An infrared photothermoelectric detector enabled by MXene and PEDOT:PSS composite for noncontact fingertip tracking
Bibliographic record
Abstract
Abstract Photothermoelectric (PTE) detectors functioning on the infrared spectrum show much potential for use in many fields, such as energy harvesting, nondestructive monitoring, and imaging fields. Recent advances in low-dimensional and semiconductor materials research have facilitated new opportunities for PTE detectors to be applied in material and structural design. However, these materials applied in PTE detectors face some challenges, such as unstable properties, high infrared reflection, and miniaturization issues. Herein, we report our fabrication of scalable bias-free PTE detectors based on Ti3C2and poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) composites and characterization of their composite morphology and broadband photoresponse. We also discuss various PTE engineering strategies, including substrate choices, electrode types, deposition methods, and vacuum conditions. Furthermore, we simulate metamaterials using different materials and hole sizes and fabricated a gold metamaterial with a bottom-up configuration by simultaneously combining MXene and polymer, which achieved an infrared photoresponse enhancement. Finally, we demonstrate a fingertip gesture response using the metamaterial-integrated PTE detector. This research proposes numerous implications of MXene and its related composites for wearable devices and Internet of Things (IoT) applications, such as the continuous biomedical tracking of human health conditions.
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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".