Modeling of the scaffold fabrication process for tissue engineering applications
Bibliographic record
Abstract
Biomaterial scaffolds in tissue engineering are used to provide structural templates for cell seeding and extracellular matrix formation. Currently, rapid prototyping (RP) with fluid dispensing technique has been widely used in the process to fabricate the scaffolds. In such a process, the flow rate of dispensed bio-materials and the porosity of fabricated scaffolds are critical to the tissue engineering applications, and can be affected by various factors, such as the operating conditions in the process, the flow properties of the bio-material, and the structural parameters of the dispensing system. Taking these influences into account, this paper presents the model development for scaffold fabrication process, so as that the flow rate of dispensed bio-materials and the porosity of fabricated scaffolds can be represented. Based on the developed models, simulations are carried out, with the objective to identify the influences on the fabrication process of such parameters as the driving pressure used to dispense fluid, the flow behaviour of bio-materials, and the structural parameters of the dispensing system. Finally, the conclusions from the present study are presented, along with the future work suggested.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".