MétaCan
Menu
Back to cohort
Record W4212952416 · doi:10.1038/s41598-022-06930-0

Effect of phosphate and ammonium concentrations, total suspended solids and alkalinity on lignin-induced struvite precipitation

2022· article· en· W4212952416 on OpenAlexafffund
Mozhu Li, Huixin Zhang, Huijuan Sun, Abdul Mohammed, Yang Liu, Qingye Lu

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of Calgary
KeywordsStruviteAlkalinityLigninPrecipitationPhosphorusPhosphateChemistryWastewaterAmmoniumTotal suspended solidsSuspended solidsEutrophicationPulp and paper industryEnvironmental engineeringNutrientChemical oxygen demandEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract To solve the problems of eutrophication and resource crisis, the recovery of phosphorus by struvite (NH 4 MgPO 4 ·6H 2 O) precipitation has become a focus of recent research. The feasibility of using Kraft lignin powder as a seed to promote struvite precipitation has been demonstrated in the previous study. In this study, the effect of lignin in promoting struvite precipitation in synthetic wastewater with different characteristics was investigated. Lignin-induced struvite crystallization was tested under various initial concentrations of PO 4 –P and NH 4 –N, total suspended solids (TSS) and alkalinity. At pH 7.9, the enhancement of PO 4 –P recovery remains around 45% under different PO 4 –P and NH 4 –N concentrations. Moreover, lignin is more effective under relatively lower alkalinity and still workable to reduce co-precipitates potential under higher alkalinity. Also, the effect of TSS on PO 4 –P recovery is not significant. Overall, the effect of lignin in promoting phosphorus recovery is relatively stable and can be used in synthetic wastewater with different characteristics.

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)0.002
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 score0.400
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicPhosphorus and nutrient managementFrench-language works237,207