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Record W3184012730 · doi:10.52547/jcc.3.2.6

Application of Polyoxometalate-based composites for sensor systems: A review

2021· review· en· W3184012730 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

VenueJournal of Composites and Compounds · 2021
Typereview
Languageen
FieldMaterials Science
TopicPolyoxometalates: Synthesis and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPolyoxometalateCarbon nanotubeMaterials scienceComposite materialGrapheneComposite numberNanotechnologyChemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Composites based on polyoxometalates (POMs) have been increasingly attracted by many researchers due to their multitudinous architectures and excellent redox activities as well as outstanding proton and electron transport capacities. Lately, much research has been done on POMs composited with well-porous framework materials (including ZIFs, MOFs) or conducting polymers, carbon quantum dot (CQD), graphene, carbon structures (e.g. carbon nanotubes (CNTs)), and metal nanoparticles (NPs). The results exhibited improved stability and enhanced electrochemical performances. Hence, developing POMs and POM-based composite materials (PCMs) has long been a topic of interest for chemical researchers. Herein, the properties and applications of pristine POMs, doped POMs, and composite-based POMs are reviewed in detail. The various compositions of POMs with sensing application such as POMs-nanocarbon composites (POMs-graphene composites and POMs-carbon nanotube composites), POMs-conductive polymer composites, and POMs-metal composites are also investigated in this review.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.322
Teacher spread0.287 · 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