Improvement of 3D printing quality for fabricating soft scaffolds
Bibliographic record
Abstract
Tissue engineering (TE) integrates methods of cells, engineering and materials to improve or replace biological functions of native tissues or organs. 3D printing technologies have been used in TE to produce different kinds of tissues. Based on review of the exiting 3D printing technologies used in TE, special requirements of fabricating soft scaffolds are identified. Soft scaffolds provide a microenvironment with biocompatibility for living cells proliferation. This research focuses on 3D printer design and printing parameters investigation for fabrication of soft scaffolds. A 3D printer is proposed for producing artificial soft scaffolds, with components of a pneumatic dispenser, a temperature controller and a multi-nozzle changing system. Relations of 3D printing parameters are investigated to improve the printing quality of soft scaffolds. It provides guidance for printing customized bio-materials with improved efficiency and quality. In the research, printing parameters are identified and classified based on existing research solutions. A deposition model is established to analyze the parameters relations. Quantitative criteria of parameters are proposed to evaluate the printing quality. A series of experiments including factors experiments and comparison tests are conducted to find effects of parameters and their interactions. A case study is conducted to verify the analytic solution of proposed models. This research confirms that the hydrogel concentration and nozzle diameters have significant effects on the filament diameter. Factor interactions are mainly embodied in between the concentration of hydrogel solutions and dispensing pressures. Besides filament diameters, the nozzle height and space also affect the printing accuracy significantly. An appropriate nozzle height is considered to be 1.4 times than the nozzle diameter, and a reasonable nozzle space is suggested from 2.0 to 2.5 times of the nozzle diameter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".