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Assessment of Bulk and Interface Quality for Liquid Phase Crystallized Silicon on Glass
摘要： This paper reports on the electrical quality of liquid phase crystallized silicon (LPC-Si) on glass for thin-film solar cell applications. Spatially resolved methods such as light beam induced current (LBIC), microwave photoconductance decay (MWPCD) mapping, and electron backscatter diffraction were used to access the overall material quality, intra-grain quality, surface passivation, and grain boundary (GB) properties. LBIC line scans across GBs were fitted with a model to characterize the recombination behavior of GBs. According to MWPCD measurement, intra-grain bulk carrier lifetimes were estimated to be larger than 4.5 μs for n-type LPC-Si with a doping concentration in the order of 1016 cm?3. Low-angle GBs were found to be strongly recombination active and identified as highly defect-rich regions which spatially extend over a range of 40–60 μm and show a diffusion length of 0.4 μm. Based on absorber quality characterization, the influence of intra-grain quality, heterojunction interface, and GBs/dislocations on the cell performance were separately clarified based on two-dimensional (2-D)-device simulation and a diode model. High back surface recombination velocities of several 105 cm/s are needed to get the best match between simulated and measured open circuit voltage (Voc), indicating back surface passivation problem. The results showed that Voc losses are not only because of poor back surface passivation but also because of crystal defects such as GBs and dislocation.
关键词： Bulk lifetime,heterojunction,grain boundaries (GBs),two-dimensional (2-D)-device simulation,liquid phase crystallized silicon (LPC-Si),light beam induced current (LBIC)
更新于2025-11-14 15:25:21
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[IEEE 2018 International Conference on Radar (RADAR) - Brisbane, Australia (2018.8.27-2018.8.31)] 2018 International Conference on Radar (RADAR) - Radar Cross Section of Modified Target Using Gaussian Beam Methods: Experimental Validation
摘要： The aim of this paper is to study the Radar Cross Section (RCS) of modified radar targets (plate with notch) using Gaussian Beam techniques. The Gaussian methods used in this work are Gaussian Beam Summation (GBS) and Gaussian Beam Launching (GBL). We establish the theoretical formulation of the GBS and GBL techniques and analyze the influence of the main Gaussian beam parameters on the variation of the scattered field. Then, we present the simulations of RCS. The numerical results are compared with PO, MoM methods, and also with experimental measurements performed in the anechoic chamber at Lab-STICC (ENSTA Bretagne).
关键词： Radar Cross Section (RCS),Physical Theory of Diffraction (PTD),Physical Optic (PO),Gaussian Beam Summation (GBS),Gaussian Beam Launching(GBL),Method of Moment (MoM)
更新于2025-09-23 15:22:29
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Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques
摘要： Background: Group B streptococcus (GBS) is an important pathogen that is responsible for invasive infections, including sepsis and meningitis. GBS serotyping is an essential means for the investigation of possible infection outbreaks and can identify possible sources of infection. Although it is possible to determine GBS serotypes by either immuno-serotyping or geno-serotyping, both traditional methods are time-consuming and labor-intensive. In recent years, the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported as an effective tool for the determination of GBS serotypes in a more rapid and accurate manner. Thus, this work aims to investigate GBS serotypes by incorporating machine learning techniques with MALDI-TOF MS to carry out the identification. Results: In this study, a total of 787 GBS isolates, obtained from three research and teaching hospitals, were analyzed by MALDI-TOF MS, and the serotype of the GBS was determined by a geno-serotyping experiment. The peaks of mass-to-charge ratios were regarded as the attributes to characterize the various serotypes of GBS. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), were then used to construct predictive models for the five different serotypes (Types Ia, Ib, III, V, and VI). After optimization of feature selection and model generation based on training datasets, the accuracies of the selected models attained 54.9–87.1% for various serotypes based on independent testing data. Specifically, for the major serotypes, namely type III and type VI, the accuracies were 73.9 and 70.4%, respectively. Conclusion: The proposed models have been adopted to implement a web-based tool (GBSTyper), which is now freely accessible at http://csb.cse.yzu.edu.tw/GBSTyper/, for providing efficient and effective detection of GBS serotypes based on a MALDI-TOF MS spectrum. Overall, this work has demonstrated that the combination of MALDI-TOF MS and machine intelligence could provide a practical means of clinical pathogen testing.
关键词： GBS,Machine learning,Serotypes,Group B streptococcus,MALDI-TOF-MS
更新于2025-09-16 10:30:52