Electrode-Integrated Textile-Based Sensors for In Situ Temperature and Relative Humidity Monitoring in Electrochemical Cells
Bibliographic record
Abstract
Temperature and humidity measurements in electrochemical energy devices are essential for maximizing their overall performance under different operating conditions and avoiding hazardous consequences that may arise from the malfunction of these systems. Using sensors for in situ measurements of temperature and relative humidity (RH) is a promising approach for continuous monitoring and management of electrochemical power sources. Here, we report on the feasibility of using thread-based sensors for in situ measurements of temperature and RH in proton exchange membrane fuel cells (PEMFCs) as an example of electrochemical energy devices. Commodity threads are low-cost and flexible materials that hold great promise for the creation of complex three-dimensional (3D) circuits using well-established textile methods such as weaving, braiding, and embroidering. Ex situ and in situ characterization show that threads can be introduced in the gas diffusion layer (GDL) structure to inscribe water highways within the GDL with minimal impact on the GDL microstructure and transport properties. Fluorinated ethylene propylene (FEP) is coated on thread-based sensors to decouple the response to temperature and humidity; the resulting threads achieve a linear change of resistance with temperature (-0.31%/°C), while RH is monitored with a second thread coated with poly(dimethylsiloxane) (PDMS). The combination of both threads allows for minimally invasive and dynamically responsive monitoring of local temperature and RH within the electrode of PEMFCs.
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 | 0.001 |
| 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".