<p>Calorimetry data for continuous-flow photochemistry are scarce, hindering kinetic model development and safe scale-up. Here we show that in situ calorimetry combined with designed experiments enables a quantitative, heat-consistent kinetic description for the continuous-flow photobromination of (<i>E</i>)-methyl 2-(methoxyimino)-2-(<i>o</i>-tolyl) acetate (EMMA). We record total heat release, conversion, and selectivity over defined windows of residence time and light intensity, and fit a two-step kinetic scheme constrained by the calorimetric heat-release data. The model predicts total heat with <i>R</i><sup>2</sup> = 0.957 and conversion with <i>R</i><sup>2</sup> = 0.894, while the second bromination step shows negligible dependence on light intensity within the explored window, indicating thermally driven behaviour. We translate these parameters into a real-time visual digital model that maps the spatial distributions of conversion, selectivity, and heat-release rate and provides thermal-management guidance in real time. This approach extends the calorimetric dimension of process analytics for photochemistry and supports safer, more efficient process development.</p>

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Calorimetry informed visual digital model for continuous flow photobromination

  • Yiming Xu,
  • Yun Zou,
  • Junfei Zhang,
  • Fujun Li,
  • Shengyang Tao

摘要

Calorimetry data for continuous-flow photochemistry are scarce, hindering kinetic model development and safe scale-up. Here we show that in situ calorimetry combined with designed experiments enables a quantitative, heat-consistent kinetic description for the continuous-flow photobromination of (E)-methyl 2-(methoxyimino)-2-(o-tolyl) acetate (EMMA). We record total heat release, conversion, and selectivity over defined windows of residence time and light intensity, and fit a two-step kinetic scheme constrained by the calorimetric heat-release data. The model predicts total heat with R2 = 0.957 and conversion with R2 = 0.894, while the second bromination step shows negligible dependence on light intensity within the explored window, indicating thermally driven behaviour. We translate these parameters into a real-time visual digital model that maps the spatial distributions of conversion, selectivity, and heat-release rate and provides thermal-management guidance in real time. This approach extends the calorimetric dimension of process analytics for photochemistry and supports safer, more efficient process development.