Shaozhou Wang

The rise of urbanization coupled with pollution has highlighted the importance of outdoor self-cleaning coatings. These revolutionary coatings contribute to the longevity of various surfaces and reduce maintenance costs for a wide range of applications. Despite ongoing research to develop efficient and durable self-cleaning coatings, adopting systematic research methodologies could accelerate these advancements. In this work, we use Natural Language Processing (NLP) strategies to generate open- and traceable-sourced datasets about self-cleaning coating materials from 39,011 multi-disciplinary papers. The data are from function-based and property-based corpora for self-cleaning purposes. These datasets are presented in four different formats for diverse uses or combined uses: material frequency statistics, material dictionary, measurement value datasets for self-cleaning-related properties and optical properties, and sentiment statistics of material stability and durability. This provides a literature-based data resource for the development of self-cleaning coatings and also offers potential pathways for material discovery and prediction by machine learning.

Multidimensional simulations for diverse solar cells often encounter distinctive configurations, even when employing the same simulation software. The complexity and inefficiency of this process are further exacerbated when employing different simulators. From our extensive decade‐long experience in numerical simulations of diverse solar cells, we have identified ten common simulation steps intrinsic to typical electrical and optical simulations. Subsequently, we propose ten sets of variables that encompass all the relevant details required for these steps. To address the challenge of varying information requirements for each variable across different simulations, we assign a list, a versatile data type, to each variable. This approach, by design, enables concise, coherent, and flexible input, accommodating the unique demands of each simulation. However, to ensure unambiguous simulations, precise specifications for these variables are essential. Computer code has been successfully implemented to ensure adherence to specifications and expedite variable synchronization with Sentaurus, the de facto standard for device simulation. Within this framework, users are only tasked with editing variables in a plain text file, obviating the need for in‐depth knowledge of Sentaurus. This streamlines the prerequisites for engaging in numerical simulation significantly. Through thoughtful design considerations, we preserve the simulation capacity while simultaneously enhancing productivity considerably. This open‐source framework welcomes global collaboration within the photovoltaic community and has the potential to generate an extensive dataset for cost‐effective artificial intelligence training.

The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials.

Phosphorous dopant diffusion profiles feature in many silicon semiconductor devices, including the vast majority of silicon solar cells. Accurate spatially resolved dopant profiling is crucial for understanding the performance of these diffused regions, however, it is very challenging to obtain such profiles in non‐planar samples. Scanning electron microscopy for dopant contrast imaging (SEMDCI), where the secondary electron (SE) image contrast is used to determine the dopant level of a semiconductor sample, is an ideal candidate for Si dopant profiling, especially for silicon samples with surface nanotexturing or black silicon (BSi) technology. However, in previous SEMDCI studies, the dopant concentration of heavily doped n‐type layers in silicon samples have shown a poor correlation with the SE signal contrast. In this work, 1) good contrast for n‐type diffused silicon without contrast‐enhancing techniques; 2) a new contrast definition to account for imaging non‐uniformities; 3) clear correlations between SE contrast and sample work function for phosphorus‐diffused planar silicon specimens across a wide range of emitter profiles; 4) implementation of an empirical baseline correction to normalize scanning electron microscopy image condition variations, are presented. This SEMDCI method is subsequently used for the first time to obtain 2D electron concentration maps for both planar and BSi samples.

In this work, single‐side aluminum oxide (Al₂O₃) deposition enabled by a new tube‐type industrial plasma‐assisted atomic layer deposition (PEALD) technique is presented to meet the increasingly stringent requirements for high‐efficiency solar cell mass production. Extremely low emitter saturation current densities, J_0e, down to 15 fA/cm² are achieved on an industrial textured boron emitter with a sheet resistance of 104 Ω/sq, passivated by PEALD Al₂O₃/PECVD SiN_x stack after firing. An implied open‐circuit voltage of up to 721 mV is obtained on symmetrical lifetime samples. The underlying passivation mechanisms of this new tube‐type PEALD Al₂O₃ are investigated by contactless corona‐voltage measurements. The results indicate that the superior passivation is mainly attributed to a low interface defect density down to 1.1 × 10¹¹ cm⁻² eV⁻¹ and a high negative fixed charge density up to 4.5 × 10¹² cm⁻². Simulations show that the obtained J_0e is close to its intrinsic limit. Lastly, the developed tube‐type PEALD Al₂O₃ is applied to industrial TOPCon solar cells achieving an average cell efficiency above 24% and a maximum V_oc of 707 mV. This work shows that the record level of surface passivation available from lab‐scale PEALD reactors is now available in a flexible high‐throughput industrial PEALD platform, which opens a new route for mass production of high‐efficiency industrial TOPCon solar cells with a lean process at low costs.

