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Rational tailoring of catalytic systems offers highly desirable transformations targeting the growing environmental challenges associated with plastics pollution. For example, the identification of efficient catalysts to address alarming end-of-life Nylon pollution remains underexplored. Nylon-6 is a non-biodegradable high-performance engineering plastic with centuries of chemical persistence, resulting in millions of tons of waste accumulation. Here we report the rational manipulation of organolanthanide catalyst structure to achieve an exceptionally efficient, solventless, and scalable Nylon-6 depolymerization process, affording monomer ε-caprolactam in ≥99% yield. Specifically, catalyst Cp*₂LaCH(TMS)₂ (Cp* = η₅-C₅Me₅, TMS = SiMe₃) operates at catalyst loadings as low as 0.2 mol% and temperatures as low as 220 °C. For efficient deconstruction of more recalcitrant commodity Nylon-6 end-of-life articles such as fishing nets, car-pets, and clothing, the robust, thermally stable ansa-metallocene catalyst Me₂SiCp’’₂YCH(TMS)₂, (Cp’’ = η₅-C₅Me₄) effects >99% conversion of these items into ε-caprolactam. The collected product can be readily re-polymerized to afford pristine Nylon-6 with higher molecular masses and comparable structural regularity, providing a superior upcycling pathway for end-of-life Nylon plastics. Experimental mechanistic studies reveal intriguing and effective depolymerization pathways, such as catalytic intrachain “unzipping” enabled by the catalyst π-ancillary ligand steric constraints. Effective interchain “hopping” mechanisms, as well as chain-end deactivation are also demonstrated and supported by DFT analyses.

Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online.

Acid-catalyzed hydrocarbon transformations are essential for industrial processes, including oligomerization, cracking, alkylation, and aromatization. However, these chemistries are extremely complex, and computational (automatic) reaction network generation is required to capture these intricacies. The approach relies on the concept that underlying mechanisms for the transformations can be described by a limited number of reaction families applied to various species, with both gaseous and protonated intermediate species tracked. Detailed reaction networks can then be tailored to each industrially relevant process for better understanding or for application in kinetic modeling, which is demonstrated here. However, we show that these networks can grow very large (thousands of species) when they are bound by typical carbon number and rank criteria, and lumping strategies are required to decrease computational expense. For acid-catalyzed hydrocarbon transformations, we propose lumping isomers based on carbon number, branch number, and ion position to reach high carbon limits while maintaining the high resolution of species. Two case studies on propene oligomerization verified the lumping technique in matching a fully detailed model as well as experimental data.

Summary This perspective discusses a set of design principles for next-generation kinetically trapped, intrinsically circular polymers (iCPs) that are inherently, selectively, and expediently depolymerizable to their monomer state once their kinetic barriers of deconstruction are overcome, thereby enabling not only the ideal shortest chemical circularity but also tunable performance properties. After describing four elements of the design principles—thermodynamics and kinetics, strategies to overcome trade-offs and unify conflicting properties, predictive modeling, and supply-chain life-cycle assessment and techno-economic analysis, which are illustrated with state-of-the-art examples—it concludes with presenting key challenges and opportunities for sustainable development of iCPs.

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a novel combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

Porous aluminosilicates such as zeolites are ubiquitous catalysts for the production of high-value and industrially relevant commodity chemicals, including the conversion of hydrocarbons, amines, alcohols, and others. Bimolecular reactions are an important subclass of reactions that can occur on Brønsted acid sites of a zeolite catalyst. Kinetic modeling of these systems at the process scale requires the interaction energetics of reactants and the active sites to be described accurately. It is generally known that adsorption is a coverage-dependent phenomenon, with lower heats of adsorption observed for molecules at higher coverage. However, few studies have systematically investigated the coadsorption of molecules on a single active site, specifically focusing on the strength of interaction of the second adsorbate after the initial adsorption step. In this work, we quantify the unimolecular and bimolecular adsorption energies of varying adsorbates, including paraffins, olefins, alcohols, amines, and noncondensible gases in the acidic and siliceous ZSM-5 frameworks. As a special case, olefin adsorption was examined for physisorption and chemisorption regimes, characterized by π-complex, framework alkoxide and carbenium ion adsorption, respectively. The effects of functional groups and molecular size were quantified, and correlations that relate the adsorption of the second adsorbate identity to that of the first adsorbate are provided.

