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Record W3210276821 · doi:10.2166/wst.2021.480

Effect of the coexistence of SO32− and PO43− on the adsorption performance of zeolite-loaded FeOOH@ZnO for S2−

2021· article· en· W3210276821 on OpenAlexaff
Fen Li, Youjing Li, Baocai Ge, Jinguang Hu, Cailian Yu, Fanzhu Meng

Bibliographic record

VenueWater Science & Technology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAdsorptionZeoliteHydrothermal circulationAqueous solutionIonChemistryInorganic chemistryChemical engineeringSulfateMaterials scienceNuclear chemistryMineralogyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The present study deals with the synthesis of zeolite-loaded FeOOH@ZnO by hydrothermal method and investigates the effects of coexisting SO32− and PO43− ions in the aqueous solution on the adsorption performance for S2−. The results showed that the HNO3-modified zeolite loaded with FeOOH@ZnO (FeOOH@ZnO/HZ) resulted in a maximum S2− removal rate of ≈98%. The adsorbent's performance on removing S2− was significantly enhanced, compared with NaOH and ZnCl2-modified zeolites loaded with FeOOH@ZnO, and the adsorption was proved to be a heat-absorbing process. When SO32− and PO43− coexisted with S2−, SO32− and PO43− had a significant influence on the adsorption properties of FeOOH@ZnO/HZ. When three ions of S2−, SO32− and PO43− were present simultaneously, the adsorption performance of FeOOH@ZnO/HZ on S2− was further, and the removal rate dropped to about 80%. Moreover, FeOOH@ZnO/HZ also adsorbed PO43− and SO32− in the system containing multiple ions, but the adsorption rates of PO43− and SO32− were much lower than S2−. This indicated that the adsorption of S2− in the presence of FeOOH@ZnO/HZ dominates under competitive conditions.

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)0.001
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 score0.024
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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