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Record W2738358211 · doi:10.1139/cjc-2017-0144

One-dimensional hydroxyapatite materials: preparation and applications

2017· article· en· W2738358211 on OpenAlexvenueno aff
Bing‐Qiang Lu, Ying‐Jie Zhu

Bibliographic record

VenueCanadian Journal of Chemistry · 2017
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.209
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2017
Admission routes1
Has abstractyes

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