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Record W2528353256 · doi:10.1680/jenes.16.00014

Mineralisation of sulfolane by UV/O3/H2O2 in a tubular reactor

2016· article· en· W2528353256 on OpenAlexaffvenue
Mitra Mehrabani-Zeinabad, Linlong Yu, Gopal Achari, Cooper H. Langford

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Calgary
FundersUniversity of Pittsburgh
KeywordsSulfolaneHydrogen peroxideOzoneChemistrySulfateInorganic chemistryEnvironmental chemistryNuclear chemistryOrganic chemistrySolvent

Abstract

fetched live from OpenAlex

Degradation of sulfolane in contaminated groundwater was studied in a flow-through photoreactor with recirculation, using various oxidative methods, including ozone (O3)/hydrogen peroxide (H2O2), ultraviolet (UV)/ozone, UV/hydrogen peroxide and UV/ozone/hydrogen peroxide. Total organic carbon as an indicator of the extent of mineralisation and sulfate as a byproduct produced during degradation were measured. The effects of several operating conditions, including flow rate, initial concentration of sulfolane and ratio of different oxidants, were investigated. It was noted that although the loss of sulfolane was similar for the UV/ozone/hydrogen peroxide and the UV/hydrogen peroxide process, the overall mineralisation rates were significantly different, indicating that the combination of oxidants offers a significantly more efficient system. The results show that in water containing 100 mg/l sulfolane, more than 99·5% can be mineralised to carbon dioxide (CO2), water (H2O) and sulfate (SO4 2−) by the UV/ozone/hydrogen peroxide process (the most efficient process) within 4 h (13·2 kJ energy from UV irradiation transferred). The UV/ozone/hydrogen peroxide process mineralised sulfolane in contaminated groundwater, but with a longer reaction 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.222

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 communication0.0000.001
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 head0.003
GPT teacher head0.175
Teacher spread0.171 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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