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Utilizing waste eggshells as a calcium precursor for contact precipitation of phosphorus from digested sludge centrate

2024· article· en· W4391739186 on OpenAlexafffund
Linyu Deng, Basem S. Zakaria, Jianfeng Zhang, Bipro Ranjan Dhar

Bibliographic record

VenueThe Science of The Total Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsPhosphorusPrecipitationWastewaterEggshellBrushiteChemistryChemical oxygen demandStruvitePulp and paper industryPhosphateEnvironmental chemistryNuclear chemistryEnvironmental engineeringEnvironmental scienceEcologyBiochemistryBiology

Abstract

fetched live from OpenAlex

Phosphorus (P) recovery from wastewater is an essential component of the global P cycle. A contact precipitation process using chicken eggshells as a calcium (Ca) precursor was used to recover P from synthetic wastewater and real digested sludge centrate. Up to 96.4 % of P could be recovered from the digested sludge centrate after three repeated cycles of the contact precipitation process. In addition, 36.1 % of total chemical oxygen demand and 37.6 % of total ammonia nitrogen were removed from the centrate. Finally, most of the precipitates could be collected by a simple washing step. Scanning electron microscopy-energy dispersive spectroscopy and x-ray diffraction results indicated that the eggshells played three roles in this process: Ca source, precipitation substrate, and filter medium. Precipitates were mainly brushite. This research provides a new perspective on P recovery from wastewater using waste eggshells, and if further optimized, has a potential for practical future applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.236
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2024
Admission routes2
Has abstractno

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