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Record W2593179998 · doi:10.1002/9783527340934.ch3

Direct Growth of One‐, Two‐, and Three‐Dimensional Nanostructured Materials at Electrode Surfaces

2017· other· en· W2593179998 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

VenueAdvances in electrochemical science and engineering · 2017
Typeother
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsLakehead University
Fundersnot available
KeywordsNanomaterialsMaterials scienceNanotechnologyNanorodGrapheneAnodizingSubstrate (aquarium)Titanium dioxideNanowireElectrodeOxideHydrothermal circulationChemical engineeringChemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

In this chapter, one-dimensional (1D) nanostructured materials primarily focus on nanowires, nanorods, and nanotubes. Two-dimensional (2D) nanomaterials mainly include nanoplates and graphene oxide (GO) sheets, whereas three-dimensional (3D) nanomaterials chiefly comprise nanodendrites and nanoflowers. Various widespread synthesis methods which are currently in common use for the growth of nanomaterials on electrode surfaces, spanning hydrothermal, templated, thermal decomposition, anodization, and chemical deposition, are discussed in the chapter. The direct growth of nanomaterials on a substrate provides a number of advantages, such as an enhanced mechanical interface between the substrate and the grown nanomaterial, greater electron transfer, and higher stability. The chapter describes the mechanism for the fabrication of titanium dioxide (TiO2) nanotubes on titanium substrates. The morphologies of the TiO2 nanotubes also significantly depend on the solution temperature, applied voltage, and anodization duration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.104
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.003
GPT teacher head0.210
Teacher spread0.206 · 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