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Record W3173918238 · doi:10.3390/nano11071686

Metal Oxide-Related Dendritic Structures: Self-Assembly and Applications for Sensor, Catalysis, Energy Conversion and Beyond

2021· review· en· W3173918238 on OpenAlexaff
Ruohong Sui, Paul A. Charpentier, Robert A. Marriott

Bibliographic record

VenueNanomaterials · 2021
Typereview
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsWestern UniversityUniversity of Calgary
Fundersnot available
KeywordsNanotechnologyMaterials scienceSelf-assemblyNanostructureOxideDrug deliveryEnergy transformationRenewable energyBiomimeticsComputer scienceEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

In the past two decades, we have learned a great deal about self-assembly of dendritic metal oxide structures, partially inspired by the nanostructures mimicking the aesthetic hierarchical structures of ferns and corals. The self-assembly process involves either anisotropic polycondensation or molecular recognition mechanisms. The major driving force for research in this field is due to the wide variety of applications in addition to the unique structures and properties of these dendritic nanostructures. Our purpose of this minireview is twofold: (1) to showcase what we have learned so far about how the self-assembly process occurs; and (2) to encourage people to use this type of material for drug delivery, renewable energy conversion and storage, biomaterials, and electronic noses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2021
Admission routes1
Has abstractyes

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