Pyrolysis of polyolefins has been proposed as a potential resource recovery strategy by converting macromolecules into valuable fuels and chemicals. Due to variations in possible backbone structures, chain-length distributions, and arrangements of pendant groups, their decomposition behavior via pyrolysis can be complex. In the present work, a review of historical data and empirical models for two distinct polyolefins, polyethylene (PE) and polypropylene (PP), is provided followed by a comparison to recent mechanistic models. The characteristic sigmoidal behavior of linear polymer decomposition is captured with global, lumped-species, and mechanistic models of high-density polyethylene. The PE model was extended to simulate PP using the same reaction families and reaction family parameters, but with distinct rate coefficients that accounted for the difference in the structure of PP with its pendant methyl groups compared to PE as manifested through heats of reaction embedded in the Evans–Polanyi relationship, Ea= E0 + γ×ΔHreacn. The change in structure and its associated kinetic parameters resulted in no sigmoidal conversion, consistent with experimental reports for atactic PP. This suggests that mechanistic modeling could be an important complement to global model studies to understand when other effects are at play in the pyrolytic decomposition of polymers such as PP.

Sourcing polymers from biobased materials is desirable for a transition to a more sustainable world. However, finding new monomers sourced from biobased molecules requires extensive experimental ef...

The microkinetic modelling methodology that we developed previously to describe Bronsted acid-catalyzed propene oligomerization on medium-pore MFI zeolites has been extended successfully to large-pore Beta zeolites. The extension of the model was supported by the identification of the key descriptors that account for the different topologies and acid strengths of the zeolite frameworks (physisorption enthalpies, stabilization enthalpies, frequency factors). The model is validated with experimental conversion and selectivity data measured in a plug-flow reactor on a commercial Beta zeolite over a range of operating conditions. Analysis of net reaction rates allowed identifying the preferred pathways that increase oligomerization selectivity toward C9 species with increasing propene pressure. The model was additionally used to investigate how the stabilization enthalpies of chemisorbed intermediates, an important catalyst descriptor, influenced the selectivity and surface coverage at iso-conversion. This analysis provides mechanistic insights into the propene oligomerization reaction network and its dependence on zeolite topology, and demonstrates how microkinetic models can describe catalyst behaviour and aid in catalyst and process optimization.

A kinetic Monte Carlo model of polyurethane polymerization which explicitly tracks the polymer sequences is developed and shared. This model is benchmarked against theoretical and experimental polyurethane data and used to investigate the effect on oligomer distributions of unequal reactivity of the first and second isocyanate to react. The reverse reactions using thermodynamic consistency are then added to the framework, and analogous to the addition polymerization concept of ceiling temperature, equilibrium chain length distributions at various temperatures are calculated. For a mixture of three monomers AA, BB, and CC, where BB and CC do not react with one another, are present in stoichiometric proportions, and have different enthalpies of reaction with AA, an odd-even effect emerges. Odd length chains are more likely than even length chains for temperatures at which BB and CC have significantly different equilibrium conversions. The concept of ceiling temperature that is typically cited for addition polymers is extended here to provide a measure of conditions under which depolymerization for recycling is favored.

Fast pyrolysis of biomass is an important technology in the conversion of lignocellulosic feedstocks to value-added fuels and chemicals. Significant efforts have been dedicated to modeling of these...

Light alkanes from shale resources can potentially be converted to an easy-to-transport liquid hydrocarbon product by catalytic dehydrogenation followed by catalytic oligomerization. The chemical s...

Lignin liquefaction microkinetics is a move toward a more first-principles (i.e., ab initio)-based understanding at the molecular level in reaction engineering. While the microkinetic modeling of r...

Density functional theory (DFT) calculations, experimental data, and microkinetic modeling are used to extend a triple-pathway (Lewis acid, water-mediated, and alcohol-mediated) mechanism for tris(...

