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Record W4212855290 · doi:10.1139/cjc-2021-0339

Synthesizing luminescent carbon from condensed tobacco smoke: bio-waste for possible bioimaging

2022· article· en· W4212855290 on OpenAlexvenueno aff
Tanushree Ghosh, Suchita Kandpal, Chanchal Rani, Devesh K. Pathak, Manushree Tanwar, Shweta Jakhmola, Hem Chandra Jha, Maxim Maximov, Anjali Chaudhary, Rajesh Kumar

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPhotoluminescenceRaman spectroscopyLuminescenceChemistrySpectroscopyAnalytical Chemistry (journal)Fluorescence spectroscopyQuantum yieldCarbon fibersPhotoluminescence excitationScanning electron microscopeMicroscopyFluorescencePhotochemistryOptoelectronicsMaterials scienceOrganic chemistryOpticsComposite material

Abstract

fetched live from OpenAlex

Used cigarette filters, a waste material and a major source of land pollution, were used as raw material to study the nature of condensed tobacco smoke (tar) using microscopy, optical, IR, photoluminescence, and Raman spectroscopy, as well as X-ray diffraction and electron and fluorescence microscopy. The tar present in the cigarette filter bud was used to synthesize luminescent low dimensional carbon using a simple methanol extraction technique. The collected material shows light blue emission under UV excitation with emission peak energy depending strongly on the excitation wavelength. Such excitation energy dependent emission is observed from the extract solution and the dried film. Careful analysis was carried out to understand its origin, which revealed the presence of a giant red-edge effect in the samples. A correlation between room temperature photoluminescence spectroscopy and fluorescence microscopy was carried out. The presence of amorphous phase carbon was established using Raman spectroscopy, and a quantum yield of more than 9% was estimated, which was moderately high in comparison with the one shown by carbon dots prepared by using other sources and can be used for bioimaging 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 categoriesInsufficient payload (model declined to judge)
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 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 studies0.0000.000
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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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
Published2022
Admission routes1
Has abstractyes

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