Living Dual Heat- and pH-Responsive Textiles
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
Smart textiles that integrate multiple environmental sensing capabilities are an emerging frontier in wearable technology. In this study, we developed dual pH- and temperature-responsive textiles by combining engineered bacterial systems with bacterially derived proteins. For temperature sensing, we characterized the properties of a heat sensitive promoter, P hs, in Escherichia coli ( E. coli ) using enhanced green fluorescent protein as a reporter. Our findings demonstrate that the P hs promoter drives elevated gene expression at temperatures between 37 and 43 °C, maintaining sustained activity for several hours. Moreover, we found that short heat shocks can significantly boost expression levels of the P hs promoter. We successfully integrated E. coli expressing P hs -EGFP cells onto textiles and confirmed their ability to retain heat-responsive behavior after integration. To achieve pH responsiveness, we utilized curli fibers, genetically engineered to incorporate a pH-sensitive fluorescent protein, pHuji. pH-sensing curli fibers are bacterial proteins that have a proven track record of creating stable bioresponsive textile coatings. By embedding P hs -EGFP-expressing bacteria within curli fiber coatings, we created a dual-responsive textile capable of differentiating between acidic and alkaline environments while simultaneously responding to thermal stimuli. These multifunctional textiles exhibited dual environmental response and sensing capabilities. This work establishes a proof-of-concept for creating smart living textiles with modular functionalities, paving the way toward advanced bioresponsive materials.
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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 | 0.000 |
| 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 it