在催化领域的一次突破性进展中，苏黎世联邦理工学院（ETH Zurich）的研究人员提出了一种创新策略，将主动学习融入到实验工作流程中，以此加速催化剂的开发过程，特别是在合成气热催化加氢合成高级醇（HAS）领域。这一策略显著降低了成本，并减少了环境足迹，标志着催化技术领域的一大进步。
合成气热催化加氢合成高级醇（HAS）技术作为一项有潜力的技术，需要多组分材料进行催化。然而，复杂的反应动力学和广泛的化学空间使得催化剂设计变得极具挑战性。为应对这一难题，ETH Zurich的研究团队提出了一种新颖的方法，将主动学习融入实验工作流程中。通过构建数据辅助框架，该团队成功地导航了86个实验中广泛成分和反应条件的空间，这一框架使环境足迹和成本相比传统方法减少了90%以上。
该框架的核心在于优化了催化剂的成分和反应条件，最终确定了Fe65Co19Cu5Zr11催化剂。这一催化剂在稳定运行150小时的情况下，实现了1.1的更高醇生产率，相比于通常报告的产量提高了5倍。这一成果不仅超越了现有HAS催化剂设计策略，也为更广泛的催化转化提供了可能性，同时促进了实验室的可持续性。
这项研究成果以《Active learning streamlines development of high performance catalysts for higher alcohol synthesis》为题，于7月11日发布在《Nature Communications》杂志上。这一研究不仅为催化领域提供了新的思路，也为可持续化学工艺的发展开辟了新路径，有望在能源、材料科学等领域产生深远影响。
主动学习在催化剂开发中的应用，不仅加速了科学探索的速度，也显著提高了资源利用效率，是催化科学领域的一大突破，对于推动绿色化学和可持续发展具有重要意义。
英语如下：
Headline: “AI-Driven High-Efficiency Catalyst Development, Reduces Cost by 90%, Accelerates Higher Alcohol Synthesis”
Keywords: Active Learning, Catalyst Development, Cost Reduction
News Content: In a groundbreaking advancement in catalysis, researchers at ETH Zurich have introduced an innovative approach that integrates active learning into experimental workflows to expedite the development of catalysts, particularly in the field of synthesis gas thermal catalytic hydrogenation for the synthesis of higher alcohols (HAS). This strategy significantly reduces costs and minimizes environmental impact, marking a significant leap forward in catalytic technology.
The HAS technology, a promising method, necessitates the use of multi-component materials for catalysis. However, the complexity of reaction kinetics and the vast chemical space involved in catalyst design presents a formidable challenge. To tackle this issue, the ETH Zurich research team proposed a novel method that incorporates active learning into experimental workflows. By constructing a data-assisted framework, the team navigated through the expansive space of 86 experiments with a wide range of components and reaction conditions, resulting in an environment footprint and cost reduction of over 90% compared to traditional methods.
The core of this framework lies in optimizing catalyst components and reaction conditions, culminating in the identification of the Fe65Co19Cu5Zr11 catalyst. This catalyst demonstrated a higher alcohol productivity of 1.1 in stable operation for 150 hours, a 5-fold increase compared to typical reported yields. This achievement not only surpasses existing HAS catalyst design strategies but also opens up possibilities for broader catalytic transformations, while promoting laboratory sustainability.
The findings, titled “Active learning streamlines development of high-performance catalysts for higher alcohol synthesis,” were published on July 11 in the journal Nature Communications. This research not only offers new perspectives for the catalysis field but also paves new paths for the development of sustainable chemical processes, with potential far-reaching impacts in energy, materials science, and beyond.
The application of active learning in catalyst development not only accelerates the pace of scientific discovery but also significantly improves resource efficiency, marking a major breakthrough in the field of catalysis and a significant contribution to the advancement of green chemistry and sustainable development.
【来源】https://www.jiqizhixin.com/articles/2024-07-18-8
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