Metabolic models containing kinetic information can answer unique questions about cellular metabolism that are useful to metabolic engineering. Several kinetic modeling frameworks have recently been developed or improved. In addition, techniques for systematic identification of model structure, including regulatory interactions, have been reported. Each framework has advantages and limitations, which can make it difficult to choose the most appropriate framework. Common limitations are data availability and computational time, especially in large-scale modeling efforts. However, recently developed experimental techniques, parameter identification algorithms, as well as model reduction techniques help alleviate these computational bottlenecks. Opportunities for additional improvements may come from the rich literature in catalysis and chemical networks. In all, kinetic models are positioned to make significant impact in cellular engineering.

Epoxide alcoholysis is extensively employed in the synthesis of polymers and chemical intermediates, and it generally requires an acid catalyst for high rates and selectivity. Tris(pentafluoropheny...

The construction of a computational framework that describes the kinetic details of the propylene oligomerization reaction network on Bronsted acidic zeolites is particularly challenging due to the...

Developing autotrophic, acetogenic bacteria strains as gas fermentation platforms is a promising avenue for converting industrial waste gas streams into valuable chemical products. One such strain, Clostridium autoethanogenum, naturally converts CO, CO2, and H2 gases into ethanol and acetate. Currently, lowering the acetate to ethanol production ratio is a key strategy for accomplishing large-scale industrial application of C. autoethanogenum gas fermentation. Unfortunately, the limited availability and time-intensive implementation of genetic engineering tools for clostridia strains greatly hinders metabolic engineering efforts toward this goal. To alleviate the lack of sufficient mutant phenotype data interrogating the pathways of interest, computational tools are needed to resolve experimental observations and predict engineering targets to help minimize experimental characterization in the lab. While stoichiometric models of C. autoethanogenum metabolism are available, they are unable to provide insight into regulatory relationships, rate-limiting steps, or the effects of altering enzyme expression. In this work, we offer the first kinetic representation of C. autoethanogenum core metabolism developed using the Ensemble Modeling (EM) framework. We have adapted the existing method to enable the usage of non-genetic perturbation data, specifically the effects of changing biomass concentration, to sample and train our kinetic parameter sets. Our final kinetic parameter ensemble accurately predicts intracellular metabolite concentrations and engineering strategies for improved ethanol production.

Bio-oil produced from fast pyrolysis of biomass is a complex mixture of more than 200 compounds, including oxygenates and acids. As these species are highly undesirable in fuels, catalytic upgradin...

Background The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach. Results Here, we developed a genome-scale metabolic model of the mouse ovarian follicle. This model was constructed using an updated mouse general metabolic model (Mouse Recon 2) and contains several key ovarian follicle development metabolic pathways. We used this model to characterize the changes in the metabolism of each follicular cell type (i.e., oocyte, granulosa cells, including cumulus and mural cells), during ovarian follicle development in vivo. Using this model, we predicted major metabolic pathways that are differentially active across multiple follicle stages. We identified a set of possible secreted and consumed metabolites that could potentially serve as biomarkers for monitoring follicle development, as well as metabolites for addition to in vitro culture media that support the growth and maturation of primordial follicles. Conclusions Our systems approach to model follicle metabolism can guide future experimental studies to validate the model results and improve oocyte maturation approaches and support growth of primordial follicles in vitro. Electronic supplementary material The online version of this article (10.1186/s12859-019-2825-2) contains supplementary material, which is available to authorized users.

Bioprivileged molecules are biology-derived chemical intermediates that can be efficiently converted to a diversity of chemical products including both novel molecules and drop-in replacements. Bridging chemical and biological catalysis by bioprivileged molecules provides a useful and flexible new paradigm for producing biobased chemicals. However, the discovery of bioprivileged molecules has been demonstrated to require extensive experimental effort over a long period of time. In this work, we developed a computational framework for identification of all possible C6HxOy molecules (29252) that can be honed down to a manageable number of candidate bioprivileged molecules based on analysis of structural features, reactive moieties, and reactivity of species, and the evaluation of the reaction network and resulting products based on automated network generation. Required input is the structure data file (SDF) of the starting molecules and the reaction rules. On-the-fly estimation of thermodynamics by a group...

Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. We developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.

