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Record W2724477417 · doi:10.1149/ma2017-02/8/658

Fluorescently Active Carbon Nanostructures from Neurotransmitter Family Precursors

2017· article· en· W2724477417 on OpenAlexaff
Vinayaraj Ozhukil Kollath, Francis D. Mayer, Thanmayee Mudigonda, Muhammad Naoshad Islam, Kunal Karan

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldChemistry
TopicFullerene Chemistry and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNanostructureDopaminePolymerizationMaterials scienceNanotechnologyChemical engineeringNorepinephrineChemistryOrganic chemistryPolymerBiology

Abstract

fetched live from OpenAlex

Three biologically significant molecules – dopamine, epinephrine and norepinephrine, all fall under the neurotransmitter category of molecules, upon liquid-phase polymerization gave rise to sphere, petal and nanodot shapes in a water-alcohol mixture under basic conditions. The size and uniformity of these nanostructures can be controlled by varying the reaction solvents. An extrapolation of size control in the case of dopamine nanospheres showed a minimum achievable diameter of 30 nm. Zetapotential measurements of the resulting nanostructure suspensions showed -40 mV, -18 mV and -11 mV respectively for dopamine, epinephrine and norepinephrine nanostructures. The current consensus on the formation mechanism of dopamine nanospheres in alkaline water-alcohol mixture cannot explain the nanostructures formed from epinephrine and norepinephrine. All three nanostructures showed inherent fluorescence, which makes them interesting candidates for shape and size controlled carbon nanostructures that can be employed for imaging structures in complex fluids. The resulted nanostructures were tested as precursor for carbon nanostructures, and characterized for structure integrity and fluorescence properties. The presentation will discuss the results of synthesis and characterization of basic properties.

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 categoriesMeta-epidemiology (narrow)
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 score0.316
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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