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Record W2002993482 · doi:10.1021/ed5004756

Sweet Nanochemistry: A Fast, Reliable Alternative Synthesis of Yellow Colloidal Silver Nanoparticles Using Benign Reagents

2014· article· en· W2002993482 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 Chemical Education · 2014
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNanochemistryReagentNanoparticleSilver nitrateColloidSilver nanoparticleNanotechnologyMaterials scienceChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This work describes a convenient and reliable laboratory experiment in nanochemistry that is flexible and adaptable to a wide range of educational settings. The rapid preparation of yellow colloidal silver nanoparticles is achieved by glucose reduction of silver nitrate in the presence of starch and sodium citrate in gently boiling water, using either a hot plate or domestic microwave oven as the heat source. During the experiment, the students are encouraged to consider the role that each reagent plays in the synthesis. If desired, the experiment can be repeated with the systematic elimination of individual reagents to more rigorously investigate the importance of each in the optimized reaction conditions. The benign reagents used in the preparation are commonly found in most educational science storerooms. The optimized synthesis has proven to be quite reliable in student hands and may also appeal as an alternative method to instructors who already include the preparation of silver nanoparticles in their laboratory curriculum.

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)0.000
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 score0.005
Threshold uncertainty score0.323

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 head0.009
GPT teacher head0.259
Teacher spread0.249 · 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