Correlated magnetic field effects on carriers and excitons in single-carrier exciplex-based organic photodiodes.

Magnetic field effects in nonmagnetic organic semiconductors (OMFEs) are attracting increasing attention because of the fingerprint characteristics of their line shapes when they are used to analyze the dynamic processes of organic photodiodes. However, the origin and correlation of OMFEs on carriers and excitons are currently still major challenges for researchers. In this study, we strategically designed exciplex-based single-carrier devices to effectively separate carriers and excitons and investigate their OMFEs and relationships. It is found that the obvious positive magneto-conductance (MC) with illumination is not only related to the formation of charge transfer state (CT) excitons, but is also caused by the secondary recombination process between dissociated carriers from the photo-generated excitons and the injected carriers. Moreover, a negative MC appears under dark conditions, reflecting the intrinsic magnetic field effects on carrier transport. The relationships of the OMFEs with the excitons and carriers were further studied by measuring the incident-wavelength dependent photo-induced MC and the magneto-photoluminescence (MPL) of the exciplex films. The two films show similar amplitude-dependent curves, which are contributed by their donor-acceptor absorption properties. Moreover, the strong triplet-polaron annihilation (TPA) processes exhibited in this device afford different amplitude-dependent photo-induced MCs.

Research of intelligent reasoning system of Arabidopsis thaliana phenotype based on automated multi-task machine learning.

Traditional machine learning in plant phenotyping research requires the assistance of professional data scientists and domain experts to adjust the structure and hy-perparameters tuning of neural network models with much human intervention, making the model training and deployment ineffective. In this paper, the automated machine learning method is researched to construct a multi-task learning model for Arabidopsis thaliana genotype classification, leaf number, and leaf area regression tasks. The experimental results show that the genotype classification task's accuracy and recall achieved 98.78%, precision reached 98.83%, and classification F 1 value reached 98.79%, as well as the R 2 of leaf number regression task and leaf area regression task reached 0.9925 and 0.9997 respectively. The experimental results demonstrated that the multi-task automated machine learning model can combine the benefits of multi-task learning and automated machine learning, which achieved more bias information from related tasks and improved the overall classification and prediction effect. Additionally, the model can be created automatically and has a high degree of generalization for better phenotype reasoning. In addition, the trained model and system can be deployed on cloud platforms for convenient application.

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices.

Classification of rice disease is one significant research topics in rice phenotyping. Recognition of rice diseases such as Bacterialblight, Blast, Brownspot, Leaf smut, and Tungro are a critical research field in rice phenotyping. However, accurately identifying these diseases is a challenging issue due to their high phenotypic similarity. To address this challenge, we propose a rice disease phenotype identification framework which utilizing the transfer learning and SENet with attention mechanism on the cloud platform. The pre-trained parameters are transferred to the SENet network for parameters optimization. To capture distinctive features of rice diseases, the attention mechanism is applied for feature extracting. Experiment test and comparative analysis are conducted on the real rice disease datasets. The experimental results show that the accuracy of our method reaches 0.9573. Furthermore, we implemented a rice disease phenotype recognition platform based microservices architecture and deployed it on the cloud, which can provide rice disease phenotype recognition task as a service for easy usage.

Improved transient electroluminescence technique based on time-correlated single-photon counting technology to evaluate organic mobility.

The transient electroluminescence (EL) technique is widely used to evaluate the carrier mobility in the field of organic light emitting diodes. The traditional analog detection strategy using oscilloscopes is generally limited since the background noise causes an underestimation of the mobility value. In this paper, we utilize time-correlated single-photon counting (TCSPC) to probe the transient EL for mobility calculation. The measurements on tris(8-hydroxyquinoline) aluminum (Alq3) show that the electron mobilities obtained using the TCSPC technique are slightly higher than those obtained from the analog method at all the investigated voltages. Moreover, the TCSPC mobilities demonstrate weaker dependence on the root of electrical field compared to the oscilloscope mobilities. These improvements are attributed to the unique principle of TCSPC, which quantifies the EL intensity by counting the number of single-photon pulses, improving its single-photon sensitivity and eliminating the negative impacts of electrical noise. These advantages make TCSPC a powerful technique in the characterization of time-resolved electroluminescence.

