Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical C−H activations, these arene functionalizations generally require directing groups to enable the C−H activation. The installation and removal of these directing groups call for additional synthesis steps, which jeopardizes the inherent efficacy of the electrochemical C−H activation approach, leading to undesired waste with reduced step and atom economy. In sharp contrast, herein we present palladium-electrochemical C−H olefinations of simple arenes devoid of exogenous directing groups. The robust electrocatalysis protocol proved amenable to a wide range of both electron-rich and electron-deficient arenes under exceedingly mild reaction conditions, avoiding chemical oxidants. This study points to an interesting approach of two electrochemical transformations for the success of outstanding levels of position-selectivities in direct olefinations of electron-rich anisoles. A physical organic parameter-based machine learning model was developed to predict position-selectivity in electrochemical C−H olefinations. Furthermore, late-stage functionalizations set the stage for the direct C−H olefinations of structurally complex pharmaceutically relevant compounds, thereby avoiding protection and directing group manipulations.

电氧化已成为分子合成中越来越可行的平台，可以避免化学计量的化学氧化还原剂。尽管电化学 C−H 活化取得了重大进展，但这些芳烃官能化通常需要导向基团来实现 C−H 活化。这些导向基团的安装和去除需要额外的合成步骤，这会危及电化学CH活化方法的固有功效，导致步骤和原子经济性降低，从而导致不需要的浪费。与此形成鲜明对比的是，本文提出了不含外源导向基团的简单芳烃的钯电化学 CH 烯化反应。事实证明，强大的电催化方案在极其温和的反应条件下适用于各种富电子和缺电子芳烃，避免化学氧化剂。这项研究提出了一种有趣的两种电化学转化方法，可以在富电子苯甲醚的直接烯化中成功实现出色的位置选择性水平。开发了基于物理有机参数的机器学习模型来预测电化学 C−H 烯化中的位置选择性。此外，后期官能化为结构复杂的药物相关化合物的直接CH烯化奠定了基础，从而避免了保护并指导基团操作。开发了基于物理有机参数的机器学习模型来预测电化学 C−H 烯化中的位置选择性。此外，后期官能化为结构复杂的药物相关化合物的直接CH烯化奠定了基础，从而避免了保护并指导基团操作。开发了基于物理有机参数的机器学习模型来预测电化学 C−H 烯化中的位置选择性。此外，后期官能化为结构复杂的药物相关化合物的直接CH烯化奠定了基础，从而避免了保护并指导基团操作。