Research advances and future perspectives of zinc‐based biomaterials for additive manufacturing
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) of zinc‐based biodegradable materials is a hot research topic, especially for bone‐scaffold applications, because of the moderate degradation rate, good biocompatibility, and suitable mechanical properties of these materials. Furthermore, AM enables the fabrication of complex internal structures suitable for implants. Literature on the AM of degradable zinc‐based biomaterials from the Web of Science Core Collection was evaluated in this review. The bibliometric tool CiteSpace was used to analyze historical characteristics, evolving research topics, and emerging trends in this field. Our research results predict that the composition, processing techniques, in vitro biocompatibility, and manufacturing quality of biodegradable AM zinc‐based materials will continue to be hot topics in recent years. To address implant requirements, particularly for bone‐repair materials, the mechanical properties of materials (including the resistance to degradation, creep, and aging), degradation rates, in‐vivo biocompatibility, and specialized processing techniques that affect these properties (such as coating processes, heat treatments, material surface structures, and microstructural compositions) will become hot research topics in the future. We propose future research directions based on an in‐depth analysis of four main topics of AM biodegradable zinc‐based materials (manufacturing quality, material composition, unit configuration, and biocompatibility). The findings provide important guidance for future theoretical research and industrial development of AM zinc‐based biomaterials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".