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High resolution <scp>STEM</scp> and <scp>EELS</scp> investigation of <scp>N</scp> ‐doped carbon allotropes decorated with noble metal atom catalysts

2016· other· en· W4237753229 on OpenAlexafffund
Samantha Stambula, Matthieu Bugnet, Niancai Cheng, Andrew Lushington, Xueliang Sun, Gianluigi A. Botton

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern UniversityMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsGrapheneScanning transmission electron microscopyMaterials scienceHigh-resolution transmission electron microscopyDangling bondNanotechnologyChemical engineeringNanoparticleChemisorptionCatalysisTransmission electron microscopyChemistrySiliconOptoelectronics

Abstract

fetched live from OpenAlex

Graphene's unique properties make this material an ideal catalyst support for use in the proton exchange membrane fuel cell (PEMFC). Graphene offers increased electrical conductivity and a larger surface area for catalyst deposition compared to other catalyst support material, such as carbon black.[1,2]. Utilizing graphene as the electrode support also results in increased chemical stability due to the sp 2 bonding; however, this precludes the availability of dangling bonds for chemisorption, thus leading to a poor Pt distribution and the formation of large nanoparticles (NPs). Functionalization can be used to introduce nucleation sites into the graphene lattice, where N‐dopants have been shown to increase the Pt‐C binding energy.[3] The atomic layer deposition (ALD) technique creates ultra‐small NPs (&lt;1 nm) which, when combined with the enhanced catalyst binding energy from the N‐doped graphene support can produce stable Pt clusters/atoms, resulting in increased Pt utilization while subsequently reducing the cost.[4] To fully understand and design a more efficient PEMFC the material must be characterized at the atomic level. This can be accomplished by the use of aberration‐corrected transmission electron microscopy (TEM). High resolution TEM (HRTEM) can be utilized to observe the structure of the graphene lattice, while high‐angle annular dark‐field (HAADF) scanning transmission electron microscopy (STEM) can be used to examine the Pt clusters' size and distribution. Furthermore, electron energy loss spectroscopy (EELS) can be utilized to examine local chemical composition and bonding of the probed atoms from nanometer scaled areas in order to reveal the coordination of the N‐dopant species in the graphene lattice.[4] Here we used a FEI Titan 80‐300 Cubed TEM equipped with aberration correctors of the probe and imaging forming lens, and a monochromator for optimal imaging at a low accelerating voltage. Low‐energy condition HRTEM (figure 1) and STEM imaging were used to investigate thermally‐exfoliated graphene (figure 2). From these observations, we deduced that the graphene lattice maintained its short range order; however, the long range order was lost due to the presence of steps, folds, defects, and incomplete exfoliation.[4] The mass‐thickness dependence in HAADF imaging confirmed the presence of numerous steps and ledges in the N‐doped graphene nanosheets. More importantly the Z‐contrast in the HAADF images revealed that Pt was present as stable single atoms and clusters on the N‐doped graphene and that Pt NPs were not detected.[4] Lastly, using EELS and the N‐K edge fine structures, we probed the distribution of N‐dopants within the nanosheets (figure 3). Significant variations in the local concentration of N‐dopants were observed among the graphene sheets, in which an inhomogeneous distribution was discovered. Such effects have not been observed in previous broad beam techniques.[4] To futher investigate the effect of N‐doping, mutliwalled N‐doped CNTs (N‐CNTs) were produced and ALD was utilized to deposit Pd. Initial investigations showed the stabilization of single Pd atoms with N‐doping; however, small NPs were also formed (figure 4). It was demonstrated with EELS that many of the N‐CNTs were filled with N 2 gas. Using controlled evacuation of the multiwalled CNTs induced by the electron beam, we were able to reveal the intrinsic N‐type doping within the CNT walls.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.473
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreOther

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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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Same venueEuropean Microscopy Congress 2016: ProceedingsSame topicElectrocatalysts for Energy ConversionFrench-language works237,207