Graphene Inks Printed by Aerosol Jet for Sensing Applications: The Role of Dispersant on the Inks’ Formulation and Performance
Bibliographic record
Abstract
This study presents graphene inks produced through the liquid-phase exfoliation of graphene flakes in water using optimized concentrations of dispersants (gelatin, triton X-100, and tween-20). The study explores and compares the effectiveness of the three different dispersants in creating stable and conductive inks. These inks can be printed onto polyethylene terephthalate (PET) substrates using an aerosol jet printer. The investigation aims to identify the most suitable dispersant to formulate a high-quality graphene ink for potential applications in printed electronics, particularly in developing chemiresistive sensors for IoT applications. Our findings indicate that triton X-100 is the most effective dispersant for formulating graphene ink (GTr), which demonstrated electrical conductivity (4.5 S·cm−1), a high nanofiller concentration of graphene flakes (12.2%) with a size smaller than 200 nm (<200 nm), a low dispersant-to-graphene ratio (5%), good quality as measured by Raman spectroscopy (ID/IG ≈ 0.27), and good wettability (θ ≈ 42°) over PET. The GTr’s ecological benefits, combined with its excellent printability and good conductivity, make it an ideal candidate for manufacturing chemiresistive sensors that can be used for Internet of Things (IoT) healthcare and environmental applications.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".