Unveiling Defect-Mediated Carrier Dynamics in Monolayer Semiconductors by Spatiotemporal Microwave Imaging

The optoelectronic properties of atomically thin transition-metal dichalcogenides are strongly correlated with the presence of defects in the materials, which are not necessarily detrimental for certain applications. For instance, defects can lead to an enhanced photoconduction, a complicated process involving charge generation and recombination in the time domain and carrier transport in the spatial domain. Here, we report the simultaneous spatial and temporal photoconductivity imaging in two types of WS2 monolayers by laser-illuminated microwave impedance microscopy. The diffusion length and carrier lifetime were directly extracted from the spatial profile and temporal relaxation of microwave signals respectively. Time-resolved experiments indicate that the critical process for photo-excited carriers is the escape of holes from trap states, which prolongs the apparent lifetime of mobile electrons in the conduction band. As a result, counterintuitively, the photoconductivity is stronger in CVD samples than exfoliated monolayers with a lower defect density. Our work reveals the intrinsic time and length scales of electrical response to photo-excitation in van der Waals materials, which is essential for their applications in novel optoelectronic devices.Comment: 21 pages, 4 figure

Superior photo-carrier diffusion dynamics in organic-inorganic hybrid perovskites revealed by spatiotemporal conductivity imaging

The outstanding performance of organic-inorganic metal trihalide solar cells benefits from the exceptional photo-physical properties of both electrons and holes in the material. Here, we directly probe the free-carrier dynamics in Cs-doped FAPbI3 thin films by spatiotemporal photoconductivity imaging. Using charge transport layers to selectively quench one type of carriers, we show that the two relaxation times on the order of 1 μs and 10 μs correspond to the lifetimes of electrons and holes in FACsPbI3, respectively. Strikingly, the diffusion map- ping indicates that the difference in electron/hole lifetimes is largely compensated by their disparate mobility. Consequently, the long diffusion lengths (3~5 μm) of both carriers are comparable to each other, a feature closely related to the unique charge trapping and de- trapping processes in hybrid trihalide perovskites. Our results unveil the origin of superior diffusion dynamics in this material, crucially important for solar-cell applications.The research at UT-Austin was primarily supported by the NSF through the Center for Dynamics and Control of Materials, an NSF Materials Research Science and Engineering Center (MRSEC) under Cooperative Agreement DMR-1720595. The authors also acknowledge the use of facilities and instrumentation supported by the NSF MRSEC. K.L. and X.M. acknowledge the support from Welch Foundation Grant F-1814. X. Li acknowledges the support from Welch Foundation Grant F-1662. The tip-scan iMIM setup was supported by the US Army Research Laboratory and the US Army Research Office under Grants W911NF-16-1-0276 and W911NF-17-1-0190. The work at NREL was supported by the US DOE under Contract No. DE-AC36-08GO28308 with Alliance for Sustainable Energy, Limited Liability Company (LLC), the Manager and Operator of the National Renewable Energy Laboratory. K.Z., J.H., X.C., X.W., and Y.Y. acknowledge the support on charge carrier dynamics study from the Center for Hybrid Organic-Inorganic Semiconductors for Energy (CHOISE), an Energy Frontier Research Center funded by the Office of Basic Energy Sciences, Office of Science within the US DOE. F.Z. acknowledges the support on devices fabrication and characterizations from the De-Risking Halide PSCs program of the National Center for Photovoltaics, funded by the US DOE, Office of Energy Efficiency and Renewable Energy, Solar Energy Technologies Office.Center for Dynamics and Control of Material

Co-Correcting: Combat Noisy Labels in Space Debris Detection

Space debris detection is vital to space missions and space situation awareness. Convolutional neural networks are introduced to detect space debris due to their excellent performance. However, noisy labels, caused by false alarms, exist in space debris detection, and cause ambiguous targets for the training of networks, leading to networks overfitting the noisy labels and losing the ability to detect space debris. To remedy this challenge, we introduce label-noise learning to space debris detection and propose a novel label-noise learning paradigm, termed Co-correcting, to overcome the effects of noisy labels. Co-correcting comprises two identical networks, and the predictions of these networks serve as auxiliary supervised information to mutually correct the noisy labels of their peer networks. In this manner, the effect of noisy labels can be mitigated by the mutual rectification of the two networks. Empirical experiments show that Co-correcting outperforms other state-of-the-art methods of label-noise learning, such as Co-teaching and JoCoR, in space debris detection. Even with a high label noise rate, the network trained via Co-correcting can detect space debris with high detection probability

