Flexible Infrared Detectors Based on MWCNT/PEDOT:PSS Nanocomposites by Laser Ablation <sup>*</sup>
Bibliographic record
Abstract
Photothermoelectric (PTE) detectors have emerged as a promising technology for broadband infrared (IR) detection due to their unique ability to convert thermal radiation into electrical signals without requiring an external bias voltage, enabling operation under ambient conditions. However, conventional PTE detectors often suffer from complex fabrication processes, poor material stability, limited flexibility, and inconsistent film formation, hindering their practical applications. To address these challenges, we present a novel flexible IR PTE detector based on a multi-walled carbon nanotube (MWCNT)/poly(3,4-ethylenedioxythiophene):poly (styrenesulfonate) (PEDOT:PSS) nanocomposites, fabricated using a laser ablation patterning technique. The device is realized through a low-cost two-step process: (1) preparation of a stable MWCNT/PEDOT:PSS dispersion into film and (2) direct laser patterning to form well-defined, homogeneous nanocomposite structures. By optimizing the laser ablation parameters, we fabricated the detector exhibits outstanding mechanical flexibility, maintaining stable performance under 100 bending cycles. With its excellent broadband IR detection, flexibility, this MWCNT/PEDOT:PSS-based PTE detector opens new possibilities for wearable health monitoring and smart Internet-of-things (IoTs) systems.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".