Impact of Interlayer Optimization on the Performance of Flexible, Self‐Powered Organic Photodetectors for Ambient Light‐Based PPG Sensing
Bibliographic record
Abstract
Abstract This study explores optimizing interlayers in flexible, self‐powered organic photodetectors (OPDs). Integration of conductive PEDOT:PSS (poly(3,4‐ethylenedioxythiophene)‐poly(styrenesulfonate)), PH1000 flexible anodes on PET substrates demonstrates that PH1000 outperforms traditional ITO (Indium tin oxide) anodes, offering higher optical transmittance, minimal surface roughness, and superior mechanical stability, with less than 10% resistance change after 2000 bending cycles. Various hole transport layers (HTLs) and electron transport layers (ETLs) are studied, showing that their incorporation reduces dark current and increases photocurrent at zero bias. Mott‐Schottky analysis reveals that HTLs and ETLs improve exciton dissociation. CuSCN (Copper(I) thiocyanate) HTL and PFN‐Br (Poly(9,9‐bis(3′‐(N,N‐dimethyl)‐N‐ethylammoinium‐propyl‐2,7‐fluorene)‐alt‐2,7 (9,9‐dioctylfluorene))dibromide) ETL provide the highest performance enhancements, reducing trap density and improving exciton dissociation. Capacitance‐frequency measurements and photoluminescence spectroscopy confirm enhanced exciton dissociation due to reduced binding energy, especially for CuSCN. Charge collection efficiency (CCE) is significantly improved, with CuSCN ETL and PFN‐Br ETL achieving a CCE of over 22.93% at zero bias. PH1000‐based, zero‐biased OPDs with CuSCN and PFN‐Br show a maximum responsivity of 0.23 A/W, specific detectivity of 3.89 × 10 1 2 Jones, rapid response times, and excellent mechanical stability. These OPDs, integrated into an ambient light‐based photoplethysmography sensing system, provide accurate heart rate monitoring, demonstrating their potential for next‐generation low power wearables.
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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 | 0.001 | 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".