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Record W2993678681

BLACK NANOFLUIDS FOR SOLAR ABSORPTION ON THE BASIS OF HYDROGEN PEROXIDE TREATED CARBON PARTICLES

2013· article· en· W2993678681 on OpenAlexvenueno aff
K Rumen, G.K. Christian, S Petko

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2013
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNanofluidMaterials scienceCarbon blackTransmittanceAbsorption (acoustics)Solar energyCarbon fibersAbsorbanceNanofluids in solar collectorsChemical engineeringAnalytical Chemistry (journal)NanoparticleComposite materialNanotechnologyChemistryOptoelectronicsChromatographyNatural rubber
DOInot available

Abstract

fetched live from OpenAlex

In this study the effect of carbon black nanoparticles concentration in water and propylene glycol based nanofluids on the solar energy absorbance has been studied. These nanofluids have been obtained by preliminary treatment of the used carbon particles with H 2 O 2 , heating and magnetic stirring. This pretreatment alters their wettability, sticking and surface adsorption, which permits the producing of stable nanofluids. Transmittance and extinction coefficients of these carbon black nanofluids have been estimated in the visible range. The optimal concentration of the carbon particles (0.2 g dm -3 for both dispersion media has been determined. The extinction coefficients for water-based fluids are slightly higher than those for the propylene glycol based ones. Their values slightly decrease with wavelength. Solar transmittance mean values in the visible spectrum indicate that the investigated fluids have a high potential for solar energy conversion. Such a good efficiency has also been established for solar radiation absorption in a wider wavelength range (200-2500 nm). Photothermal experiments of the studied carbon black nanofluids show a good temperature increase rate with solar irradiation time.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.159
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.003
Scholarly communication0.0000.003
Open science0.0020.000
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 head0.013
GPT teacher head0.268
Teacher spread0.256 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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