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Record W2335567729 · doi:10.1080/09542299.2016.1165080

Pyrolysis temperature and steam activation effects on sorption of phosphate on pine sawdust biochars in aqueous solutions

2016· article· en· W2335567729 on OpenAlexafffund
Kangyi Lou, Anushka Upamali Rajapaksha, Yong Sik Ok, Scott X. Chang

Bibliographic record

VenueChemical Speciation and Bioavailability · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiocharPyrolysisPhosphateAdsorptionSawdustAqueous solutionSorptionChemistryCharcoalChemical engineeringEnvironmental chemistryNuclear chemistryPulp and paper industryOrganic chemistry

Abstract

fetched live from OpenAlex

Biochar can be used as an adsorbent for phosphate removal in aquatic environments to treat eutrophication problems. Designing biochars that have large phosphate adsorption capacity through altering pyrolysis conditions and applying activation techniques will improve phosphate removal efficiency. In this study, four pine sawdust biochars were produced at 300 and 550 °C with and without steam activation. Batch sorption experiments including isotherm and kinetic studies were conducted to understand how phosphate removal capabilities and adsorption mechanisms of biochars were affected by pyrolysis temperature and steam activation. Our results showed that the steam activation and pyrolysis temperature did not affect phosphate adsorption by the biochars. The four biochars removed <4% of phosphate from the aqueous solution, which were not affected by the pH of the solution and biochar application rate. The repulsion forces between biochar surfaces and phosphate ions were likely the cause of the low adsorption.

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 score0.003
Threshold uncertainty score0.005

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)0.0010.000

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 head0.007
GPT teacher head0.191
Teacher spread0.185 · 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

Citations110
Published2016
Admission routes2
Has abstractyes

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