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Record W2953894986 · doi:10.1680/jenes.18.00039

Theoretical model for micro-nano-bubbles mass transfer during contaminant treatment

2019· article· en· W2953894986 on OpenAlexvenueno aff
Zhiran Xia, Liming Hu

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Science Foundation
KeywordsMass transferEnvironmental scienceOzoneVolume (thermodynamics)Water treatmentEnvironmental chemistryEnvironmental engineeringChemistryThermodynamics

Abstract

fetched live from OpenAlex

Micro-nano-bubbles (MNBs), in particular ozone and oxygen MNBs, represent an innovative method for wastewater treatment and groundwater remediation. Although several models have been developed to describe the fate of MNBs in water, a theoretical model describing the mass transfer processes of MNBs during treatment of contaminants has not been adequately developed. In this study, a theoretical model considering the decomposition and reaction of dissolved gas is proposed based on the Epstein and Plesset theory, aiming to describe mass transfer processes of oxygen and ozone MNBs during treatment of contaminants. The life of MNBs, volume-weighted average dissolved gas concentration and utilisation efficiency of gas, which are of significant importance in MNB applications, are further studied. The life of MNBs increases with the half-life of dissolved gas, but the impact diminishes. For gases which present a slow consumption rate and a long half-life, the effect of half-life on the life of MNBs is negligible. With controlled total mass of gas, smaller-sized bubbles result in a significantly higher dissolved gas concentration and more efficient treatment of contaminants. The newly developed model can describe the fate of MNBs during treatment of contaminated water and theoretically proves the advantages of MNBs over large-sized bubbles.

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 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: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.330

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)0.0000.000

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 head0.005
GPT teacher head0.200
Teacher spread0.196 · 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.

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

Citations16
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicMinerals Flotation and Separation TechniquesFrench-language works237,207