Living Dual Heat- and pH-Responsive Textiles
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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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".