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Record W4405995289 · doi:10.3390/ma18010151

Preparation of Fe3O4/C Composite Material from Red Mud for the Degradation of Acid Orange 7

2025· article· en· W4405995289 on OpenAlex
Jiaxing Cai, Yunye Cao, Bingfei Yang, Jiajie Li, Michael Hitch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials · 2025
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsUniversity of the Fraser Valley
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsRoastingComposite numberRed mudPersulfateChemical oxygen demandMagnetiteIron oxideX-ray photoelectron spectroscopyNuclear chemistryMaterials scienceChemistryChemical engineeringCatalysisPulp and paper industryMetallurgyComposite materialEnvironmental engineeringWastewaterOrganic chemistryEnvironmental science

Abstract

fetched live from OpenAlex

This study presents a novel Fe3O4/C composite material synthesized from red mud through a process of magnetic roasting and separation. The research explores the impact of Fe3O4/C dosages, sodium persulfate (PS) concentrations, and initial solution pH on the chemical oxygen demand (COD) removal efficiency using Acid Orange 7 as a model pollutant. Optimal conditions were identified as 3 g/L Fe3O4/C, 20 mM PS, and an initial pH of 2, achieving a 94.11% COD removal efficiency within 30 min. X-ray diffraction and photoelectron spectroscopy analyses confirmed that the magnetization roasting process effectively transformed red mud’s ferric oxide (Fe2O3) into magnetite (Fe3O4). Concurrently, Fe3O4 interacted with residual carbon to form the Fe3O4/C composite. This composite demonstrated superior catalytic performance, along with excellent recyclability and reusability.

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.

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 score0.013
Threshold uncertainty score0.202

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 head0.014
GPT teacher head0.262
Teacher spread0.249 · 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