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Record W4408038562 · doi:10.1039/d4mh01896f

Droplet-templating soft materials into structured bead-based aerogels with compartmentalized or welded configurations

2025· article· en· W4408038562 on OpenAlexafffund
Shayan Ghasemi, Mahyar Panahi‐Sarmad, Elnaz Erfanian, Tianyu Guo, Vahid Rad, Adel Jalaee, Gabriel Banvillet, E. Johan Foster, Kam Chiu Tam, Masoud Soroush, Feng Jiang, Orlando J. Rojas, Milad Kamkar

Bibliographic record

VenueMaterials Horizons · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of British ColumbiaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsTemplateAerogelMaterials scienceBeadNanotechnologyComposite material

Abstract

fetched live from OpenAlex

the interfacial co-assembly of nanoparticles-surfactants (NPSs) at polar/apolar liquid interfaces. This approach enables the creation of aerogels with tailored compartmentalized or welded bead architectures, exhibiting multilayer, gradient, and hybrid morphologies from a range of 1D and 2D nanomaterials. By precisely controlling evaporation and freeze-drying processes, we fabricate aerogels with customizable micro-domains, without requiring chemical binders. Our approach provides a platform for developing soft materials with tunable properties, paving a new path for applications in soft matter and aerogel engineering.

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 score0.011
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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.019
GPT teacher head0.277
Teacher spread0.259 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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