When the intensity of the incident light increases, the photocurrents of organic photodiodes (OPDs) exhibit relatively early saturation, due to which OPDs cannot easily detect objects against strong backlights, such as sunlight. In this study, this problem is addressed by introducing a light‐intensity‐dependent transition of the operation mode, such that the operation mode of the OPD autonomously changes to overcome early photocurrent saturation as the incident light intensity passes the threshold intensity. The photoactive layer is doped with a strategically designed and synthesized molecular switch, 1,2‐bis‐(2‐methyl‐5‐(4‐cyanobiphenyl)‐3‐thienyl)tetrafluorobenzene (DAB). The proposed OPD exhibits a typical OPD performance with an external quantum efficiency (EQE) of <100% and a photomultiplication behavior with an EQE of >100% under low‐intensity and high‐intensity light illuminations, respectively, thereby resulting in an extension of the photoresponse linearity to a light intensity of 434 mW cm−2. This unique and reversible transition of the operation mode can be explained by the unbalanced quantum yield of photocyclization/photocycloreversion of the molecular switch. The details of the operation mechanism are discussed in conjunction with various photophysical analyses. Furthermore, they establish a prototype image sensor with an array of molecular‐switch‐embedded OPD pixels to demonstrate their extremely high sensitivity against strong light illumination.

Herein, we explore the strategy of realizing a red-selective thin-film organic photodiode (OPD) by synthesizing a new copolymer with a highly selective red-absorption feature. PCZ-Th-DPP, with phenanthrocarbazole (PCZ) and diketopyrrolopyrrole (DPP) as donor and acceptor units, respectively, was strategically designed/synthesized based on a time-dependent density functional theory calculation, which predicted the significant suppression of the band II absorption of PCZ-Th-DPP due to the extremely efficient intramolecular charge transfer. We demonstrate that the synthesized PCZ-Th-DPP exhibits not only a high absorption coefficient within the red-selective band I region, as theoretically predicted, but also a preferential face-on intermolecular structure in the thin-film state, which is beneficial for vertical charge extraction as an outcome of a glancing incidence X-ray diffraction study. By employing PCZ-Th-DPP as a photoactive layer of Schottky OPD, to fully match its absorption characteristic to the spectral response of the red-selective OPD, we demonstrate a genuine red-selective specific detectivity in the order of 1012 Jones while maintaining a thin active layer thickness of ∼300 nm. This work demonstrates the possibility of realizing a full color image sensor with a synthetic approach to the constituting active layers without optical manipulation.

We show that crystallographic compatibility, quantum yield, and fatigue resistance are three important factors that diarylethene (DAE) should simultaneously satisfy to realize high-performance photoprogrammable polymer field-effect transistors (FETs). The enhancement of crystallographic compatibility achieved by locating DAE preferentially in the vicinity of intercrystallite tie chains is mainly dependent on the overall molecular volume of DAE. The quantum yield of DAE for photocyclization is dependent on the molar portion of the photoactive antiparallel conformer, while photocycloreversion is determined by both the aromatic stabilization energy of the closed isomer and allowed free space for each DAE molecule. While the chemical resistance of DAE relies entirely on its chemical structure, the electrical fatigue resistance of DAE-embedded FET depends on both the morphological/structural environment of the DAE/polymer blend and chemical resistance of the DAE molecule. To precisely control each of these determining factors of DAE-embedded polymer FETs, a series of DAE is synthesized and systematically analyzed. High-mobility DPPDTT is blended with various DAE derivatives as a matrix polymer. We show that strategic substitution of functional groups at the specific reaction site of DAE can lead to an ideal molecular switch for high-performance photoprogrammable polymer FETs with high photoprogrammable switching ratios of 4405, as well as high electrical fatigue resistance of up to 100 photoprogrammable switching steps. The physics behind the success of the optimized DAE structure is discussed using the results from various analysis techniques. We shed light on how the crystallographic compatibility, quantum yield, and fatigue resistance of DAE can vary with and be optimized by chemical modification of the DAE reaction site.

A fully water-based patterning method for polymer semiconductors was developed and utilized to realize high-precision lateral patterning of various polymers. Water-borne polymer colloids, wherein hydrophobic polymers are dispersed in water with the assistance of surfactant molecules, possess a hydrophilic surface when printed onto a substrate. When this surface is exposed to a washing molecule, the surface of the polymer film recovers its original hydrophobic nature. Such surfactant-induced solubility control (SISC) enables environmentally benign, water-processed, and high-precision patterning of various polymer semiconductors with totally different solubilities, so that fully water-processed polymer organic image sensors (OISs) can be realized. B-/G-/R-selective photodiodes with a pixel size of 100 μm × 100 μm were fabricated and patterned by this water-based SISC method, leading to not only high average specific detectivity values (over 1012 Jones) but also narrow pixel-to-pixel deviation. Thanks to the superiority of the SISC method, we demonstrate the image capturing ability of OISs without B-/G-/R-color filters, from a fully water-based fabrication process.

A molecular and synthetic approach to strengthen the switching performance of diarylethene (DAE)-based organic transistors is proposed. By tuning the length of alkyl side chains of the biphenyl unit attached to 1,2-bis(5-biphenyl-2-methylthien-3-yl)perfluorocyclopentene (DAE), we show that the molecular environment for reversible photoisomerization of DAEs can be optimized. Four different DAEs are synthesized with different alkyl chains (DAE_C0, DAE_C1, DAE_C6, and DAE_C10), and ITIC is chosen to construct a semiconductor matrix to maximize the quantum yield of photoconversion considering the complementary absorption range of both materials. From photophysical, structural, and morphological analyses, the longer alkyl chains inhibit intermolecular aggregation between DAEs and allow more hydrophobic surface properties of DAEs, thus improving molecular miscibility with ITIC. The improved molecular compatibility of DAEs with ITIC makes the overall bulk heterojunction film amorphous, allowing more free volume for reversible photoisomerization. Consequently, DAE_C6 exhibits the maximum quantum yield for both photocyclization and photocycloreversion, enabling high light-controlled on/off ratios in photoswitchable transistors. Furthermore, the exceptionally high DAE_C6 quantum yield enables robust fatigue resistance under repeated photoswitching with only a 30% decrease in the on/off ratio after 100 cycles. Overall, this work shows that not only the energy level but also the molecular compatibility can endow significant switching performances for molecular switches.

Precise and facile junction engineering of organic photodiodes (OPDs) via chemical doping is demonstrated.

In this study, it is shown that fluorinated azide, employed as a functional additive to photomultiplication-type organic photodiodes (PM-OPDs), can not only enhance an operational stability by freezing the morphology...

This paper presents a novel hybrid ensemble approach for classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as parallel neural-based strong clusters fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid ensemble approach. The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, and Pima Indian Diabetics. A comparative performance analysis of the proposed approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases.