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Record W884186612 · doi:10.1155/2016/5390808

Integrated Nanozero Valent Iron and Biosurfactant-Aided Remediation of PCB-Contaminated Soil

2016· article· en· W884186612 on OpenAlexafffundabout
He Zhang, Baiyu Zhang, Bo Liu

Bibliographic record

VenueApplied and Environmental Soil Science · 2016
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsEnvironmental remediationContaminationEnvironmental chemistryZerovalent ironEnvironmental scienceSoil contaminationSoil remediationBioremediationWaste managementChemistrySoil waterSoil scienceAdsorptionBiologyEcology

Abstract

fetched live from OpenAlex

Polychlorobiphenyls (PCBs) have been identified as environmental hazards for years. Due to historical issues, a considerable amount of PCBs was released deep underground in Canada. In this research, a nanoscale zero valent iron- (nZVI-) aided dechlorination followed by biosurfactant enhanced soil washing method was developed to remove PCBs from soil. During nZVI-aided dechlorination, the effects of nZVI dosage, initial pH level, and temperature were evaluated, respectively. Five levels of nZVI dosage and two levels of initial pH were experimented to evaluate the PCB dechlorination rate. Additionally, the temperature changes could positively influence the dechlorination process. In soil washing, the presence of nanoiron particles played a key role in PCB removal. The crude biosurfactant was produced using a bacterial stain isolated from the Atlantic Ocean and was applied for soil washing. The study has led to a promising technology for PCB-contaminated soil remediation.

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 score0.002
Threshold uncertainty score0.004

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)0.0000.000

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 head0.004
GPT teacher head0.163
Teacher spread0.159 · 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

Citations11
Published2016
Admission routes3
Has abstractyes

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