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Record W2068545729 · doi:10.1002/adfm.200305455

Template‐Assisted Synthesis of Mesoporous Magnetic Nanocomposite Particles

2004· article· en· W2068545729 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

VenueAdvanced Functional Materials · 2004
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMesoporous materialNanocompositeChemical engineeringMagnetiteMesoporous organosilicaFourier transform infrared spectroscopyTransmission electron microscopyAdsorptionMesoporous silicaZeta potentialNanoparticleNanotechnologyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A new concept is proposed to synthesize mesoporous magnetic nanocomposite particles of great scientific and technological importance. Mesoporous silica coatings were created on micrometer‐sized magnetite (Fe 3 O 4 ) particles using cetyltrimethylammonium chloride micelles as molecular templates. The characterization by transmission electron microscopy (TEM), nitrogen adsorption–desorption, diffuse‐reflectance Fourier‐transform infrared spectroscopy, and zeta‐potential measurements confirmed the deposition of mesoporous silica thin layers on the magnetite particles. The synthesized particles showed a drastic increase in specific surface area with an average pore size of 2.5 nm. The coating material showed a negligible effect on the saturation magnetization of the original particles that were fully protected by silica coatings. The synthesized mesoporous magnetic nanocomposite particles have a wide range of applications in toxin removal, waste remediation, catalysis, reactive sorbents, and biological cell separations.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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