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Record W7116742164 · doi:10.1021/acs.chemmater.5c02937

Microwave-Assisted Citrate-Mediated Hydrothermal Synthesis of Mono- and Multi-Metal Silicates Supported on Stöber Silica and Mesoporous Silica Substrates

2025· article· en· W7116742164 on OpenAlexafffund
Chloe F. McLeod, Christina Zafeiridou, Mia Dolanjski, Jonathan G. C. Veinot

Bibliographic record

VenueChemistry of Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of Alberta
KeywordsMesoporous silicaSilicateMetalHydrothermal synthesisX-ray photoelectron spectroscopyMesoporous materialHydrothermal circulationAdsorptionCatalysis

Abstract

fetched live from OpenAlex

Metal silicates have far reaching applications that include catalysis, pollutant remediation, and energy storage. Conventional syntheses are timely (6–48 h) and often incorporate multiple metals inhomogeneously into the silicate framework, thus limiting predictable tailorability. Here, we report a microwave-assisted citrate-mediated hydrothermal synthesis of mono- and multimetal silicate coatings on Stöber silica spheres and mesoporous silica (SBA-15). Using microwave irradiation facilitates rapid and uniform heating and reduces reaction times to 20 min. These heating characteristics, combined with citrate modulated metal ion release, prevent unwanted oxidation and provide homogeneous deposition of metal silicates onto the silica templates. Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy provide confirmation of the formation of metal silicates, showing characteristic binding motifs and binding energy shifts. Transmission electron microscopy coupled with energy dispersive X-ray spectroscopy demonstrates the preservation of the silica template morphology and the formation of a uniform metal silicate shell. Nitrogen adsorption studies verified the retention of the mesoporous silica structure and high surface area (>240 m 2 /g), confirming the versatility of this synthesis on fragile silica templates. This work demonstrates a versatile approach toward synthesizing metal silicates with controlled composition and morphology that offer opportunities for tailoring catalytic materials.

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)0.001
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 score0.005
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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