Co-Correcting: Combat Noisy Labels in Space Debris Detection

Space debris detection is vital to space missions and space situation awareness. Convolutional neural networks are introduced to detect space debris due to their excellent performance. However, noisy labels, caused by false alarms, exist in space debris detection, and cause ambiguous targets for the training of networks, leading to networks overfitting the noisy labels and losing the ability to detect space debris. To remedy this challenge, we introduce label-noise learning to space debris detection and propose a novel label-noise learning paradigm, termed Co-correcting, to overcome the effects of noisy labels. Co-correcting comprises two identical networks, and the predictions of these networks serve as auxiliary supervised information to mutually correct the noisy labels of their peer networks. In this manner, the effect of noisy labels can be mitigated by the mutual rectification of the two networks. Empirical experiments show that Co-correcting outperforms other state-of-the-art methods of label-noise learning, such as Co-teaching and JoCoR, in space debris detection. Even with a high label noise rate, the network trained via Co-correcting can detect space debris with high detection probability

Structural, Magnetic, and Thermodynamic Evolutions of Zn-Doped Fe3O4 Nanoparticles Synthesized Using a One-Step Solvothermal Method

Zn-doped Fe3O4 magnetic nanoparticles represented as ZnxFe3-xO4 with different Zn contents of x varying from 0.0 to 1.0 were synthesized using a facile one-step solvothermal method. The Zn/Fe ratio in these particles could be accurately controlled using this facile synthesis technique. The ICP-OES and XRD measurements indicated that in the x range from 0 to 0.4 the doped Zn2+ may replace the Fe3+ at the A site and consequently the B-site Fe2+ changed to Fe3+, while above 0.4 the Zn2+ tends to replace the B-site Fe2+. The morphologies and size distributions of these samples characterized from the TEM showed that the nanoparticles appeared to aggregate into magnetic nanocrystal clusters with varying cluster sizes and different Zn doping contents. The magnetic measurement and Mossbauer spectra investigation revealed that the magnetic properties of the ZnxFe3-xO4 would exhibit a sensitive dependence with the doped Zn variations. Most importantly, the heat capacity studies illuminated that, at low temperatures, the samples could have a ferromagnetic contribution with x = 0.0 and 0.2 and turn to an antiferromagnetic contribution with x = 0.5, 0.8, and 1.0

BSC-Net: Background Suppression Algorithm for Stray Lights in Star Images

Most background suppression algorithms are weakly robust due to the complexity and fluctuation of the star image’s background. In this paper, a background suppression algorithm for stray lights in star images is proposed, which is named BSC-Net (Background Suppression Convolutional Network) and consist of two parts: “Background Suppression Part” and “Foreground Retention Part”. The former part achieves background suppression by extracting features from various receptive fields, while the latter part achieves foreground retention by merging multi-scale features. Through this two-part design, BSC-Net can compensate for blurring and distortion of the foreground caused by background suppression, which is not achievable in other methods. At the same time, a blended loss function of smooth_L1&Structure Similarity Index Measure (SSIM) is introduced to hasten the network convergence and avoid image distortion. Based on the BSC-Net and the loss function, a dataset consisting of real images will be used for training and testing. Finally, experiments show that BSC-Net achieves the best results and the largest Signal-to-Noise Ratio (SNR) improvement in different backgrounds, which is fast, practical and efficient, and can tackle the shortcomings of existing methods

Farmland Soil Block Identification and Distribution Statistics Based on Deep Learning

Soil block distribution is one of the important indexes to evaluate the tillage performance of agricultural machinery. The traditional manual screening methods have the problems of low efficiency and damaging the original surface of the soil. This study proposes a statistical method of farmland soil block distribution based on deep learning. This method combines the adaptive learning rate and squeeze-and-excitation networks channel attention mechanism based on the original Mask-RCNN and uses the improved model to identify, segment and distribute statistics of the farmland soil blocks. Firstly, the influence of different learning rates and an improved Mask-RCNN algorithm model on training results were analyzed. Secondly, the effectiveness of the model in soil block identification and size measurement was analyzed. Finally, the identified soil blocks were classified accordingly, and the scale problem of soil block distribution after removing edge soil blocks was analyzed. The results show that with the decrease of learning rate, the loss value of model training decreases and the prediction accuracy of model is improved. The average precision value of the improved model increased by 25.29 %, and the recall value increased by 8.92%. The correlation coefficient of the maximum diameter measured by manual measurement and the maximum diameter measured by model algorithm was 0.99, which verifies the feasibility of the algorithm model. The prediction error of the model is the smallest when the camera height is 40 cm. Large-scale detection of soil block size in an experimental field in Hefei, Anhui, with an average confidence of over 97%. At the same time, the soil block is effectively classified according to the set classification standard. This study can provide an effective method for the accurate classification of soil block size and can provide a quantitative basis for the control of farmland cultivation intensity

