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The strong binding energy of CO on iron surfaces has rendered Fe electrodes as poor electrochemical CO₂reduction (eCO2R) catalysts, predominantly producing hydrogen. Recent studies on tuning the microenvironment near the catalyst surfaces by tuning the local electric field in nonaqueous environments have been shown to promote eCO2R by facilitating the CO₂activation step. Herein, the use of tetraethylammonium (TEA) cation to tune the electric field on Fe surfaces, such that it leads to the formation of industrially relevant oxalates (C₂products), is reported. At optimal cation concentrations, the developed eCO2R system achieves 25 mA cm⁻²of current density and Faradaic Efficiencies up to 75% toward oxalate. Furthermore, in situ attenuated total reflectance Fourier transform infrared spectroscopy indicates the presence of surface‐adsorbed TEA cations and other species on the Fe surfaces, leading to the well‐known outer‐sphere mechanism of electron transfer during eCO2R. The employment of Fe, along with microenvironment tuning, not only demonstrates high catalytic performance but also provides a safer and more sustainable alternative to toxic catalysts such as Pb that dominate the nonaqueous eCO₂R literature. These findings pave the way for further optimization and scale‐up of the process, offering a viable route for sustainable chemical production and CO₂mitigation. 

In-situ characterization techniques, although complex, can provide a wealth of insight into material chemistry and evolution dynamics. Grasping the fundamental kinetics of material synthesis is essential to enhance and streamline these processes and facilitate easier scaleup. Metal–organic frameworks (MOFs), a class of porous crystalline materials discovered three decades ago, have been developed and implemented in various applications at the laboratory scale. However, only a few studies have explored the fundamental mechanisms of their formation that determine their physical structure and chemical properties. Independent experimental and theoretical investigations focusing on chemical kinetics have provided some understanding of the mechanisms governing MOF formation. However, more effort is needed to fully control their formation pathways and properties to enhance stability, optimize performance, and design strategies for scalable production. This Perspective highlights current techniques for studying MOF kinetics, discusses their limitations, and proposes multimodal experimental and theoretical protocols, emphasizing how improved data acquisition and multiscale approaches can advance scalable applications. 

The morphological evolution of organic crystals during crystallization depends on the face-specific growth rates. Classical growth rate models relate the face-specific growth rates to the crystal lattice, energy of stable facets, growth mechanism, and supersaturation. The complexities of these models have increased over time to account accurately for solution conditions, the structure of growth units, and their attachment rates. Such advanced growth rate models require several layers of computations to obtain attachment energies of facets, nucleation rates, kink density, and attachment rates. Among these, the most intensive and time-consuming computation is for attachment rates, which require molecular dynamic simulations. This substantially increases the overall computation time to predict the absolute growth rate for even one crystallization condition. Since it is nearly impossible to iterate such a growth rate model, optimization schemes cannot be implemented to identify solution conditions that favor specific crystal growth. To reduce the computational time for attachment rate calculations, we implement a group contribution method (GCM) that relates the properties of functional groups in a molecule to their attachment rates to the crystal lattice, thereby rapidly estimating the growth rates of organic crystals. The process of molecular attachment involves partial desolvation of a solvated molecule, referred to as a transition state, followed by total desolvation via spontaneous attachment to a crystal facet. The first step in GCM is to identify the equilibrium states of fully solvated and partially desolvated solute molecules. The degree of supersaturation dictates the extent of this equilibrium and, thereby, the activation barrier for the growth of crystals, according to transition state theory. Identifying this equilibrium phenomenon allows for capturing the functional-group-specific interactions that depend on molecular motion, which could be related to operating conditions such as temperature and pressure. The stochastic optimization technique with Monte-Carlo sampling allows an efficient optimization problem solution to obtain the group interaction parameters. The GCM approach is first validated for the estimation of growth rates of glutamic acid and L-histidine, and then extended to predict growth rates of alanine and glycine rapidly. The optimized parameters and GCM scheme can be used to estimate growth rates in other crystallization systems. 

The induction time for the onset of nucleation is known to decrease with increasing solution supersaturation. A large variation in induction time is experimentally observed for various organic crystals, whose origin is often associated with the stochastic nature of the nucleation process. Although several empirical models for induction time and nucleation rate have been developed, they remained highly unreliable, with model predictions differing by orders of magnitude from experimental measurements. A satisfactory explanation for the induction time variation has not been developed yet. We report here that the variations in induction times can be attributed to a previously unrecognized consequence of the phase separation or emulsification of supersaturated solution, in addition to the effect of stochastic nucleation. A large-scale Brownian dynamics simulation of antisolvent crystallization of histidine in a water–ethanol mixture is performed to demonstrate the mechanism of microphase/emulsion formation in supersaturated solutions and its consequence on induction time variation. Furthermore, we show that the average induction time depends on supersaturation, and the supersaturation-dependent diffusion of histidine molecules governs the stochastic nature of the induction time. Moreover, at varying supersaturations, the likelihood of forming stable and metastable polymorphs of histidine was estimated. This approach provides valuable insights into the crystallization behavior of histidine, and predicted induction time reasonably matches the experimentally observed induction time. 

The chemical pathway for synthesizing covalent organic frameworks (COFs) involves a complex medley of reaction sequences over a rippling energy landscape that cannot be adequately described using existing theories. Even with the development of state-of-the-art experimental and computational tools, identifying primary mechanisms of nucleation and growth of COFs remains elusive. Other than empirically, little is known about how the catalyst composition and water activity affect the kinetics of the reaction pathway. Here, for the first time, we employ time-resolved in situ Fourier transform infrared spectroscopy (FT-IR) coupled with a six-parameter microkinetic model consisting of ∼10 million reactions and over 20 000 species. The integrated approach elucidates previously unrecognized roles of catalyst p K a on COF yield and water on growth rate and size distribution. COF crystalline yield increases with decreasing p K a of the catalysts, whereas the effect of water is to reduce the growth rate of COF and broaden the size distribution. The microkinetic model reproduces the experimental data and quantitatively predicts the role of synthesis conditions such as temperature, catalyst, and precursor concentration on the nucleation and growth rates. Furthermore, the model also validates the second-order reaction mechanism of COF-5 and predicts the activation barriers for classical and non-classical growth of COF-5 crystals. The microkinetic model developed here is generalizable to different COFs and other multicomponent systems. 

The two-step nucleation (TSN) theory and crystal structure prediction (CSP) techniques are two disjointed yet popular methods to predict nucleation rate and crystal structure, respectively. The TSN theory is a well-established mechanism to describe the nucleation of a wide range of crystalline materials in different solvents. However, it has never been expanded to predict the crystal structure or polymorphism. On the contrary, the existing CSP techniques only empirically account for the solvent effects. As a result, the TSN theory and CSP techniques continue to evolve as separate methods to predict two essential attributes of nucleation – rate and structure. Here we bridge this gap and show for the first time how a crystal structure is formed within the framework of TSN theory. A sequential desolvation mechanism is proposed in TSN, where the first step involves partial desolvation to form dense clusters followed by selective desolvation of functional groups directing the formation of crystal structure. We investigate the effect of the specific interaction on the degree of solvation around different functional groups of glutamic acid molecules using molecular simulations. The simulated energy landscape and activation barriers at increasing supersaturations suggest sequential and selective desolvation. We validate computationally and experimentally that the crystal structure formation and polymorph selection are due to a previously unrecognized consequence of supersaturation-driven asymmetric desolvation of molecules.