One-dimensional hydroxyapatite materials: preparation and applications
Bibliographic record
Abstract
As one of the biominerals, hydroxyapatite (HAP) plays important roles in biology, and inspires researchers to investigate HAP-based materials for the applications in various biomedical fields. Among them, one-dimensional (1-D) micro-/nanostructured HAP materials have attracted great interest in the last decades. This review summarizes the preparation and applications of 1-D HAP materials, and discusses different aspects of 1-D HAP materials. Various synthetic methods have been developed to prepare 1-D HAP materials with different morphologies, sizes, surface properties and crystallinities. In addition, elements-substituted 1-D HAP materials and composites have also been prepared. Surfactants and additives are usually adopted to control the nucleation and growth of 1-D HAP materials, but the related mechanisms are not very clear yet. The applications of 1-D HAP materials have been widely investigated, and the biomedical applications show great prospect but still need further improvements. A new kind of highly flexible fire-resistant inorganic paper made of ultralong HAP nanowires has been developed and is a promising alternative of the traditional cellulose paper for valuable archives and important documents. Regardless of the advances, further studies should be made for preparing 1-D HAP materials with controlled structures, sizes and morphologies and for boosting their various 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".