Cyclic Liquid-Phase Exfoliation of Electrically Conductive Graphene-Derivative Inks
Bibliographic record
Abstract
Advances in electrically conductive inks and printing technology have enabled microelectrodes, chemical sensors, wireless radio frequency identification tags, and bioelectronic circuits to be fabricated on mechanically flexible polymers, paper, and bioresorbable silk. Although conductive polymer and metal-based inks have been used to build functional devices, these materials are often expensive and may involve complicated toxic chemical processes. Recent research has demonstrated that graphene (G), and its derivatives, can be used to create water-based conductive inks. In this paper, an environmentally friendly and cost-effective G-based conductive ink is proposed. The ink is created by using a nontoxic hydrophilic cellulose derivative, Carboxymethyl Cellulose (CMC), to facilitate liquid phase exfoliation and stabilization of naturally hydrophobic G sheets in DI water at high concentrations. It is demonstrated that recycling of nonexfoliated material can produce inks at comparable concentrations. Physical dimensions and defect status of exfoliated G sheets are characterized. Inkjet deposition of thin and thick films is achieved using a binary solvent system and electrical performance of resulting films is investigated. Film morphology is shown to be consistent between thin and thick films. Experiments have demonstrated that G-CMC films of <;700 Ω/sq can be inkjet printed without use of dopants or dangerous solvents.
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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 | 0.000 |
| 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".