Low-Power Organic LED Fabricated by a Novel Solution-Based Process for Photoplethysmography Sensing
Bibliographic record
Abstract
Power consumption level of electronic devices has been always considered as an important factor for a wide range of applications. In this study, a novel fabrication step, namely pre-drying, applicable to solution-based fabrication processes is proposed to minimize the overall power consumption of organic light-emitting diodes (OLEDs). The pre-dry step allows the solution to dwell on the substrate for a certain short time before the spin coater is turned on. It is experimentally shown that the pre-drying step for OLEDs with the hole transfer layer (HTL) of Poly (3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) can reduce the ohmic-loss by a factor of 2.41, in addition to reducing the turn-on voltage by 0.9 V and increasing the luminance 1.3 times at 15 V in comparison to the fabricated OLED without pre-drying step. To further minimize the power consumption of OLEDs, it is proposed to replace PEDOT:PSS with copper(I) thiocyanate (CuSCN) as HTL, which can be deposited by a solution fabrication process including the pre-drying step. A comparison among the fabricated OLEDs with the pre-dried HTLs made of CuSCN and PEDOT:PSS indicates that the generated luminance increases by a factor of 4.28 from 2.8 to 12 kcd/m2for an identical voltage of 11 V. Due to the superior performance of the proposed OLED based on the pre-dried HTL of CuSCN, its behavior as a light source for photoplethysmography (PPG) sensing systems is surveyed. In this context, the PPG sensor is implemented in the form of a flexible-hybrid sensor system in which the fabricated OLED and a commercial photodiode are housed on a flexible substrate. Results confirm that the implemented flexible-hybrid PPG sensor operating in reflectance mode can acquire high quality PPG signals with relatively low power consumption and accurately estimate heart rate. The competency of the proposed OLED as a low-power solution for PPG sensor technology is highlighted by comparing its performance with the state of the art.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".