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Record W2888818829 · doi:10.1002/slct.201801544

pH‐Switchable Water‐Soluble Boron Nitride Nanotubes

2018· article· en· W2888818829 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemistrySelect · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBoron nitrideSolubilityAqueous solutionBoronBromineHydrogen bondYield (engineering)ChemistryInorganic chemistryMaterials scienceChemical engineeringOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Abstract The first example of aqueous solutions of boron nitride nanotubes (BNNTs) with switchable stability and without use of surfactants is demonstrated. The as‐produced boron nitride nanotubes are both purified and solubilized through an environmentally friendly and scalable approach using water extraction and liquid bromine treatment. This treatment effectively removes elemental boron particles and excess bromine reacts with BNNTs, inducing B−N bond cleavage on the BNNT surface to yield hydroxyl (OH) and amino (NH 2 ) functionalized BNNTs. The resulting functionalized BNNTs form stable aqueous solutions around neutral pH (4 < pH < 8), but readily precipitate in other pH ranges. This switchable solubility is explained in terms of the capability of the functional groups (OH and NH 2 ) to form hydrogen‐bonding networks in water. Further, the solubility differences in selected polar organic solvents have been utilized to prepare functionalized BNNTs with higher purity.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.014
Threshold uncertainty score0.999

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)0.0030.002

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 head0.265
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