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Record W3020018237 · doi:10.1002/pssb.202000019

Negative Differential Resistance and Hysteresis in Self‐Assembled Nanoscale Networks with Tunable Molecule‐to‐Nanoparticle Ratios

2020· article· en· W3020018237 on OpenAlexafffund
Anusha Venkataraman, Eberechukwu Victoria Amadi, Tristan Zaborniak, Po Zhang, Chris Papadopoulos

Bibliographic record

Venuephysica status solidi (b) · 2020
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHysteresisMaterials scienceNanoparticleQuantum tunnellingNanotechnologyNanoscopic scaleElectrodePlanarMolecular electronicsMoleculeElectronic circuitChemical physicsNanoelectronicsTopology (electrical circuits)OptoelectronicsCondensed matter physicsComputer scienceChemistryElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Electronic transport is investigated through self‐assembled benzenedithiol–gold nanoparticle networks with tunable molecule‐to‐particle ratios (1:5–50:1) deposited between planar electrodes. Two‐terminal current–voltage measurements of the networks display linear behavior at low bias, which is described using a circuit model that accounts for different network morphologies, tunable via molecule‐to‐nanoparticle ratio, and defects. At larger biases, nonlinear negative differential resistance and hysteresis behavior are observed for different molecular concentrations, which can be attributed to a combination of field‐assisted tunneling and charge trapping occurring in the nanoscale networks. The directed self‐assembly of benzenedithiol–metal nanoparticle molecular electronic networks is suggested for molecular integrated circuits in applications such as memory, switching, hardware security, and computing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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.

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

Citations4
Published2020
Admission routes2
Has abstractyes

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