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Record W2067078051 · doi:10.1063/1.1870112

Electrical switching in sol–gel derived Ag–SiO2 nanocomposite thin films

2005· article· en· W2067078051 on OpenAlexaff
Debabrata Sarkar, F. Cloutier, My Alı El Khakani

Bibliographic record

VenueJournal of Applied Physics · 2005
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsNanocompositeMaterials scienceElectrical resistivity and conductivityThin filmAtmospheric temperature rangeNanoparticleDiffusionUltravioletAnalytical Chemistry (journal)Sol-gelChemical engineeringComposite materialNanotechnologyOptoelectronicsChemistryThermodynamics

Abstract

fetched live from OpenAlex

The sol–gel technique has been used to produce Ag–SiO2 nanocomposite thin films consisting of silver nanoparticles embedded in a SiO2 matrix. The size of the silver nanoparticles is of about (4±0.2)nm when the firing temperature is in the (500–700°C) range, as determined from ultraviolet-visible spectroscopy analysis. The increase of the firing temperature beyond 300°C, was found to lead to an outer diffusion of Ag toward the surface. As a consequence the surface atomic concentration of Ag is found to increase from 1.3% to 12% for the as-dried (at 120°C) and for those further fired at 600°C, respectively. On the other hand, it is shown that the electrical behavior of these Ag–SiO2 nanocomposite films can drastically change from highly insulating to conducting depending on the firing temperature used. In the intermediate firing temperature (300–400°C) range, the films were found to exhibit a reversible switching behavior with a resistivity transition of about 7 orders of magnitude between the insulating (OFF) and the conducting (ON) state. Such an electrical switching could be explained by a change in the predominant conduction mechanism of the films.

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 score0.009
Threshold uncertainty score0.401

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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

Citations34
Published2005
Admission routes1
Has abstractyes

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