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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

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.

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 score0.071
Threshold uncertainty score0.252

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 head0.008
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