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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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237 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A New Low-Temperature Approach for Efficient and Low-Cost Group V Doping in CdTe Thin Film Solar Cells (Final Technical Report)

The goal of this project is to develop a highly efficient low-temperature ex-situ Group V diffusion doping approach in the CdCl 2 treated polycrystalline CdTe film using the group V chlorides (i.e., PCl 3 , AsCl 3 , SbCl 3 , and BiCl 3 ) as dopants source. For the first time, our innovative doping strategies through the low-temperature ex-situ Group V doping process in polycrystalline CdSeTe successfully demonstrated a promising power conversion efficiency of ~19% compared to the Cu-doped counterparts. Our newly developed low-temperature ex-situ group V doping allows dedicated control of group V diffusion amount, generates a desirable depth profile, and is completely compatible with commercial CdTe module manufacturing. We built the partnership with the largest CdTe solar module manufacturing in the US, e.g., First Solar, Inc to further develop these low-temperature diffusion doped CdSeTe solar cells. Our technology was granted US Patent No. US11257977B2. In this project, we systematically investigate and characterize the new ex-situ group V doping mechanisms, defect chemistry, microstructure, and device physics in CdTe solar cells by combining experimental and theoretical means to pave the way to achieve lower cost toward $\$$0.02/kWh and higher power conversion efficiency (PCE) toward 25% in polycrystalline CdTe solar technology. This project will benefit the public by providing more affordable solar electricity and provide an effective way to address climate change.

14 SOLAR ENERGY↗
DOE OSTI2022

Synthesis and property of EPEG ‐based polycarboxylate ether superplasticizers via RAFT polymerization

Abstract Polycarboxylate ether superplasticizers (PCEs) with comb‐shaped molecular structures have attracted considerable attention in the area of admixtures for construction engineering. However, there are some contradictions in studying the relationship between the molecular structure and property of PCE due to its random structure based on traditional free radical polymerization, which is confused for designing and developing novel PCEs. In this article, a series of reversible addition fragmentation chain transfer (RAFT) polymerization‐based polycarboxylate ether superplasticizers (RPCs) were prepared by the copolymerization of acrylic acid (AA) and ethylene‐glycol monovinyl polyethylene glycol (EPEG). The chemical structure and composition of RPCs were characterized and their adsorption behavior and dispersion properties were conducted. The results demonstrate that the molecular weights of RPCs do not vary significantly with prolonging reaction time, and the actual monomer ratio in RPCs displays a significant change to the feed one due to the monomer reactivity and conversion at higher reaction temperature, which is different from conventional free radical polymerization. The dispersion and adsorption properties of RPCs to cement were evaluated by slurry fluidity tests and total organic carbon (TOC) experiments. The effect of polymerization temperature and time on the dispersion ability has been systematically studied. The results indicate that the dispersion ability and adsorption capacity of RPCs are closely related to their molecular structures. The dispersion ability of RPCs were enhanced with increasing carboxyl group density in a certain range of monomer molar ratios (≤6:1). This study will provide a new platform for designing and developing high‐performance and rational cost‐effective superplasticizers with well‐defined structure and controlled molecular weight.

Chen, Xiaodong↗
DOE OSTI2021

Accelerated basis adaptation in homogeneous chaos spaces

Polynomial chaos expansions (PCE) provide an efficient approach to uncertainty quantification (UQ) and have been adapted to diverse applications across the spectrum of science and engineering. For situations involving large stochastic parameterizations, the curse of dimensionality renders PCE-based methods computationally prohibitive. A basis adaptation approach for PCE was proposed by Tipireddy and Ghanem (2014) which transforms the input random variables through an isometry such that sufficient probabilistic characterization of specific quantities of interest (QoI) is concentrated in an algebraic manifold embedded in the linear span of the dominant transformed variables. While quite versatile, that original version of the PCE basis adaptation exhibited slow convergence for a number of problems of practical significance. In the present paper, we propose two novel methods to accelerate the convergence of the original basis adaptation approach, thus expanding its reach while also providing insight into its performance. In the first method, information gained from a pilot PCE representation is used to correct the mean and Gaussian coefficients in the adapted space. By taking advantages of probabilistic information in higher dimensional adaptation gleaned from an initial adaptation, the second method updates the rotation matrix used to identify the dominant transformed variables. In this manner, the new rotation matrix concentrates even more probabilistic information in its first few dimensions. These two method can be combined to achieve even better performance, the combined method is referred to as sequentially optimized adaptation method. Here, the methods are demonstrated on an analytical test function and a model of a space structure with several sub-components and a non-smooth quantity of interest representing the maximum acceleration over time. Both methods achieve accelerated convergence of the basis adaptation approach with negligible additional costs.

Accelerated convergence↗