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Record W4249507077 · doi:10.24124/2017/1371

Arsenic adsorption in aqueous solution and immobilization in soils and using hand warmers

2017· dissertation· en· W4249507077 on OpenAlexfundno aff
Zeyi Tong

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
FundersUniversity of Northern British ColumbiaMcGill University
KeywordsArsenicAdsorptionFreundlich equationAqueous solutionChemistryEnvironmental remediationSiltSoil waterExtraction (chemistry)Human decontaminationFraction (chemistry)Nuclear chemistryEnvironmental chemistryChromatographyWaste managementContaminationEnvironmental scienceGeologySoil scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Adsorption is a commonly used method for arsenic remediation. The adsorption and immobilization effectiveness of arsenic by soil particles and used hand warmers was studied. The adsorption effectiveness at equilibrium of soil particles in 10 ppm As(III) solution was: clay (77.70%) > silt (69.24%) > sand (41.35%). In 1000 ppm As(III) solution, 17.02 mg As(III) was adsorbed onto each gram of hand warmers at equilibrium, which was significantly higher than other adsorbents. For As(III) adsorption from aqueous solution, soil samples and hand warmers were well fitted to the pseudo secondorder model and the Freundlich model. After 8 weeks of soil incubation, the sequential extraction procedure data indicated the labile fractions of arsenic (F1 and F2) decreased with the addition of hand warmers. Meanwhile, the percentage of the most stable fraction, F5, increased. These results are valuable for the future application of used hand warmers as an adsorbent/amendment for arsenic decontamination.

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)0.0010.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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