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Record W4292212514 · doi:10.36959/326/774

Phosphate Determination in Aqueous Samples by Complex Formation with Rhodamine B

2022· article· en· W4292212514 on OpenAlexafffund
G Carolyn Gregory, Kumaragamage Darshani, Indraratne Srimathie, B Douglas Craig, M Douglas Goltz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Winnipeg
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Winnipeg
KeywordsRhodamine BPhosphateCapillary electrophoresisAqueous solutionRhodamineChemistryFluorescenceMolybdateElectrophoresisChromatographyElectrokinetic phenomenaFluorescence spectrometryAnalytical Chemistry (journal)Inorganic chemistryPhotocatalysisOrganic chemistryOptics

Abstract

fetched live from OpenAlex

In this study novel approaches for determining trace levels of phosphate in aqueous pore water solutions are described.Phosphate in aqueous samples (freshwater, drinking water, soil pore water) forms an aggregate compound with molybdate and Rhodamine B in 1M HCl.Incident light directed at the Rhodamine B molybdo-phosphate aggregate follows a second order light scattering behaviour.A bench top fluorescence spectrophotometer was used for examining the light scattering properties of the aggregate as well as for examining the feasibility of using this species for quantitative analysis.In this study this approach was adapted for quantitative analysis using capillary electrophoresis equipped with laser induced fluorescence.Quantifying phosphate with CE-LIF could be achieved by detecting the Rhodamine B phosphomolybdate aggregate directly in aqueous samples.Capillary electrophoresis was also used to indirectly detect phosphate by detecting Rhodamine B that dissociated from the aggregate species prior to electrokinetic injection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes2
Has abstractyes

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