Novel Polymer Donors Based on Strong Electron‐Withdrawing Unit and Non‐Covalent Interactions for Ultra‐Sensitive Near‐Infrared Organic Photodetectors in Health Monitoring

Abstract Near‐infrared (NIR) organic photodetectors (OPDs) have many important potential applications, while their detection performance is limited by high dark current density ( J d ) and low responsivity ( R ), which are closely related to the lack of systematic studies on polymer donors. Herein, two novel polymer donors, named PBPyT‐EH and PBPyT‐BO, are demonstrated with strong electron‐withdrawing units. Among them, PBPyT‐EH exhibits superior morphology and charge characteristics owing to the subtle regulation of non‐covalent interactions. Consequently, an impressively high R of 0.54 A W −1 and suppressed J d of 1.88 × 10 −9 A cm −2 at −1 V can be achieved, leading to a remarkable specific detectivity of 2.20 × 10 13 Jones at −1 V and over 10 14 Jones in self‐powered mode (0 V), representing one of the highest detection performances. Meanwhile, the pulse signals can be clearly monitored based on the optimized device. This work illustrates the viability of PBPyT‐polymer donors and offers a promising pathway for the practical application of sensitive NIR OPDs in health monitoring.

Authors

Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2025-09-18
DOI
https://doi.org/10.1002/adfm.202516379
Citations
1
Primary Topic
Analytical Chemistry and Sensors
Type
article
Field-Weighted Citation Impact
0.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Novel Polymer Donors Based on Strong Electron‐Withdrawing Unit and Non‐Covalent Interactions for Ultra‐Sensitive Near‐Infrared Organic Photodetectors in Health Monitoring

S. Yang, Tong Liu, Junhao Chu, Tan Wang et al.
1 citations
Advanced Functional Materials
Analytical Chemistry and Sensors
0.74
article

Novel Polymer Donors Based on Strong Electron‐Withdrawing Unit and Non‐Covalent Interactions for Ultra‐Sensitive Near‐Infrared Organic Photodetectors in Health Monitoring

S. Yang, Tong Liu, Junhao Chu, Tan Wang, Xichang Bao, Yuchang Zhao, Yongfu Li, Jianxiao Wang, Bin Tang, Fuzhen Bi
article en
1 citations

Abstract

Abstract Near‐infrared (NIR) organic photodetectors (OPDs) have many important potential applications, while their detection performance is limited by high dark current density ( J d ) and low responsivity ( R ), which are closely related to the lack of systematic studies on polymer donors. Herein, two novel polymer donors, named PBPyT‐EH and PBPyT‐BO, are demonstrated with strong electron‐withdrawing units. Among them, PBPyT‐EH exhibits superior morphology and charge characteristics owing to the subtle regulation of non‐covalent interactions. Consequently, an impressively high R of 0.54 A W −1 and suppressed J d of 1.88 × 10 −9 A cm −2 at −1 V can be achieved, leading to a remarkable specific detectivity of 2.20 × 10 13 Jones at −1 V and over 10 14 Jones in self‐powered mode (0 V), representing one of the highest detection performances. Meanwhile, the pulse signals can be clearly monitored based on the optimized device. This work illustrates the viability of PBPyT‐polymer donors and offers a promising pathway for the practical application of sensitive NIR OPDs in health monitoring.

Advanced Functional MaterialsVol. 36(14)
Shandong University (CN), Beijing Solar Energy Research Institute (CN), Shanghai Institute of Technical Physics (CN), Qingdao Institute of Bioenergy and Bioprocess Technology (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 27%
Analytical Chemistry and Sensors
0.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.