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Record W2885091521 · doi:10.1021/acsanm.8b00765

Mechanism of Hierarchical Porosity Development in Hexagonal Boron Nitride Nanocrystalline Microstructures for Biomedical and Industrial Applications

2018· article· en· W2885091521 on OpenAlexafffund
Jose Humberto Ramirez Leyva, Gerardo Vitale, Afif Hethnawi, Azfar Hassan, M.J. Pérez-Zurita, Guillermo U. Ruiz‐Esparza, Nashaat N. Nassar

Bibliographic record

VenueACS Applied Nano Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Calgary
FundersMitacsConsejo Nacional de Ciencia y Tecnología
KeywordsPorosityMaterials scienceNanocrystalline materialCeramicMicrostructureBoron nitrideNanotechnologyvan der Waals forceChemical engineeringComposite materialChemistryOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

A well-known ceramic material, hexagonal boron nitride (h-BN), has a number of unique properties, including structural and porosity features, that make it suitable for a wide range of industrial applications. Hierarchical porosity and high specific surface area are desirable properties for adsorption processes such as water and air cleaning, hydrogen storage, and drug delivery. These characteristics could be controlled and optimized by synthesis procedures; however, this process requires an understanding of the factors and mechanisms of nanocrystalline h-BN porosity development and textural properties. In this study, we demonstrate that hierarchical porosity displays evidence of the consecutive h-BN synthesis steps and thermal decomposition of intermediants. In addition, evidence shows that h-BN nanosheets can be folded as a result of van der Waals force interactions at elevated temperatures, which is corroborated by a computational modeling. The biocompatibility of the prepared h-BN was also evaluated to confirm the nontoxicity of the material. The results of this research could aid in the optimization and scaling up of an environmentally friendly h-BN synthesis process and assist in the development of new methods for the production of h-BN at a commercial level.

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)0.001
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 score0.016
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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