Assessing the role of surface electric fields on the interfacial degradation in silicon solar cells

Enhancing the reliability and longevity of solar modules is critical for expanding solar power to a multi-terawatt scale target worldwide. A major problem in maintaining high efficiency is the recombination of the photoexcited charge carriers at different interfaces. Within industrial silicon solar cells, there are three main device architectures in actual use: passivated emitter and rear cell (PERC), tunnel oxide passivated contact solar cells (TOPCon), and silicon heterojunction solar cells (SHJ). All these architectures suffer from different instabilities of efficiencies under prolonged illumination. This work investigates the mechanisms involved in interface-related degradation in these three architectures, specifically associated with hydrogen in silicon, especially considering the effects of surface electric fields. Through the current-voltage measurement for the PERC and TOPCon cells during bias annealing, I have shown the first bias-controlled hydrogen-induced contact resistance change in TOPCon cells. I demonstrate that the degradation occurs purely at the n-type Si / Ag interface on both cell architectures. Through the application of the surface electric fields on TOPCon and SHJ lifetime specimens during light soaking, I show that light-induced instabilities can be varied by the polarity and strength of the surface polarisation on the dielectric layer. Here, I demonstrate that the charged hydrogen ions, which respond readily to electric fields, are responsible for these observed instabilities. In addition, three mechanistic models are proposed to explain the hydrogen dynamics in these advanced solar cell architectures with surface polarisation. This work contributes to the body of evidence aimed at understanding hydrogen kinetics at different interfaces and providing valuable insights into the mechanisms behind these light-induced instabilities of these architectures. Besides that, applying surface electric fields via the corona charge or direct bias shows a potential method for detecting and controlling the kinetics of hydrogen in actual PV systems

Temperature Prediction for Stored Grain: A Multi-model Fusion Approach Based on Machine Learning

Temperature fluctuations significantly affect microorganism growth and pest activities in grain pile, precise monitoring and forecasting temperature of stored grain are essential for maintaining the quality and safety of grain storage. This paper proposes a multi-model fusion approach to predict grain temperature using historical temperature data of stored grain and meteorological data from the region. Firstly, four distinct machine learning models, namely Adaboost, decision tree, extra trees, and random forest, are fine-tuned through parameter optimization to enhance their predictive capabilities respectively; Subsequently, these optimized models are fused to form different ensemble models, which are compared for predidction accuracy to obtain the optimal fusion model. In essence, the fusion process integrates the predictions of each individual model as new feature inputs into the fusion models. Furthermore, random forest is utilized to identify the key factors influencing grain temperature, providing insights into the importance of different influencing factors. The experimental results demonstrate that the proposed fusion models can achieve higher prediction accuracy and robustness compared with the single-model prediction methods. Additionally, the analysis of feature importance also offers empirical evidence for understanding the factors influencing grain temperature

Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking

The digital reconstruction of single neurons from 3D confocal microscopic images is an important tool for understanding the neuron morphology and function. However the accurate automatic neuron reconstruction remains a challenging task due to the varying image quality and the complexity in the neuronal arborisation. Targeting the common challenges of neuron tracing, we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative backtracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises. By evaluating the proposed pipeline with the data provided by the Diadem challenge and the recent BigNeuron project, Rivulet is shown to be robust to challenging microscopic imagestacks. We discussed the algorithm design in technical details regarding the relationships between the proposed algorithm and the other state-of-the-art neuron tracing algorithms

Elementary models of 3D topological insulators with chiral symmetry

We construct a set of lattice models of non-interacting topological insulators with chiral symmetry in three dimensions. We build a model of the topological insulators in the class AIII by coupling lower dimensional models of $\mathbb{Z}$ classes. By coupling the two AIII models related by time-reversal symmetry we construct other chiral symmetric topological insulators that may also possess additional symmetries (the time-reversal and/or particle-hole). There are two different chiral symmetry operators for the coupled model, that correspond to two distinct ways of defining the sublattices. The integer topological invariant (the winding number) in case of weak coupling can be either the sum or difference of indices of the basic building blocks, dependent on the preserved chiral symmetry operator. The value of the topological index in case of weak coupling is determined by the chiral symmetry only and does not depend on the presence of other symmetries. For $\mathbb{Z}$ topological classes AIII, DIII, and CI with chiral symmetry are topologically equivalent, it implies that a smooth transition between the classes can be achieved if it connects the topological sectors with the same winding number. We demonstrate this explicitly by proving that the gapless surface states remain robust in $\mathbb{Z}$ classes as long as the chiral symmetry is preserved, and the coupling does not close the gap in the bulk. By studying the surface states in $\mathbb{Z}_2$ topological classes, we show that class CII and AII are distinct, and can not be adiabatically connected