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Record W2126606143 · doi:10.1155/2013/710902

Green Synthesis of Gold Nanoparticles with Self‐Sustained Natural Rubber Membranes

2013· article· en· W2126606143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Nanomaterials · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Windsor
FundersInstituto Nacional de Ciência e Tecnologia em Eletrônica OrgânicaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMaterials scienceMembraneNatural rubberNanoparticleColloidal goldComposite materialChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Green chemistry is an innovative way to approach the synthesis of metallic nanostructures employing eco‐friendly substances (natural compounds) acting as reducing agents. Usually, slow kinetics are expected due to, use of microbiological materials. In this report we study composites of natural rubber (NR) membranes fabricated using latex from Hevea brasiliensis trees (RRIM 600) that works as reducing agent for the synthesis of gold nanoparticles. A straight and clean method is presented, to produce gold nanoparticles (AuNP) in a flexible substrate or in solution, without the use of chemical reducing reagents, and at the same time providing good size’s homogeneity, reproducibility, and stability of the composites.

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.

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)0.001
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 score0.003
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.209
Teacher spread0.202 · 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