Review and Analysis of CO₂ Photoreduction Kinetics

Abstract

Microkinetic, in situ analytics, and empirical modeling approaches for developing intrinsic CO2 photoreduction kinetics are presented in this Perspective. Intrinsic kinetic models that are independent of photoreactor geometry are critical for scaling CO2 photoreduction photoreactors. Successfully scaling CO2 photoreduction is limited using the current extrinsic CO2 photoreduction kinetic models described in this Perspective, because they are dependent on the photoreactor geometry and scale used. The impact of different photoreactor geometries and light transport that lead to extrinsic kinetic models is reviewed. The impacts of temperature and pressure on surface diffusion is highlighted as additional important process parameters. The current Langmuir–Hinshelwood-based kinetic models are discussed, and their limitations are highlighted, with respect to modeling the possible deactivation of the photocatalyst. With a view on developing an intrinsic kinetic model, the challenges for developing CO2 photoreduction kinetics are highlighted and discussed with reference to the current extrinsic CO2 photoreduction kinetic model examples found in the literature. Robust analytical methods for collecting CO2 photoreduction kinetic data and for confirming the carbon source are discussed. The false positive production from adventitious carbon and organic impurities introduced during the synthesis and/or coating of photocatalysts with solvents and degradation of photoreactor components is highlighted as a challenge to collecting CO2 photoreduction kinetic data. It is shown that a wide range of the kinetic model coefficient values are possible when using a multistart, genetic algorithm, or particle swarm approach for estimating nonlinear model coefficients. Finally, an easy to test and implement approach using a mean median multistart and trust-region reflective algorithm method is presented for the estimation of nonlinear CO2 photoreduction kinetic model coefficients.

Inside‐and‐Out Semiconductor Engineering for CO2 Photoreduction: From Recent Advances to New Trends

“…HCOOH generation can usually be achieved by sulfides (e.g., ZnS) with suitable metal doping, such as Ni or Cd, in liquid systems, and this process needs the same number of protons and electrons as CO formation, but demanding a higher overpotential. [ 63 ] Other products are usually hard to be observed with a high activity and selectivity, because they are always thermodynamically uphill and consume different electrons or protons through multiple reactions. [ 64 ] In practice, photocatalytic CO 2 reduction is much more complex due to various competing reactions, such as H 2 evolution in aqueous system.…”

Reticular Materials for Artificial Photoreduction of CO2

“…[37] This is understandable because of the inert CO bond in the linear OCO molecule demanding very high dissociation energy of >805 kJ mol −1 . [2,38,39] The highly kinetic barriers required for the transformation of the symmetrically linear D∞ h of CO 2 molecule to the bent radical anion of CO 2…”

“…[ 37 ] This is understandable because of the inert CO bond in the linear OCO molecule demanding very high dissociation energy of >805 kJ mol −1 . [ 2,38,39 ] The highly kinetic barriers required for the transformation of the symmetrically linear D ∞h of CO 2 molecule to the bent radical anion of CO 2 •− adopting C 2 v symmetry imply that this intermediate reaction cannot be catalyzed by semiconductors unless there exists a catalyst possessing a reduction potential more negative than −1.90 V versus NHE. Figure 2 underlines the common reduction potentials of semiconductors.…”

Photocatalytic toluene degradation: braiding physico-chemical and intrinsic kinetic analyses

“…Otherwise the solution can have some uncertainly due to the complex surface of hyperpotential of the fitting problem. 35 A MATLAB® R2018b algorithm was build up to obtain the kinetic parameters using a subroutine to solve (simultaneously) the differential eqn (1) and (3) (subroutine ode45 based in a Runge-Kutta formalism) subjected to boundary conditions (eqn (2) and (4)), coupled with a nonlinear least-squares fitting algorithm (lsqnonlin, algorithm: trust-region-reflective optimization) to obtain the parameters of eqn (5) and (6). To provide an insightful analysis of the fitting procedure, we used the "MultiStart" MATLAB formalism 36 in order to obtain a global minimum (mathematical) of the fitting procedure and compare it with the result obtained using the additional constrains (with physical meaning) coming from introducing the relationship between alpha parameters used to define the beta parameters (Table 3).…”