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Record W7023860005

Plasma functionalization of graphene nanoflakes for non-noble catalyst in fuel cells

2013· dissertation· en· W7023860005 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2013
Typedissertation
Languageen
FieldComputer Science
TopicPolynomial and algebraic computation
Canadian institutionsnot available
Fundersnot available
KeywordsGrapheneCatalysisPlatinumNanocrystalline materialNanomaterialsGraphitePalladiumCrystallinity
DOInot available

Abstract

fetched live from OpenAlex

Two major obstacles currently limit the commercial viability of proton exchange membrane fuel cells (PEMFCs): cost and operating life. The most important contribution to the high cost of these systems is the use of platinum (Pt) as a catalyst, especially on the cathode where the oxygen reduction reaction (ORR) takes place. This thesis is part of the intensive international research efforts to find an alternative substitute for platinum. Doped carbon nanomaterials have been identified as a potential replacement for platinum ORR-electrocatalyst due to their excellent electrical conductivity and chemical resistance in acidic and basic environments. By doping the carbon nanomaterials with nitrogen, in the preferred pyridinic and quaternary forms, iron can be coordinated to complete the catalytic sites on an atomic scale. Nanocrystalline powder has recently been developed in the Plasma Processing Laboratory (PPL) at McGill University. The particles constituting the powder, in the form of graphene nanoflakes (GNFs), are formed by the superposition of ten graphene layers on average and have a spatial extension on the order of hundreds of nanometers. These planes have many terminating edges upon which nitrogen can be incorporated due to their high reactivity. The crystallinity also leads to a highly stable material paving the way for a promising catalyst replacement in the PEMFC.The objective of this thesis is to take these crystalline GNFs and dope them with nitrogen in high quantities on the edges of the graphene planes in pyridinic and quaternary forms to create the catalytic sites necessary for ORR. An inductively-coupled thermal plasma (ICP) is used to dissociate methane at very high temperatures, with homogeneous GNF nucleation commencing shortly after by way of rapid quenching. Nitrogen doping occurs in a second treatment phase by manipulating plasma conditions in order to create excited and dissociated nitrogen species that react at the edges of the GNFs.Nitrogen doping up to 33.4 at.%Ntotal has been demonstrated, which bests any other nitrogen-doped graphene by at least a factor of 2.6 and even the best nitrogen-doped carbonaceous material by 67%. Pyridinic and quaternary nitrogen constitute 8.2 at.%Npyrid and 4.9 at.%Nquat, respectively. This has been done whilst maintaining the crystalline structure and without introducing defects or impurities that would otherwise affect crystallinity and durability of these materials in future potential applications. Sequential in-situ GNF synthesis and deposition/dispersion onto a carbon cloth, which functions as the gas diffusion layer (GDL) in fuel cells, has also been demonstrated. Solid anchoring of the deposited GNFs on the individual carbon fibers is observed, and columnar growth with open film porosity reveals GNF films of micrometer-scale thicknesses. These films also exhibit desirable properties required for the ORR: porosity, homogeneity over a large area, good contact to the electrical transport throughout the network of particles and accessibility to the catalytic sites. The obtained properties seem in fact unmatched by catalytic particle ink applications commonly used in the manufacture of the catalyst layer. This in-situ work is promising and original, establishing a potential new method of producing membrane electrode assemblies (MEAs) in PEM fuel cell manufacturing.This new graphene nanomaterial could also pave the way for its potential use in supercapacitors, solar cells, biosensors, batteries, fuel storage, field-effect transistors, filtration and electrochemical devices, in addition to the fuel cell catalysis applications under study.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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