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Record W2912558919 · doi:10.1149/ma2018-02/19/762

(Invited) Fabrication and Characterization of Carbon-Based Nanoscale Devices: Insights and Applications

2018· article· en· W2912558919 on OpenAlexaff
Adam Johan Bergren, Michael Hughes, Angela Beltaos, Bryan Szeto, A. Meldrum, Richard L. McCreery, Colin Van Dyck, Nikola Pekas

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNanotechnologyElectronicsFabricationNanoscopic scaleMaterials scienceTransistorCharacterization (materials science)Computer scienceElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

Nanoscale devices made from carbon-based materials are investigated for a variety of unique properties and features, including their promise to improve performance, decrease production costs, or provide totally new functionality relative to extant electronic devices. Because these devices have active areas composed of materials that are much thinner than those used in conventional devices, the details of the interfaces can often act to control the physics that lead to the operational device characteristics in unexpected ways. This is particularly true in molecular electronics, where design rules based on chemical intuition often fail to account for key physical aspects that dominate device behaviour. Thus, the ability to modify interfaces in a way that leads to control of device properties is a key to enabling next-generation devices based on nanoscale phenomenon. In addition, it is important to achieve an understanding of the processes that occur during carrier transport in nanoscale devices in order to obtain desirable functionality. This presentation will describe several aspects of carbon-based nanoscale devices, including fabrication and operation of molecular electronics and graphene field effect transistors (GFETs). After a discussion of some general physical principles that operate within the interfacial energy level alignment regime in molecular electoronics, a discussion of how light emission from nanoscale devices can be used to characterize them will be provided. In particular, we have used light emission from both large area molecular junctions and GFETs to understand important distance scales (elastic limits) and processes (transport and emission mechanisms). The data indicate that processes that involve hot carrier interactions with plasmonic structures in the devices lead to light emission, and that this phenomenon can be used to measure energy losses of carriers as they traverse a molecular layer. Results indicate that carriers can travel approximately 7 nm through a molecular junction before energy losses become significant, indicating that elastic transport is achieved for thin layers, but a transition in mechanism occurs for thicker films. The characteristics of the energy losses are reported for several different structures, providing insights into the nature of the transport in molecular electronics. Light emission from GFETs, on the other hand, appears to follow a novel mechanism that involves the excitation of plasmons in the graphene by hot carriers followed by decay through photon emission. By controlling the location of defects in the graphene lattice and the nanostructures around these scattering sites, emission could be localized to regions where this coupling is more optimized. In this second case, the devices may be able to be engineered through nanostructuring in order to gain control over the character of light emission. Finally, a few novel and emerging applications of nanoscale devices will be discussed, including using molecular devices in audio circuits, as well as for high frequency harmonic generation.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.019

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 head0.006
GPT teacher head0.192
Teacher spread0.187 · 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
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
Published2018
Admission routes1
Has abstractyes

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