter >100 μm) are essential for cellular infiltration; micropores (i.e., pores with a diameter of 1–10 μm) promote cell adhesion and facilitate nutrient absorption. Scaffolds containing both macropores and micropores exploit the advantages of both pore sizes and have excellent osteogenic properties. Nanopores (i.e., pores with a diameter of 1–50 nm) can be included as well, to improve cell–material interactions by further enhancing the surface area of the scaffold. This article reviews fabrication techniques and properties of scaffolds with multiscale porosity, focusing on glass, ceramic, polymeric, and composite scaffolds. After discussing the structure of bone and how it inspired scaffolds for bone tissue engineering, pore nomenclature is introduced. Then, the techniques used to induce multiscale porosity, the nature of the pores created, and the effects of scaffold porosity on mechanical properties and biological activity of the scaffolds are discussed. The review concludes by providing an outlook for this field, including advancements that are made possible by computational modeling and artificial intelligence.
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 | 0.001 | 0.001 |
| 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".