Black silicon (BSi) is a branch of silicon material whose surface is specially processed to a micro/nanoscale structure, which can achieve ultra‐low reflectance or ultra‐high electrochemical reactivity. The diversity and complex surface structures of BSi make it challenging to commercialize BSi devices. Modeling and simulation are commonly used in the semiconductor industry to help in better understanding the material properties, predict the device performance, and provide guidelines for fabrication parameters’ optimization. The biggest challenge for BSi device modeling and simulation is obtaining accurate input surface morphological data. In this work, the 3D models of challenging BSi textures are compared as obtained by atomic force microscopy (AFM) and plasma focused ion beam (PFIB) tomography techniques. In previous work, the PFIB tomography workflow toward the application of surface topography is optimized. In this work, the 3D models obtained from both AFM and PFIB are comprehensively compared, by using the surface models as inputs for finite‐difference time‐domain‐based optical simulation. The results provide strong evidence that PFIB tomography is a better choice for characterizing highly roughened surface such as BSi and provides surface 3D models with better reliability and consistency.

Nanostructured black silicon (b-Si) surfaces with an extremely low reflectance are a promising light-trapping solution for silicon solar cells. However, it is challenging to develop a high-efficiency front-junction b-Si solar cell due to the inferior electrical performance of b-Si emitters, which outweighs any optical gain. This article uses three-dimensional numerical nanoscale simulations, which are corroborated with experiment results, to investigate the effect of the surface nanofeature sizes on the b-Si emitter performance in terms of the sheet resistance (R _sheet ) and the saturation current density (J _0e ). We show that the specific surface area (SSA) is an effective parameter to evaluate the nanofeature size. A shallow surface nanofeature with a large SSA will contribute to a better electrical performance. We will show that b-Si emitter R _sheet measured by a four-point probe is not a measure of the doping level in the nanofeature, but is ruled by the doping level in the underlying substrate region. We also show that a small nanofeature with SSA > 100 μm ^-1 and height < 100 nm can lead to a relatively low J _0e (33 fA/cm ² lower than the best b-Si results reported in the literature) by suppressing surface minority carrier density and minimizing the total Auger recombination loss.

Black silicon (b-Si) surfaces typically have a high density of extreme nanofeatures and a significantly large surface area. This makes high-quality surface passivation even more critical for devices such as solar cells with b-Si surfaces. It has been hypothesized that conformal dielectrics with a high fixed charge density ( Q _f ) are preferred as the nanoscale features of b-Si result in a significant enhancement of field-effect passivation. This article uses 1-D, 2-D, and 3-D numerical simulations to study surface passivation of b-Si, where we particularly focus on the charge carrier control by | Q _f | up to 1 × 10 ¹³ cm ^-2 under accumulation conditions. We will show that there is a significant space charge region compression in b-Si nanofeatures, which affects the charge carrier population control for moderate | Q _f | up to ≈1 × 10 ¹² cm ^-2 . The average surface minority charge carrier density can be reduced by 70% in some cases, resulting in an equivalent reduction in area-normalized surface recombination losses if the effective surface recombination velocity ( S _eff ) is limited by minority carriers. This provides a possible solution for the empirical S _eff ∝ 1/ Q _f ⁴ reported previously. We will also show that the situation is more complicated for surface passivation films where the ratio between the electron and hole capture cross section (σ _n / σ _p ) is higher than 10 for p-type surfaces. For commonly used surface passivation films with a | Q _f | larger than ≈1 × 10 ¹² cm ^-2 , there is little space charge compression for b-Si. Consequently, S _eff simply scales with the surface area, i.e., there is no enhanced reduction of surface recombination by field-effect passivation on b-Si.

The integration of nanotextured black silicon (B-Si) into solar cells is often complicated by its enhanced phosphorus doping effect, which is typically attributed to increased surface area. In this article, we show that B-Si's surface-to-volume ratio, or specific surface area (SSA), which is directly related to surface reactivity, is a better indicator of reduced sheet resistance. We investigate six B-Si conditions with varying dimensions based on two morphology types prepared using metal-catalyzed chemical etching and reactive-ion etching. We demonstrate that for a POCl ₃ diffusion, B-Si sheet resistance decreases with increasing SSA, regardless of surface area. 2-D dopant contrast imaging of different textures with similar surface areas also indicates that the extent of doping is enhanced with increasing SSA. 3-D diffusion simulations of nanocones show that both the extent of radial doping within a texture feature and the metallurgical junction depth in the underlying substrate increase with increasing SSA. We suggest SSA should be considered more readily when studying B-Si and its integration into solar cells.