Poly(aspartic acid) Electrospun Nanofiber Hydrogel Membrane-Based Reusable Colorimetric Sensor for Cu(II) and Fe(III) Detection
Bibliographic record
Abstract
Electrospun nanofiber membrane (ENM) with huge specific surface area is an ideal solid substrate for sensors. However, only a few ENMs are developed into colorimetric sensors and it is even more challenging to fabricate multiple-ion-responsive ENM-based colorimetric sensor. In this study, benefiting from the excellent metal ion adsorption ability of poly(aspartic acid) (PASP) and high specific surface area of nanofibers, a reusable colorimetric sensor utilizing PASP electrospun nanofiber hydrogel membrane (ENHM) was designed to detect Cu2+ and Fe3+ in aqueous solution with simple filtration. The sensor based on PASP–ENHM exhibited high sensitivity and selectivity, and colorimetric responses for Cu2+ and Fe3+ detection could be observed by the naked eye. Upon exposure to Cu2+ aqueous solution, the color of the sensor changed from white to blue with a naked eye detection limit of 0.3 mg/L, while it turned from white to yellow with a detection limit of 0.1 mg/L for Fe3+ detection. Furthermore, this sensor was reusable after metal ion extraction by the desorption process.
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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) | 0.001 | 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".