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Record W3184364740 · doi:10.3390/cryst11070832

Photoluminescent Properties of Hydroxyapatite and Hydroxyapatite/Multi-Walled Carbon Nanotube Composites

2021· article· en· W3184364740 on OpenAlexfundno aff
Edna X. Figueroa-Rosales, J. Martínez‐Juárez, Esmeralda García‐Díaz, Daniel Hernández‐Cruz, Sergio A. Sabinas-Hernández, M. J. Robles‐Águila

Bibliographic record

VenueCrystals · 2021
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersBenemérita Universidad Autónoma de PueblaConsejo Nacional de Ciencia y TecnologíaVicerrectoría de Investigación y Estudios de Posgrado, Benemérita Universidad Autónoma de PueblaElettra-Sincrotrone TriesteCanadian Light Source
KeywordsMaterials scienceCarbon nanotubePhotoluminescenceFourier transform infrared spectroscopyComposite materialComposite numberLuminescenceInfraredNanoparticleChemical engineeringNanomaterialsNanotechnologyOpticsOptoelectronics

Abstract

fetched live from OpenAlex

Hydroxyapatite (HAp) and hydroxyapatite/multi-walled carbon nanotube (MWCNT) composites were obtained by the co-precipitation method, followed by ultrasound-assisted and microwave radiation and thermal treatment at 250 °C. X-ray diffraction (XRD) confirmed the presence of a hexagonal phase in all the samples, while Fourier-transform infrared (FTIR) spectroscopy elucidated the interaction between HAp and MWCNTs. The photoluminescent technique revealed that HAp and the composite with non-functionalized MWCNTs present a blue luminescence, while the composite with functionalized MWCNTs, under UV-vis radiation shows an intense white emission. These findings allowed presentation of a proposal for the use of HAp and HAp with functionalized MWCNTs as potential materials for optoelectronic and medical 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2021
Admission routes1
Has abstractyes

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