Coating of Threads with Fluorescent Curli Fibers for pH Sensing
Bibliographic record
Abstract
Threads coated with bioresponsive materials hold promise for innovative wearable diagnostics. However, most thread coatings reported so far cannot be easily customized for different analytes and frequently incorporate non-biodegradable components. Most optically active thread coatings rely on dyes, which often exhibit irreversible responses. In this work, we propose a biosensing coating for threads using curli fibers. Curli fibers are self-assembling fibers of the protein CsgA that can be genetically engineered to sense rapidly evolving diagnostic targets. We first established a simple electrostatic-mediated absorption protocol for coating anionic cotton threads with anionic curli fibers using an intervening cationic chitosan layer. We applied this protocol to two types of pH-sensing curli fibers, displaying either fluorescent pHuji or mCitrine proteins. This process ensures extensive curli coating over the entire thread surface using only water-based solvents. The resulting protein-coated threads are moderately hydrophobic, stretchable, and can monitor pH changes in real time through fluorescence. The coatings are also stable and functional on the surface for over 25 cycles of use, highlighting their potential for reusable practical applications. This straightforward and adaptable protocol can be extended to coat threads with diverse sensing and responsive capabilities for intelligent clothing.
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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".