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Record W2161705133 · doi:10.1080/15226514.2012.683209

UPTAKE AND TRANSLOCATION OF TI FROM NANOPARTICLES IN CROPS AND WETLAND PLANTS

2012· article· en· W2161705133 on OpenAlexaboutno aff
Donna L. Jacob, Joshua D. Borchardt, Navaratnam Leelaruban, Marinus L. Otte, Achintya N. Bezbaruah

Bibliographic record

VenueInternational Journal of Phytoremediation · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institute of General Medical SciencesDirectorate for Biological SciencesNorth Dakota State University
KeywordsEnvironmental scienceChromosomal translocationPhytoremediationWetlandAgronomyBiologyHeavy metalsEnvironmental chemistryChemistryEcology

Abstract

fetched live from OpenAlex

Bioavailability of engineered metal nanoparticles affects uptake in plants, impacts on ecosystems, and phytoremediation. We studied uptake and translocation of Ti in plants when the main source of this metal was TiO2 nanoparticles. Two crops (Phaseolus vulgaris (bean) and Triticum aestivum (wheat)), a wetland species (Rumex crispus, curly dock), and the floating aquatic plant (Elodea canadensis, Canadian waterweed), were grown in nutrient solutions with TiO2 nanoparticles (0, 6, 18 mmol Ti L(-1) for P. vulgaris, T. aestivum, and R. crispus; and 0 and 12 mmol Ti L(-1) for E. canadensis). Also examined in E. canadensis was the influence of TiO2 nanoparticles upon the uptake of Fe, Mn, and Mg, and the influence of P on Ti uptake. For the rooted plants, exposure to TiO2 nanoparticles did not affect biomass production, but significantly increased root Ti sorption and uptake. R. crispus showed translocation of Ti into the shoots. E. canadensis also showed significant uptake of Ti, P in the nutrient solution significantly decreased Ti uptake, and the uptake patterns of Mn and Mg were altered. Ti from nano-Ti was bioavailable to plants, thus showing the potential for cycling in ecosystems and for phytoremediation, particularly where water is the main carrier.

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 score0.305
Threshold uncertainty score0.174

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 studies0.0000.000
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 head0.017
GPT teacher head0.260
Teacher spread0.244 · 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

Citations129
Published2012
Admission routes1
Has abstractyes

Explore more

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