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Steam activation of camelina meal biochar to remediate PFOA-contaminated wastewater

2025· article· en· W4412862722 on OpenAlexafffund
Shivangi Jha, Falguni Pattnaik, Oscar Zapata, Bishnu Acharya, Ajay K. Dalai

Bibliographic record

VenueBiomass and Bioenergy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Red CrossBioFuelNet Canada
KeywordsBiocharCamelinaCamelina sativaWastewaterEnvironmental scienceContaminationMealWaste managementEnvironmental chemistryChemistryFood sciencePulp and paper industryAgronomyBiologyEnvironmental engineeringPyrolysisEcologyCrop

Abstract

fetched live from OpenAlex

This study investigates the steam activation of biochar produced using camelina meal for the removal of perfluorooctanoic acid (PFOA) from industrial effluent. A response surface methodology (RSM) framework with a central composite design (CCD) was utilized to optimize important activation parameters, such as steam flow rate (5–15 mL/h), time (60–120 min), and temperature (600–900 °C). The activation process considerably influenced the surface area of the biochar, with temperature emerging as the most critical factor in the activation process. The experiment performed at optimal conditions of 899 °C, 75 min, and 14 mL/h as determined by the CCD-RSM model attains a surface area of 722 m 2 /g. By conducting characterizations such as FTIR and SEM, it was disclosed that adsorption performance and the porosity of the steam-activated biochar are enhanced by the presence of oxygenated functional groups. Adsorption tests showed that the steam-activated carbon adsorbed PFOA from water, reaching equilibrium in around 5 h. The results discussed emphasize the potential of using a camelina meal activated carbon as an economical, environmentally sustainable substitute for the treatment of PFAS-contaminated water systems.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.307

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 head0.009
GPT teacher head0.229
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

Citations2
Published2025
Admission routes2
Has abstractyes

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