摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-关于人工智能ce的详细数据已经呈现。根据NEWSRX编辑在尼泊尔Lalitpur的新闻报道，研究表明，“电化学双层电容器(EDLCs)的性能是通过活性炭(AC)电极的电容来评估的。”我们的新闻记者从特里布万大学的研究中得到一句话：“交流电极的电容受前驱体或类型、活化方法、孔结构、表面化学和电解性能等诸多因素的影响，本文将基于机器学习的表面积预测方法进行了比较研究。”在有限的实验数据基础上，对活性炭(AC)的活化温度、亚甲基蓝值和碘值等合成数据进行训练，得到性能最好的ML模型为Rando M Forest模型和XG Boost模型。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting out of Lalitpur, Nepal, by N ewsRx editors, research stated, “The performance of electrochemical double-layer capacitors (EDLCs) is evaluated by the capacitance of activated carbon (AC) ele ctrodes.” Our news reporters obtained a quote from the research from Tribhuvan University: “The capacitance of AC electrodes is influenced by many factors such as precurs or type, activation method, pore structure, surface chemistry and electrolytic p roperties. In this paper, we present a comparative study of machine learning bas ed prediction of surface area, mesopore volume and total pore volume of activate d carbon for energy storage applications. The ML models were trained on a datase t of synthetic data that were generated from the limited number of experimental data and which included the activation temperature, methylene blue number and io dine number of the activated carbon (AC). The best performing ML model was rando m forest model and XG boost model.”
NETL
NSTL