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Record W2023309683 · doi:10.2166/wst.2006.853

Ammonia removal in the catalytic wet air oxygen process of landfill leachates with Co/Bi catalyst

2006· article· en· W2023309683 on OpenAlexaff
Y. Li, L. Liu, Guohe Huang, Li Zhu

Bibliographic record

VenueWater Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Regina
FundersChina Scholarship CouncilPeople's Government of Jilin Province
KeywordsAmmoniaChemistryNitrateLeachateWet oxidationCatalysisNitriteEffluentInorganic chemistryEnvironmental chemistryNitrogenEnvironmental engineeringOrganic chemistryEnvironmental science

Abstract

fetched live from OpenAlex

Oxidation of ammonia in landfill leachates in the catalytic wet air oxidation (CWAO) process was investigated with Co/Bi catalyst. The characterization of the Co/Bi catalyst was carried out by the X-ray diffraction technique. Studies of ammonia removal from the landfill leachates by CWAO showed that Co/Bi catalyst exhibited higher activities for both total organic carbon (TOC) and ammonia with removal levels of 99% for TOC and 98% for ammonia, respectively. Results also indicated that large amounts of ammonia were produced during the elimination of nitrogenous organic compounds in the CWAO process and the further oxidation of ammonia gave off essentially N2 under 240 degrees C. When the system temperature reached above 240 degrees C, ammonia oxidation rate was much higher with nitrate dominating in the effluent; a very small amount of nitrite was observed in the reaction process, it possibly acts as the intermediate of nitrate ion and molecular nitrogen formation, showing that the system temperature had significant effects on the ammonia oxidation and reaction selectivity towards the production of molecular nitrogen or nitrate.

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 score0.679
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.223
Teacher spread0.218 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

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