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Record W4224085244 · doi:10.1088/2058-8585/ac6783

Cu <sub> <i>x</i> </sub> S thin films for printed memory cells and temperature sensors

2022· article· en· W4224085244 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFlexible and Printed Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaBayerische ForschungsallianzMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsMaterials scienceAnalytical Chemistry (journal)Chemistry

Abstract

fetched live from OpenAlex

Abstract Printed electronics require a multitude of various inks for different applications which leads to compatibility issues for their integration. We present a procedure to deposit a thin layer of Cu x S via inkjet printing of Na 2 S <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi/> <mml:mrow> <mml:mi mathvariant="normal">a</mml:mi> <mml:mi mathvariant="normal">q</mml:mi> </mml:mrow> </mml:msub> </mml:math> on a thermally grown or inkjet-printed Cu surface that provides applications in electrochemical metallization memory cells (ECMs) or temperature sensors. The nanosized transformation from Cu to Cu x S is investigated via confocal microscopy, scanning electron microscopy (SEM), as well as energy-dispersive x-ray spectroscopy (EDX). We analyze individual responses from the sensor and memory and evaluate their respective potential in printed electronics. The negative temperature coefficient of the semiconducting Cu x S is determined to be <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>β</mml:mi> <mml:mrow> <mml:mn>25</mml:mn> <mml:mo>,</mml:mo> <mml:mn>80</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mo stretchy="false">(</mml:mo> <mml:mn>656</mml:mn> <mml:mo>±</mml:mo> <mml:mn>5</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> K. Resistive switching is observed for a current compliance between 0.1 and 1000 µ A, with a resistance ratio R <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi/> <mml:mrow> <mml:mi mathvariant="normal">O</mml:mi> <mml:mi mathvariant="normal">F</mml:mi> <mml:mi mathvariant="normal">F</mml:mi> </mml:mrow> </mml:msub> </mml:math> /R <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi/> <mml:mrow> <mml:mi mathvariant="normal">O</mml:mi> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:msub> </mml:math> up to 10 5 . The use of the same inks and processes for the memory and sensor components paves the way for new and customized designs for smart logistics applications where temperature monitoring is required.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.050
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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