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相关概念视频

Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

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In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
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Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)00:53

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Acyclic diene metathesis polymerization or ADMET polymerization involves cross-metathesis of terminal dienes, such as 1,8-nonadiene, to give linear unsaturated polymer and ethylene. As ADMET is a reversible process, the formed ethylene gas must be removed from the reaction mixture to complete the polymerization process.
Similar to cross-metathesis, ADMET also involves the formation of metallacyclobutane intermediate by [2+2] cycloaddition of one of the double bonds of a terminal diene with...
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相关实验视频

Updated: May 16, 2025

Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
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一个多式变压器用于预测全球最低吸附能量.

Junwu Chen1,2, Xu Huang1,3, Cheng Hua4

  • 1Laboratory of Artificial Chemical Intelligence (LIAC), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

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概括

预测催化剂性能是更快的AddsMT,一个新的AI模型. 它准确地估计了催化剂选的全球最小吸附能量 (GMAE),降低了计算成本.

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Adsorption Device Based on a Langatate Crystal Microbalance for High Temperature High Pressure Gas Adsorption in Zeolite H-ZSM-5
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 催化剂是一种催化剂.

背景情况:

  • 准确预测全球最小吸附能量 (GMAE) 对催化剂选至关重要.
  • 密度函数理论 (DFT) 计算对于确定GMAE由于众多吸附点和配置而计算昂贵.

研究的目的:

  • 开发一种快速而准确的方法来预测GMAE.
  • 为了克服大型催化剂选的传统DFT方法的计算局限性.

主要方法:

  • 设计了AddsMT,一种多式联接式变压器模型.
  • 使用表面图和吸附物特征向量进行预测.
  • 采用交叉注意力机制来捕捉没有位点绑定信息的吸附物-表面相互作用.

主要成果:

  • 在三个基准数据集中,在0.09,0.14和0.39 eV的低平均绝对误差中获得了低平均误差.
  • 广告MT有效地预测GMAE,显著降低计算成本.
  • 交叉注意力得分的可解释潜力,用于识别有利的吸附位点.

结论:

  • AdsMT为GMAE预测提供了一种计算效率高,准确的方法.
  • 该模型有助于大规模的催化剂选.
  • 综合不确定性量化提高了预测可靠性.