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High Yield Synthesis and Application of Magnetite Nanoparticles (Fe3O4)

2020· article· en· W3025434978 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMagnetochemistry · 2020
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsUniversity of WaterlooUniversity of Guelph
FundersOntario Agri-Food Innovation AllianceUniversity of Guelph
KeywordsMagnetiteAqueous solutionFerrousNanoparticleParticle sizeChemistryNuclear chemistryYield (engineering)FerricExtraction (chemistry)Analytical Chemistry (journal)Materials scienceChromatographyInorganic chemistryNanotechnologyMetallurgyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Magnetite nanoparticles (Fe3O4), average particle size of 12.9 nm, were synthesized de novo from ferrous and ferric iron salt solutions (total iron salt concentration of 3.8 mM) using steady-state headspace NH3(g), 3.3% v/v, at room temperature and pressure, without mechanical agitation, resulting in >99.9% yield. Nanoparticles size distributions were based on enumeration of TEM images and chemical compositions analyzed by: XRD, EDXRF, and FT-IR; super-paramagnetic properties were analyzed by magnetization saturation (74 emu/g). Studies included varying headspace [NH3(g)] (1.6, 3.3, 8.4% v/v), and total iron concentrations (1.0 mM, 3.8 mM, 10.0 mM, and >>10 mM). An application of the unmodified synthesized magnetite nanoparticles included analyses of tetracycline’s (50, 100, 200, 300, and 400 ppb) in aqueous, which was compared to the same tetracycline concentrations prepared in aqueous synthesis suspension with >97% extraction, analyzed with LC-MS/MS.

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 score0.013
Threshold uncertainty score0.683

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)0.0010.000

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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