Potentiometric pH Sensor Based on Flexible Screen-Printable Polyaniline Composite for Textile-Based Microfluidic Applications
Bibliographic record
Abstract
Skin pH can be used for monitoring infections in a healing wound, the onset of dermatitis, and hydration in sports medicine, but many challenges exist in integrating conventional sensing materials into wearable platforms. We present the development of a flexible, textile-based, screen-printed electrode system for biosensing applications, and demonstrate flexible polyaniline (PANI) composite-based potentiometric sensors on a textile substrate for real-time pH measurement. The pH response of the optimized PANI/dodecylbenzene sulfonic acid/screen-printing ink composite is compared to electropolymerized and drop-cast PANI sensors via open circuit potential measurements. High sensitivity was observed for all sensors between pH 3-10, with a composite based on PANI emeraldine base, demonstrating sufficient response time and a linear sensitivity of -27.9 mV/pH. This exceeded prior flexible screen-printed pH sensors in which all parts of the sensor, including the pH sensing material, are screen-printed. Even better sensitivity was observed for a PANI emeraldine salt composite (-42.6 mV/pH), although the response was less linear. Furthermore, the sensor was integrated into a screen-printed microfluidic channel demonstrating sample isolation during measurement for wearable, micro cloth-based analytical devices. This is the first fully screen-printed flexible PANI composite pH sensor demonstrated on a textile substrate that can additionally be integrated with textile-based microfluidic channels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".