Fabrication of fluorescent pH-responsive protein–textile composites
Bibliographic record
Abstract
Wearable pH sensors are useful tools in the healthcare and fitness industries, allowing consumers to access information related to their health in a convenient manner via the monitoring of body fluids. In this work, we tailored novel protein-textile composites to fluorescently respond to changing pH. To do so, we used amyloid curli fibers, a key component in the extracellular matrix of Escherichia coli, as genetic scaffold to fuse a pH-responsive fluorescent protein, pHuji. Engineered amyloids form macroscopic and environmentally resistant aggregates that we isolated to use as stand-alone hydrogel-based sensors, and that we trapped within textile matrices to create responsive bio-composites. We showed that these composites were mechanically robust and vapor-permeable, thus exhibiting favorable characteristics for wearable platforms. CsgA-pHuji fibers integrated in the textile allowed the final device to respond to pH changes and distinguish between alkaline and acidic solutions. We demonstrated that the resulting composites could sustain their fluorescence response over days, and that their sensing ability was reversible for at least 10 high/low pH cycles, highlighting their potential for continuous monitoring. Overall, we introduced a biosynthesized amyloid-based textile composite that could be used as biosensing patch for a variety of applications in the smart textile industry.
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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".