Binder jetting additive manufacturing of hydroxyapatite powders: Effects of adhesives on geometrical accuracy and green compressive strength
Bibliographic record
Abstract
Binder jetting additive manufacturing (AM) is a promising process to print hydroxyapatite (HA) powder into bone tissue implants. However, one challenge remaining is the poor reactivity between HA powder with standard water-based ink. This study investigated different water-soluble adhesives to increase the 3D printability of HA powder. Maltodextrin and polyvinyl alcohol (PVOH) with low and high molecular weight (MW) were blended with HA from 10 to 30 wt%. Powder characterisation and evaluation of the compressive properties and geometrical accuracy of the 3D printed scaffolds were performed to identify the optimal adhesive powder. This study adopted an image registration technique to quantify the geometrical accuracy of the final 3D printed scaffold in a more comprehensive and representative way than conventionally dimensional measurement. With these approaches, a highly promising binder jetting formulation has been developed via mixing HA powder with 30 wt% PVOH (high MW). Samples manufactured from this formulation successfully achieved a geometrical accuracy greater than 85% and an excellent green compressive strength of 5.63 ± 0.27 MPa, which was 500% higher than the commercial binder jetting powder. This is the first study to demonstrate a high level of printability when using a formulation containing ≥ 70 wt% HA powder and a water-based binder in the binder jetting AM process. Using the optimal powder composition developed in this study could potentially improve the structural, mechanical, and biological performances of HA-based 3D scaffolds manufactured using the binder jetting AM process for bone tissue engineering 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 | 0.001 |
| 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".