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Record W4244357097 · doi:10.1149/ma2014-01/27/1135

Molecules in Circuits: A New Type of Microelectronics?

2014· article· en· W4244357097 on OpenAlexaff
Richard L. McCreery, Adam Johan Bergren, Sayed Nagy, Haijun Yan, Akhtar Bayat, Mykola Kondratenko

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldComputer Science
TopicQuantum-Dot Cellular Automata
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicroelectronicsMolecular electronicsNanotechnologySiliconElectronicsMaterials scienceNanometreMoleculeElectronic circuitChemistryOptoelectronicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Molecules may be considered electronic systems, with electrons rapidly moving through orbitals within molecules and also long distances in biological metabolism and photosynthesis. The prospect of incorporating molecules into microelectronic circuits based on silicon and metallic conductors has great potential for enhancing consumer electronics, providing solar energy conversion, and permitting new functions not possible with silicon. In order to combine the electronic properties of molecules with conventional microelectronics, we need to understand how to “connect” to molecules as well as how electrons are transported through molecules. Once the “rules” of charge transport through molecules are understood, it should be possible to “rationally design” new molecular electronic devices for valuable functions not currently possible with silicon. While Molecular Electronics holds great promise, it also presents significant challenges in handling and fabrication of devices with dimensions of only a few nanometers. We use surface chemistry, spectroscopy, and conjugated organic molecules to make “molecular junctions” consisting of a single layer of molecules a few nanometers thick between conducting carbon and copper electrodes, then we study the behavior of molecules as circuit elements. The primary goal is to design and build functional molecular electronic components to greatly enhance the already powerful world of silicon microelectronics. Recent references: (1) Yan, H.; Bergren, A. J.; McCreery, R.; Della Rocca, M. L.; Martin, P.; Lafarge, P.; Lacroix, J. C.; Activationless charge transport across 4.5 to 22 nm in molecular electronic junctions; Proceedings of the National Academy of Sciences 2013, 110, 5326. (2) McCreery, R.; Yan, H.; Bergren, A. J.; A Critical Perspective on Molecular Electronic Junctions: There is Plenty of Room in the Middle; Phys. Chem. Chem. Phys. 2013, 15, 1065. (3) Sayed, S. Y.; Fereiro, J. A.; Yan, H.; McCreery, R. L.; Bergren, A. J.; Charge transport in molecular electronic junctions: Compression of the molecular tunnel barrier in the strong coupling regime; Proceedings of the National Academy of Sciences 2012, 109, 11498. (4) Kumar, R.; Pillai, R. G.; Pekas, N.; Wu, Y.; McCreery, R. L.; Spatially Resolved Raman Spectroelectrochemistry of Solid-State Polythiophene/Viologen Memory Devices; Journal of the American Chemical Society 2012, 134, 14869.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.010
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.003

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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 designNot applicable
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

Citations2
Published2014
Admission routes1
Has abstractyes

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