arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2209.13614cond-mat.mtrl-scicond-mat.soft
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效率与努力:一个更好的方法来比较最好的光伏研究电池的效率?

Efficiency versus effort: a better way to compare best photovoltaic research cell efficiencies?

Phillip J. Dale, Michael A. Scarpulla

摘要:

通常,记录AM1.5功率转换效率随时间变化的趋势,例如NREL效率图,用于分析不同光伏材料技术的相对优点。然而,这种方法掩盖了为实现这些业绩水平所付出的努力。我们引入累积出版物作为总研发工作的代表,并惊讶地发现硅,Cu(In,Ga)Se 2(CIGSe),CdTe和卤化物钙钛矿技术在10,000篇出版物中均遵循基本相同的20-24%效率的学习曲线,并且在出版物中每增加10倍,效率增加5%的边际率一致。虽然非光伏技术的学习溢出,其他光伏技术的交叉授粉和隐藏的商业努力没有被这个指标考虑在内,但这种分析仍然对光伏技术轨迹产生了有用和新颖的见解。低于这一学习曲线的轨迹需要更多的总努力,每一个性能和高原的效率停滞在大量的出版物可能表明(但不能保证)存在根本性的障碍,以商业相关的性能。最后,确定了目前表现出较高边际斜率的技术的例子,包括过去几年在这一指标下似乎处于休眠状态的一些技术。

英文摘要:

Frequently, trends in record AM1.5 power-conversion efficiencies versus time, such as the NREL efficiency chart, are used to analyze the relative merits of different photovoltaic material technologies. However, this approach belies the effort expended in achieving these levels of performance. We introduce cumulative publications as a proxy for total R&D efforts and find surprisingly that silicon, Cu(In,Ga)Se2 (CIGSe), CdTe, and halide perovskite technologies have each followed essentially the same learning curve of 20-24% efficiency within 10,000 publications and a consistent marginal rate of 5% efficiency increase per factor of 10 in publications. While learning spillover from non-PV technologies, cross-pollination from other PV technologies, and hidden commercial effort are not accounted for by this metric, this analysis still yields useful and novel insights into PV technology trajectories. Trajectories below this learning curve have required more total effort per performance and plateaus of efficiency stagnation at large numbers of publications may indicate (but do not guarantee) the existence of fundamental barriers to commercially relevant performance. Lastly, examples to watch are identified for technologies currently exhibiting higher marginal slopes, including some that appeared dormant by this metric in past years.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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