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Record W7019121611

Fabrication of 3D Hybrid Architectures Composed of sp2-Carbon and Inorganic Materials

2013· dissertation· en· W7019121611 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2013
Typedissertation
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsGrapheneCarbon nanotubeNanomaterialsHybrid materialFabricationOxideNanostructure
DOInot available

Abstract

fetched live from OpenAlex

Three dimensional (3D) hybrid architectures are new types of materials that have a number of technological applications. However, the synthesis of such materials has been problematic to date. The objective of this study is to fabricate 3D hybrid architectures composed of sp2-carbon nanomaterials and inorganic nanostructures using a convenient microwave assisted technique. \nSp2-Carbon nanomaterials such as carbon nanotubes (CNTs), graphene and its derivative graphene oxide (GO), have been explored by researchers as major components of hybrid materials due to their exceptional electrical, thermal, mechanical and biological properties. However, most of the research has been devoted to the hybrids with randomly dispersed phases. The present study explores the feasibility of using aligned 3D sp2-carbon structures in a bottom-up microwave-assisted chemical synthesis approach to fabricate various 3D sp2-carbon/inorganic hybrid architectures. The carbon nanostructures, either tubular or planar, not only contribute to the functionalities of the hybrids, but also template the ordered assembly of phases on nanometer scale. \nMimicking nature is a key to develop novel types of materials with enhanced physical and mechanical properties suitable for advanced applications (e.g. lightweight and yet tough materials that are extensively needed in automotive and aerospace industries). One approach to obtain such materials or devices is to mimic nature processes and synthesize hybrid materials with ordered structures on the nanometer scale. Those functional structures are fabricated in this thesis through an in-situ microwave synthesis of inorganic materials on 3D sp2-carbon architectures. Generally, in chapter 1, it was shown and discussed the procedures to fabricate 3D architectures of carbon nanotubes and graphene oxide as basic components for template synthesis of the hybrids. Then in chapter 2 the microwave chemical synthesis approach was introduced as a convenient route for fabricating inorganic materials such as zinc oxide (ZnO) which was shown to be used as UV sensors. \nThrough photolithography patterning of the iron catalyst thin films on Si/SiO2 substrates, 3D aligned CNT structures were fabricated and were coated in-situ with inorganic materials such as cobalt oxide, zinc oxide and manganese oxide using a microwave synthesis approach (chapter 3). The obtained aligned strips of CNT/Co3O4 were chosen as an example to illustrate the application of such 3D hybrids in energy storage applications. The capacitance of the aligned CNT/Co3O4 strips was measured to be 123.94 F/g. \nUsing graphene oxide as template for manufacturing the 3D sp2-carbon/inorganic hybrid structures, interesting novel layered configurations are obtained that are similar to the layered structures of exoskeleton of the mollusks nacre. The layered hybrid structure shown to be mechanically improved compared to its constituents (chapter 4). Finally in chapter 5, some of the future routes have been proposed for further research on this novel field of 3D hybrid materials composed of sp2-carbons and inorganic nanomaterials.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.007
GPT teacher head0.194
Teacher spread0.188 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2013
Admission routes1
Has abstractyes

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