Fabrication of Conductive Graphene Aerogel Membranes with Selective Adsorbent Capabilities
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.
Bibliographic record
Abstract
Permeable membranes are useful in multifarious applications, e.g. gas and water filters, sensors, cellular membranes (e.g. artificial skin), drug delivery patches, and high energy density batteries etc. Aerogel is a polymeric material fabricated by removing the liquid phase in a gel, creating a low density 3D scaffold with ultra-porous microstructures. Research has been focused on building several low cost environmentally friendly aerogels based on crosslinking nanocellulose with 1,4-butanediol diglycidyl ether (BDE), followed by a freeze drying and a thermal curing process. Conductive aerogels involving crosslinking nanocellulose and graphene oxide (produced in house using Hummers method) with BDE were also made. Graphene aerogels are also known as aerographene, a unique conductive material with astounding structural robustness, ultra-low density, absorbency (capacity to absorb over 500 times its own weight) and elasticity, easily retaining original shape after numerous compression cycles. Both types of aerogels are highly stable and are capable of rapidly and repeatedly separating oil from water via adsorption, like a sponge. Further work includes the removal of simple cations such as Na+, Ca2+, K+ etc using aerographene under voltage, which could point to its use for water desalination. The combination of adsorption capabilities and ease of use makes the produced aerographene a potential solution to remove a diverse range of contaminants from a water supply. * Indicates faculty mentor.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it