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分形太阳能电池板:优化美学和电气性能。

Fractal solar panels: Optimizing aesthetic and electrical performances.

机构信息

Department of Physics, University of Oregon, Eugene, Oregon, United States of America.

Department of Psychology, Gonzaga University, Spokane, Washington, United States of America.

出版信息

PLoS One. 2020 Mar 10;15(3):e0229945. doi: 10.1371/journal.pone.0229945. eCollection 2020.

DOI:10.1371/journal.pone.0229945
PMID:32155189
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7064250/
Abstract

Solar energy technologies have been plagued by their limited visual appeal. Because the electrical power generated by solar panels increases with their surface area and therefore their occupancy of the observer's visual field, aesthetics will play an increasingly critical role in their future success in urban environments. Inspired by previous psychology research highlighting the aesthetic qualities of fractal patterns, we investigated panel designs featuring fractal electrodes. We conducted behavioral studies which compared observers' preferences for fractal and conventional bus-bar electrode patterns, along with computer simulations which compared their electrical performances. This led us to develop a hybrid electrode pattern which best combines the fractal and bus-bar designs. Here we show that the new hybrid electrode matches the electrical performance of bus-bars in terms of light transmission and minimizing electrical power losses, while benefiting from the superior aesthetics of fractal patterns. This innovative integration of psychology and engineering studies provides a framework for developing novel electrode patterns with increased implementation and acceptance.

摘要

太阳能技术一直受到其视觉吸引力有限的困扰。由于太阳能电池板产生的电力与其表面积成正比,因此它们在观察者的视野中占据的空间越大,美学在其未来在城市环境中的成功中就将扮演越来越关键的角色。受先前心理学研究强调分形图案美学品质的启发,我们研究了具有分形电极的电池板设计。我们进行了行为研究,比较了观察者对分形和传统母线电极图案的偏好,以及比较它们电气性能的计算机模拟。这使我们开发出了一种混合电极图案,它最好地结合了分形和母线设计。在这里,我们表明新的混合电极在透光率和最小化电力损失方面与母线的电气性能相匹配,同时受益于分形图案的卓越美学效果。这项心理学和工程学研究的创新结合为开发具有更高实施和接受度的新型电极图案提供了框架。

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Seeing shapes in seemingly random spatial patterns: Fractal analysis of Rorschach inkblots.在看似随机的空间图案中识别形状:罗夏墨迹测验图的分形分析
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Investigation of Fractal Carbon Nanotube Networks for Biophilic Neural Sensing Applications.用于亲生物神经传感应用的分形碳纳米管网络研究。
Nanomaterials (Basel). 2021 Mar 4;11(3):636. doi: 10.3390/nano11030636.
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