Understanding Hydrothermally Reduced Graphene Oxide Hydrogels: From Reaction Products to Hydrogel Properties
Bibliographic record
Abstract
We studied the chemical processes that take place during hydrothermal gelation of graphene oxide (GO), quantifying the reaction products generated during hydrothermal reduction. The gelation proceeds with disproportionation of GO yielding a large amount of CO 2 (about a quarter of the original mass of GO), organic acidic fragments, and CO. The CO 2 that is formed is trapped in the hydrogel creating macroscopic voids which can lead to cracking of the hydrogel during compression. We were able to quantify the amount of CO 2 produced in situ by adding ammonia during the synthesis, and converting CO 2 into ionic carbonate species that we could easily quantify by titration. We used titration to evaluate the formation of organic acidic fragments too and evaluated the amount of H 2 O and CO produced by thermogravimetric analysis and mass balance. The conversion of CO 2 into ionic species allowed us to produce void-free hydrogels which remain structurally stable after extensive compression. However, such hydrogels on average showed lower mechanical strength and electrical conductivity than the hydrogels with voids. This is a result of the difference in chemistry and morphology between hydrogels reduced under acidic pH and basic pH. Our work provides for the first time a clear quantitative estimate of CO 2 evolution and organic fragment formation during hydrothermal reduction of GO, an overall picture of the reaction products, and a deepened understanding of the conditions that can be used to prepare stronger and more conductive graphene hydrogels and aerogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".