In a noncontact catalytic system, intermediaries derived from Au-catalyzed cyclooctene epoxidation effects ethylbenzene oxidation., Traditionally, a catalyst functions by direct interaction with reactants. In a new noncontact catalytic system (NCCS), an intermediate produced by one catalytic reaction serves as an intermediary to enable an independent reaction to proceed. An example is the selective oxidation of ethylbenzene, which could not occur in the presence of either solubilized Au nanoclusters or cyclooctene, but proceeded readily when both were present simultaneously. The Au-initiated selective epoxidation of cyclooctene generated cyclooctenyl peroxy and oxy radicals that served as intermediaries to initiate the ethylbenzene oxidation. This combined system effectively extended the catalytic effect of Au. The reaction mechanism was supported by reaction kinetics and spin trap experiments. NCCS enables parallel reactions to proceed without the constraints of stoichiometric relationships, offering new degrees of freedom in industrial hydrocarbon co-oxidation processes.

A nonconventional, water-mediated catalytic mechanism was proposed to explain the effects of residual water on the reactivity and regioselectivity of tris(pentafluorophenyl)borane catalyst in the ring-opening reaction of 1,2-epoxyoctane by 2-propanol. This nonconventional mechanism was proposed to operate in parallel with conventional Lewis acid-catalyzed ring-opening. Microkinetic modeling was conducted to validate the proposed reaction mechanism, with all kinetic and thermodynamic parameters derived from density functional theory (DFT) calculations. Experimental data at a variety of temperatures and water contents were captured by the model after adjustments within reasonable limits set by experimental benchmarking and accuracy of theory of a small subset of parameters. In addition, the microkinetic model was able to generate accurate predictions at reaction conditions that were not used for parameter estimation. Detailed analysis of the net reaction rates showed that >95% of the reaction flux passed th...

The construction of mechanistic models for the autoxidation of fatty acid or ester substrates found in oil paint binders is a challenging undertaking due to the complexity of the large crosslinked species that form, and the small molecules that volatilize. Building models that capture this product diversity are made possible by automating the process of network generation. This work presents a microkinetic model for the autoxidation of ethyl linoleate catalyzed by cobalt(II) 2-ethyl hexanoate. The mechanism size was controlled by using a rate-based criterion to include the most kinetically relevant reactions from among the millions of possible reactions generated. The resulting model was solved and compared to experimental metrics. Quantities such as hexanal production and the consumption of unsaturated moieties were in good agreement with experiment. Finally, the model was used to explore the effect of the catalyst concentration and temperature on key measurables.

Small gold nanoparticles (AuNPs) have recently shown potential to act as a catalyst for oxidation reactions mediated by free radicals, with their role postulated to be facilitating hydrogen abstraction by gold superoxo species and/or activation of hydroperoxides. Cyclooctene oxidation using molecular oxygen as the oxidant at 373 K showed high selectivity to the epoxide product, cyclooctene oxide, with either tert-butyl hydroperoxide as an initiator or in the presence of small AuNPs (5–8 atoms). While previous studies have investigated the mechanism leading to high epoxide selectivity using density functional theory, a full microkinetic model was developed in this work using automated network generation to determine the relative contributions of elementary reactions and the role of AuNPs. A cycle of radical addition of peroxy and alkoxy radicals with subsequent epoxidation reactions can justify the observed activity and selectivity to epoxide at multiple temperatures and initiator concentrations. The alcoh...

Lignocellulosic biomass is a promising feedstock for renewable fuels and chemical intermediates; in particular, lignin attracts attention for its favorable chemical composition. One obstacle to lignin utilization and valorization is the unknown chemical mechanism that gives rise to the complex product distributions observed upon deconstruction. Among possible deconstruction chemistries, fast pyrolysis is promising due to its short residence time, thus enabling high-volume production. However, the chemistry is inherently complex, thereby hampering the creation of detailed kinetic models describing pathways to specific low molecular products. To this end, we created a detailed kinetic model of lignin decomposition via pyrolysis comprised of 4313 reactions and 1615 species based on pathways suggested by pyrolysis of model compounds in the literature. Using a rule-based reaction network generation approach, a pathways-level reaction network is proposed to predict the evolution of macromolecular species and th...

