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Record W2911697714 · doi:10.21967/jbb.v4i1.180

Progress in Preparation and Application of Modified Biochar for Improving Heavy Metal Ion Removal From Wastewater

2019· article· en· W2911697714 on OpenAlexafffundvenue
Patrick M. Godwin, Yuanfeng Pan, Huining Xiao, Muhammad T. Afzal

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBiocharSorptionWastewaterAdsorptionSewage treatmentSurface modificationNanomaterialsWaste managementAlkali metalContaminationPulp and paper industryChemistryEnvironmental scienceMaterials scienceChemical engineeringNanotechnologyEnvironmental engineeringPyrolysisOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Modified biochar (BC) is reviewed in its preparation, functionality, application in wastewater treatment and regeneration. The nature of precursor materials, preparatory conditions and modification methods are key factors influencing BC properties. Steam activation is unsuitable for improving BC surface functionality compared with chemical modifications. Alkali-treated BC possesses the highest surface functionality. Both alkali modified BC and nanomaterial impregnated BC composites are highly favorable for enhancing the adsorption of different contaminants from wastewater. Acidic treatment provides more oxygenated functional groups on BC surfaces. Future research should focus on industry-scale applications and competitive sorption for contaminant removal due to scarcity of data.

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: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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
GenreReview

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

Citations221
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Bioresources and BioproductsSame topicAdsorption and biosorption for pollutant removalFrench-language works237,207