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Record W4285797968 · doi:10.1139/er-2021-0038

Review of resource utilization of Fe-rich sludges: purification, upcycling, and application in wastewater treatment

2022· article· en· W4285797968 on OpenAlexvenueno aff
Yu Chen, Dongxu Liang, Xinfeng Xie, Joseph Eskola

Bibliographic record

VenueEnvironmental Reviews · 2022
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
Fundersnot available
KeywordsFlocculationHematiteWaste managementResource recoverySewage treatmentRed mudWastewaterMaterials scienceEnvironmental scienceMetallurgyEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

This paper discusses the resource utilization of Fe-rich sludges generated as waste products from water treatment, hydrometallurgy, surface finishing, and dye chemical industries. Apart from the conventional landfill disposal of such sludges, the work focuses on sludge purification for new commercial products, including iron red/black dyes, an iron concentrate powder, a polyferric flocculant, a catalyst, and a magnetic adsorbent. Among such purifications, a new strategy was developed to recycle Fe-rich sludges for a new Fe/S-bearing flocculant. Given that Fe-rich sludges may contain rare and/or heavy metals, the purification of sludges as high-purity hematite nanoparticles and other valuable products is detailed as a new insight. Accordingly, the mechanisms for the phase transformation of Fe-bearing minerals and the purification of valuable Fe oxides are deeply considered. The work summarizes the pilot- and/or field-scale application for recycling of Fe-rich sludge and proposes the development of a new Fe/S flocculant and a high-purity hematite product.

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 score0.814
Threshold uncertainty score0.529

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 head0.019
GPT teacher head0.239
Teacher spread0.221 · 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

Citations27
Published2022
Admission routes1
Has abstractyes

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