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Record W1973872321 · doi:10.4236/wjnse.2012.22008

Switchgrass (<i>Panicum virgatum</i>) Extract Mediated Green Synthesis of Silver Nanoparticles

2012· article· en· W1973872321 on OpenAlexaff
Cynthia Mason, Singaravelu Vivekanandhan, Manjusri Misra, Amar K. Mohanty

Bibliographic record

VenueWorld Journal of Nano Science and Engineering · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSilver nanoparticleSilver nitrateMaterials scienceTransmission electron microscopyPanicum virgatumNanoparticleNuclear chemistryPanicumChemical engineeringNanotechnologyChemistryBotanyBiology

Abstract

fetched live from OpenAlex

A novel switchgrass (Panicum virgatum) extract mediated green process was demonstrated for the synthesis of silver nanoparticles from silver nitrate solution at ambient temperature. UV-visible spectroscopic analysis indicates the rapid reduction of silver (Ag+) ions by swithgrass extract. The silver nanoparticles began to form at 15 min and the reduction reaction was completed within 2 hours. Synthesized silver nanoparticles were subjected to x-ray diffraction (XRD) for structural characterization, which confirms the FCC symmetry of silver nanoparticles with the lattice parameter of 4.0962 ?. The particle size of bio-synthesized silver nanoparticles was identified through transmission electron microscopic (TEM) analysis and found to be in the range of 20 - 40 nm.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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

Citations89
Published2012
Admission routes1
Has abstractyes

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