Farmland Soil Block Identification and Distribution Statistics Based on Deep Learning

Soil block distribution is one of the important indexes to evaluate the tillage performance of agricultural machinery. The traditional manual screening methods have the problems of low efficiency and damaging the original surface of the soil. This study proposes a statistical method of farmland soil block distribution based on deep learning. This method combines the adaptive learning rate and squeeze-and-excitation networks channel attention mechanism based on the original Mask-RCNN and uses the improved model to identify, segment and distribute statistics of the farmland soil blocks. Firstly, the influence of different learning rates and an improved Mask-RCNN algorithm model on training results were analyzed. Secondly, the effectiveness of the model in soil block identification and size measurement was analyzed. Finally, the identified soil blocks were classified accordingly, and the scale problem of soil block distribution after removing edge soil blocks was analyzed. The results show that with the decrease of learning rate, the loss value of model training decreases and the prediction accuracy of model is improved. The average precision value of the improved model increased by 25.29 %, and the recall value increased by 8.92%. The correlation coefficient of the maximum diameter measured by manual measurement and the maximum diameter measured by model algorithm was 0.99, which verifies the feasibility of the algorithm model. The prediction error of the model is the smallest when the camera height is 40 cm. Large-scale detection of soil block size in an experimental field in Hefei, Anhui, with an average confidence of over 97%. At the same time, the soil block is effectively classified according to the set classification standard. This study can provide an effective method for the accurate classification of soil block size and can provide a quantitative basis for the control of farmland cultivation intensity

A Novel BCC-Structure Zr-Nb-Ti Medium-Entropy Alloys (MEAs) with Excellent Structure and Irradiation Resistance

Medium-entropy alloys (MEAs) are prospective structural materials for emerging advanced nuclear systems because of their outstanding mechanical properties and irradiation resistance. In this study, the microstructure and mechanical properties of three new single-phase body-centered cubic (BCC) structured MEAs (Zr40Nb35Ti25, Zr50Nb35Ti15, and Zr60Nb35Ti5) before and after irradiation were investigated. It is shown that the yield strength and elongation after fracture at room temperature are greater than 900 MPa and 10%, respectively. Three MEAs were irradiated with 3 MeV Fe11+ ions to 8 × 1015 and 2.5 × 1016 ions/cm2 at temperatures of 300 and 500 °C, to investigate the irradiation-induced hardening and microstructure changes. Compared with most conventional alloys, the three MEAs showed only negligible irradiation hardening and even softening in some cases. After irradiation, they exhibit somewhat surprising lattice constant reduction, and the microstructure contains small dislocation loops. Neither cavities nor precipitates were observed. This indicates that the MEAs have better irradiation resistance than traditional alloys, which can be attributed to the high-entropy and lattice distortion effect of MEAs

Magnetic and Thermodynamic Properties of Nanosized Zn Ferrite with Normal Spinal Structure Synthesized Using a Facile Method

Normal spinel zinc ferrite (ZnFe2O4) nanoparticles (NPs) with zero net magnetization were synthesized by a facile coprecipitation method in which two kinds of organic alkali, namely, 1-amino-2-propanol (MIPA) and bis(2-hydroxypropyl)-amine (DIPA), were used. The diameters of the ZnFe2O4 NPs were determined to be about 7 and 9 nm for samples prepared with MIPA and DIPA, respectively, and the normal spinel structure was confirmed by the magnetic property measurement at room temperature and the temperature dependence of the direct current magnetization. These results are different from those reported in the literature, where ZnFe2O4 NPs show a nonzero net magnetization. The heat capacity of the ZnFe2O4 NPs synthesized using DIPA was measured using a physical property measurement system in the temperature range from 2 to 300 K, and the thermodynamic functions were calculated based on the curve fitting of the experimental heat capacity data. The heat capacity of the ZnFe2O4 NPs was compared with that of a nanosized (Zn0.795Fe0.205)[Zn0.205Fe1.795]O-4 material studied in the literature, indicating that the Debye temperature of the present sample is more comparable with that of the bulk ZnFe2O4 reported by Westrum et al