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution.

Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.

Unraveling the Important Role of High-Lying Triplet-Lowest Excited Singlet Transitions in Achieving Highly Efficient Deep-Blue AIE-Based OLEDs.

Aggregation-induced emission (AIE) materials are attractive for achieving highly efficient nondoped organic light-emitting diodes (OLEDs) owing to their strong luminescence in the solid state. However, the electroluminescence efficiency of most AIE-based OLEDs remains low owing to the waste of triplet excitons. Here, using theoretical calculations, photophysical dynamics, and magnetoluminescence measurements, the spin conversion process is demonstrated between the high-lying triplet state (Tn ) and the lowest excited singlet state (S1 ) in AIE materials. Moreover, the relative positions of Tn (n < 4) and S1 are shown to have a significant impact on the spin-conversion efficiency, thus influencing the harvesting of triplet excitons and the device efficiency. Finally, by selecting an upconversion material with an appropriate energy level for further utilizing the triplet excitons, a deep-blue fluorescent OLED with CIE coordinates of (0.15, 0.08), a maximum external quantum efficiency of 10.2%, low efficiency roll-off, and a high brightness of 16817 cd m-2 is developed. This is one of the most efficient deep-blue OLEDs based on AIE materials reported so far. These findings also provide new insights into the design of more efficient AIE molecules and corresponding OLEDs by managing high-lying triplet excitons.

Highly Efficient Blue Fluorescent OLEDs Based on Upper Level Triplet-Singlet Intersystem Crossing.

Purely organic electroluminescent materials, such as thermally activated delayed fluorescent (TADF) and triplet-triplet annihilation (TTA) materials, basically harness triplet excitons from the lowest triplet excited state (T1 ) to realize high efficiency. Here, a fluorescent material that can convert triplet excitons into singlet excitons from the high-lying excited state (T2 ), referred to here as a "hot exciton" path, is reported. The energy levels of this compound are determined from the sensitization and nanosecond transient absorption spectroscopy measurements, i.e., small splitting energy between S1 and T2 and rather large T2 -T1 energy gap, which are expected to impede the internal conversion (IC) from T2 to T1 and facilitate the reverse intersystem crossing from the high-lying triplet state (hRISC). Through sensitizing the T2 state with ketones, the existence of the hRISC process with an ns-scale delayed lifetime is confirmed. Benefiting from this fast triplet-singlet conversion, the nondoped device based on this "hot exciton" material reaches a maximum external quantum efficiency exceeding 10%, with a small efficiency roll-off and CIE coordinates of (0.15, 0.13). These results reveal that the "hot exciton" path is a promising way to exploit high efficient, stable fluorescent emitters, especially for the pure-blue and deep-blue fluorescent organic light-emitting devices.

In situ synthesis and macroscale alignment of CsPbBr3 perovskite nanorods in a polymer matrix.

We report an in situ catalyst-free strategy to synthesize inorganic CsPbBr3 perovskite nanorods in a polymer matrix (NRs-PM) with good dimensional control, outstanding optical properties and ultrahigh environmental stability. Polarization photoluminescence (PL) imaging with high spatial resolution was carried out for the first time on single nanorod (NR) and shows a relatively high local polarization ratio (∼0.4) consistent with theoretical predictions based on a dielectric contrast model. We further demonstrate that macroscale alignment of the CsPbBr3 nanorods can be achieved through mechanically stretching the NRs-PM films at elevated temperature, without deteriorating the optical quality of the NRs. A polarization ratio of 0.23 is observed for these aligned NRs-PM films, suggesting their potential as polarized down-converters to increase the light efficiency in liquid crystal display (LCD) backlights.