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Record W4392928187 · doi:10.32920/25413859

Electromagnetic Treatment to Improve Water Clarity: The Effect of Treatment on Water Viscosity and Isoelectric Point of Colloidal Clay Particles

2024· preprint· en· W4392928187 on OpenAlexaff
Yana Snisarenko

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIsoelectric pointSettlingViscosityColloidSuspension (topology)ChemistryParticle (ecology)Electrokinetic phenomenaChromatographyChemical engineeringMaterials scienceEnvironmental engineeringEnvironmental scienceComposite materialOrganic chemistryGeologyPhysical chemistryMathematics

Abstract

fetched live from OpenAlex

This study explored effects of a device that uses a low power electromagnetic field (EMF), which has previously coincided with improved water clarity in field applications. Colloidal suspensions were exposed to EM treatment to study settling of particulate matter, where effects of EMF on isoelectric point of colloidal suspensions and viscosity of water were assessed. Results indicated that the isoelectric point occurred at lower pH with treatment (decrease by 0.29 pH units), and that viscosity may also be reduced with EM treatment. Thus, particle surface charge was decreased. These results may be attributed to alterations in interactions between water molecules and substrata in a way that suggests that EMF may disrupt H-bonding between molecules, but further tests did not show treatment effect on colloidal particle settling at neutral pH. However, at the isoelectric point for reference conditions, clays remained in suspension longer with EMF treatment, settling rapidly in its absence.

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 score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.004
GPT teacher head0.231
Teacher spread0.227 · 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
Published2024
Admission routes1
Has abstractyes

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