Additive manufacturing of sustainable biomaterials for biomedical applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biopolymers are promising environmentally benign materials applicable in multifarious applications. They are especially favorable in implantable biomedical devices thanks to their excellent unique properties, including bioactivity, renewability, bioresorbability, biocompatibility, biodegradability and hydrophilicity. Additive manufacturing (AM) is a flexible and intricate manufacturing technology, which is widely used to fabricate biopolymer-based customized products and structures for advanced healthcare systems. Three-dimensional (3D) printing of these sustainable materials is applied in functional clinical settings including wound dressing, drug delivery systems, medical implants and tissue engineering. The present review highlights recent advancements in different types of biopolymers, such as proteins and polysaccharides, which are employed to develop different biomedical products by using extrusion, vat polymerization, laser and inkjet 3D printing techniques in addition to normal bioprinting and four-dimensional (4D) bioprinting techniques. This review also incorporates the influence of nanoparticles on the biological and mechanical performances of 3D-printed tissue scaffolds. This work also addresses current challenges as well as future developments of environmentally friendly polymeric materials manufactured through the AM techniques. Ideally, there is a need for more focused research on the adequate blending of these biodegradable biopolymers for achieving useful results in targeted biomedical areas. We envision that biopolymer-based 3D-printed composites have the potential to revolutionize the biomedical sector in the near future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it