MétaCan
Menu
Back to cohort
Record W2316316404 · doi:10.1021/es301234m

Transport of Fullerene Nanoparticles (<i>n</i>C<sub>60</sub>) in Saturated Sand and Sandy Soil: Controlling Factors and Modeling

2012· article· en· W2316316404 on OpenAlexaboutno aff
Lunliang Zhang, Lei Hou, Lilin Wang, Amy T. Kan, Wei Chen, Mason B. Tomson

Bibliographic record

VenueEnvironmental Science & Technology · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsnot available
Fundersnot available
KeywordsQuartzDeposition (geology)Ionic strengthPhase (matter)Organic matterSoil scienceChemistryEnvironmental chemistryMineralogyMaterials scienceGeologyMetallurgyGeomorphologySediment

Abstract

fetched live from OpenAlex

Understanding subsurface transport of fullerene nanoparticles (nC(60)) is of critical importance for the benign use and risk management of C(60). We examined the effects of several important environmental factors on nC(60) transport in saturated porous media. Decreasing flow velocity from approximately 10 to 1 m/d had little effect on nC(60) transport in Ottawa sand (mainly pure quartz), but significantly inhibited the transport in Lula soil (a sandy, low-organic-matter soil). The difference was attributable to the smaller grain size, more irregular and rougher shape, and greater heterogeneity of Lula soil. Increasing ionic strength and switching background solution from NaCl to CaCl(2) enhanced the deposition of nC(60) in both sand and soil columns, but the effects were more significant for soil. This was likely because the clay minerals (and possibly soil organic matter) in soil responded to changes of ionic strength and species differently than quartz. Anions in the mobile phase had little effect on nC(60) transport, and fulvic acid in the mobile phase (5.0 mg/L) had a small effect in the presence of 0.5 mM Ca(2+). A two-site transport model that takes into account both the blocking-affected attachment process and straining effects can effectively model the breakthrough of nC(60).

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 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 score0.065
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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.

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

Citations106
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicNanoparticles: synthesis and applicationsFrench-language works237,207