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Record W2071749629 · doi:10.1021/es800128m

Transport and Retention of Nanoscale C<sub>60</sub>Aggregates in Water-Saturated Porous Media

2008· article· en· W2071749629 on OpenAlexaboutno aff
Yonggang Wang, Yusong Li, John D. Fortner, Joseph B. Hughes, Linda M. Abriola, Kurt D. Pennell

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersU.S. Environmental Protection Agency
KeywordsPorous mediumNanoscopic scalePorosityChemical engineeringMaterials scienceWater retentionEnvironmental scienceNanotechnologySoil scienceComposite materialSoil waterEngineering

Abstract

fetched live from OpenAlex

Experimental and mathematical modeling studies were performed to investigate the transport and retention of nanoscale fullerene aggregates (nC60) in water-saturated porous media. Aqueous suspensions of nC60 aggregates (95 nm diameter, 1 to 3 mg/L) were introduced into columns packed with either glass beads or Ottawa sand at a Darcy velocity of 2.8 m/d. In the presence of 1.0 mM CaCl2, nC60 effluent breakthrough curves (BTCs) gradually increased to a maximum value and then declined sharply upon reintroduction of nC60-free solution. Retention of nC60 in glass bead columns ranged from 8 to 49% of the introduced mass, while up to 77% of the mass was retained in Ottawa sand columns. When nC60 suspensions were prepared in deionized water alone, effluent nC60 BTCs coincided with those of a nonreactive tracer (Br-), with minimal nC60 retention. Observed differences in nC60 transport and retention behavior in glass beads and Ottawa sand were consistent with independent batch retention data and theoretical calculations of electrostatic interactions between nC60 and the solid surfaces. Effluent concentration and retention profile data were accurately simulated using a numerical model that accounted for nC60 attachment kinetics and a limiting retention capacity.

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
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 score0.091
Threshold uncertainty score0.999

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)0.0000.000

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 head0.205
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations202
Published2008
Admission routes1
Has abstractyes

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