Development of Highly pH-Sensitive Hybrid Membranes by Simultaneous Electrospinning of Amphiphilic Nanofibers Reinforced with Graphene Oxide
Bibliographic record
Abstract
Nanofibrous-based pH sensors have shown promise in a wide range of industrial and medical applications due to their fast response time and good mechanical properties. In the present study, we fabricated pH-sensitive sensors of nanofibrous membranes by electrospinning polyurethane (PU)/poly 2-acrylamido-2-methylpropanesulfonic acid (PAMPS)/graphene oxide (GO) with indicator dyes. The morphology of the electrospun nanofibers was examined using scanning electron microscopy (SEM). The effect of hydrophilic polymer ratio and concentration of GO on the sensing response time was investigated. The sensitivity of the membranes was studied over a wide pH range (1-8) in solution tests, with color change measured by calculating total color difference using UV-vis spectroscopy. The membranes were also subjected to vapor tests at three different pH values (1, 4, 8). SEM results show the successful fabrication of bimodal fiber diameter distributions of PU (mean fiber diameter 519 nm) and PAMPS (mean fiber diameter 78 nm). Sensing response time decreased dramatically with increasing concentrations of PAMPS and GO. The hybrid hydrophobic/hydrophilic/GO nanofibrous membranes are capable of instantly responding to changes in solution pH as well as detecting pH changes in chemical vapor solution in as little as 7 s.
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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) | 0.000 | 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 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".