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Record W2327366010 · doi:10.1021/es4032198

Rates and Mechanisms of Zn<sup>2+</sup> Adsorption on a Meat and Bonemeal Biochar

2013· article· en· W2327366010 on OpenAlexaff
Aaron R. Betts, Ning Chen, Jordan G. Hamilton, Derek Peak

Bibliographic record

VenueEnvironmental Science & Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of SaskatchewanCanadian Light Source (Canada)
Fundersnot available
KeywordsBiocharAdsorptionChemistryEnvironmental chemistryZincWaste managementEnvironmental scienceRadiochemistryPyrolysisOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Biochars produced from meat and bonemeal (MBM) waste materials contain large amounts of calcium phosphate and are potentially useful sorbents for the remediation of metals. Because the reactivity of biochars depends strongly upon the conditions used in their production, the objective of this study was to evaluate the rates and mechanisms of Zn sorption onto a commercially supplied MBM biochar prior to its application in a field-scale revegetation project. Sorption experiments varying pH, time, and Zn concentration found that, above pH 6.1, Zn adsorbed to MBM biochar quickly (within 5 h) with a maximum adsorption capacity of 0.65 mmol Zn g(-1). Synchrotron-based Zn K-edge extended X-ray absorption fine structure spectroscopy was consistent with a tetrahedral Zn bound to phosphate groups in a monodentate inner-sphere surface complex for all conditions tested. With an acidification pretreatment and at more acidic pH, MBM biochar causes precipitation of a ZnPO4 phase. On the basis of these results, this MBM biochar has a high capacity to rapidly adsorb Zn in adsorption experiments and can be considered a promising sorbent for Zn remediation of contaminated soils.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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; both teacher heads agree on what is shown here.

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

Citations82
Published2013
Admission routes1
Has abstractyes

Explore more

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