Cu <sub> <i>x</i> </sub> S thin films for printed memory cells and temperature sensors
Bibliographic record
Abstract
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 a q 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 β 25 , 80 = ( 656 ± 5 ) K. Resistive switching is observed for a current compliance between 0.1 and 1000 µ A, with a resistance ratio R O F F /R O N 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".