遇见数据集

石脑油芳构化催化剂制备制备、测试与评价数据集

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本数据集采集涵盖了2021年1月至2024年11月期间有关催化剂制备、测试及评价的相关数据。在实验阶段,运用水热合成法规模化制备改性铂基 beta 分子筛催化剂,将其应用于费托石脑油芳构化反应。同时,借助 XRD、BET、TEM、SEM、NH3-TPD 等一系列表征技术,深入探究反应条件、催化剂酸性、孔道结构、金属物种分散度以及反应原料碳链长度等因素,对催化剂的催化活性、使用寿命和芳烃选择性产生的影响机制及规律。本数据集采用了创新的芳构化催化剂制备方法,结合行业通用的结构表征手段和催化剂性能测试方法,确保了数据采集的高效性、稳定性与可重复性。在潜在利用价值与意义上,该数据集为制备高性能费托基石脑油芳构化催化剂,提供了关键的方法依据和数据支撑。其中的催化剂制备数据、结构表征数据以及性能测试数据,为相关领域的研究开辟了全新思路,提供了可靠的数据支持,具备重要的学术价值和应用价值。

This dataset collects and covers relevant data on catalyst preparation, testing and evaluation spanning from January 2021 to November 2024. During the experimental stage, modified Pt-based beta zeolite catalysts were prepared at scale via the hydrothermal synthesis method and applied to the Fischer-Tropsch naphtha aromatization reaction. Meanwhile, a series of characterization techniques including XRD, BET, TEM, SEM and NH3-TPD were utilized to conduct in-depth investigations into the influencing mechanisms and regularities of various factors, such as reaction conditions, catalyst acidity, pore structure, metal species dispersion and carbon chain length of reaction feedstocks, on the catalytic activity, service life and aromatic selectivity of the catalysts. This dataset adopts an innovative aromatization catalyst preparation method, combined with industry-standard structural characterization and catalyst performance testing approaches, ensuring the efficiency, stability and repeatability of data acquisition. In terms of its potential utilization value and significance, this dataset provides critical methodological foundations and data support for the preparation of high-performance Fischer-Tropsch naphtha aromatization catalysts. The catalyst preparation data, structural characterization data and performance test data contained herein have opened up novel research avenues for relevant fields, offered reliable data support, and hold significant academic and application values.

搜集汇总
数据集介绍
石脑油芳构化催化剂制备制备、测试与评价数据集 数据集图片
背景与挑战
背景概述
该数据集涵盖了2021年至2024年期间,通过水热合成法制备改性铂基beta分子筛催化剂,并应用于费托石脑油芳构化反应的相关数据。它结合了多种表征技术,探究了反应条件、催化剂结构等因素对性能的影响,为高性能芳构化催化剂的研发提供了关键数据支持。
以上内容由遇见数据集搜集并总结生成
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