Hemicellulose is one of the major components of lignocellulosic biomass, which is an abundant source of renewable carbon on the Earth and has potential for the production of renewable drop-in transportation fuels and multiple commodity chemicals. In this work, a structure for hemicellulose extracted from corn stover was proposed to capture the experimentally characterized structural properties. A mechanistic model for hemicellulose pyrolysis was constructed based on the reaction family approach that we used for cellulose pyrolysis before. The model described the decomposition of hemicellulose chains, reactions of intermediates, and formation of a range of low molecular weight products (LMWPs) at the mechanistic level and specified rate constants for all the reactions in the network. Overall, 504 reactions of 114 species were included in the mechanistic model for fast pyrolysis of extracted hemicellulose. The mechanistic model closely matched experimental yields of various products with mass yield ≥1 wt%. Modeling results show that both the degree of polymerization and the polydispersity index of hemicellulose have an insignificant effect on the pyrolysis product distribution. Then, the mechanistic model of extracted hemicellulose is further extended to simulate the fast pyrolysis of native hemicellulose. Comparison of the model results showed that fast pyrolysis of native hemicellulose from corn stalk yielded more char, gaseous species, acetol, and much more acetic acid than that of extracted hemicellulose from corn stover, while yielding less 1,2-anhydroxylopyranose, 1,2;3,4-dianhydroxylopyranose and glycolaldehyde.

The diversity of the potential arrangements of multiple monomers along the length of polymer chains and their impact on polymer properties spark interest in the design of polymer sequence characteristics for particular applications. Kinetic Monte Carlo (KMC) is a technique that can track the explicit arrangement of monomers in the polymer chains, yet it is difficult to integrate with conventional gradient-based optimization algorithms that are typically invoked to design polymer properties. In this work, we applied and compared derivative-free optimization algorithms to incorporate KMC simulations and find synthesis conditions for achieving property targets and minimizing reaction time, advancing our ability to carry out the design of polymer microstructures and control polymerization processes.

The mechanisms responsible for the production of small volatile aldehydes during low temperature condensed phase oxidation have been the subject of extensive research, and many pathways have been proposed in the literature. However, many of these mechanisms have yet to be explored quantitatively in the context of a kinetic model. A variety of mechanistic postulates for the formation of the volatile species hexanal, such as direct β-scission of alkoxy radicals, Korcek-like decomposition reactions, intramolecular hydrogen shifts, intramolecular reactions of allylic peroxy species, and scission reactions of higher order oligomeric species, were assembled, and quantum chemical calculations were performed where necessary to obtain estimates of kinetic parameters to test each reaction’s kinetic relevance in a microkinetic model for the oxidation of a cobalt-catalyzed ethyl linoleate system. Under atmospheric conditions, scission of chain ends from dimeric species was the largest contributor of hexanal, with an ...

Light alkene oligomerization on heterogeneous acidic catalysts is widely and successfully used in current commercial processes. However, ethylene oligomerization remains inefficient due to ethylene’s inability to form reaction intermediates to a sufficient extent on acid sites. Adding Ni(II) on solid acids can more efficiently catalyze ethylene oligomerization and selectively produce butenes to fuel range products. The review proposes a complete and detailed mechanism of heterogenous Ni-catalyzed oligomerization, whose structures are supported by combining various studies throughout recent literature, and focuses on the bifunctional effects of the nickel and acid sites on ethylene oligomerization. Using experiments, first-principles calculations, and kinetic modeling, Ni2+ has been shown to selectively oligomerize ethylene to light, linear alkenes via the Cossee-Arlman mechanism, while Bronsted H+ sites catalyze further alkylation, cracking, and isomerization reactions. The effects of reaction conditions and catalyst properties on selectivity and activity for oligomerization are systematically discussed. Tuning the relative nickel-to-acid site ratio and the framework support can allow for an optimal catalyst design directed towards desirable products.

Quantum chemical calculations are emerging as an effective way to screen catalysts for particular applications. In this contribution, we demonstrate the power of density functional theory to study CO2 hydrolysisby six carbonic anhydrase mimics, evaluating thermodynamic and kinetic parameters at the mechanistic level. A microkinetic model was then built based on the kinetics and thermodynamics calculated from first principles. The intrinsic reaction rate constant was calculated from the results of the microkinetic model and compared with experimental data. Overall, the rate constants were in good agreement with experimental values, except for zinc-tri and complex b, which were overestimated. This was ascribed to their ineffective complexation with Zn2+. How the reaction rate constants vary with time was also investigated. From 0 to 12 ms, the rate constants of complexes a and d decreased to 50 and 67% of their initial values, respectively; the rate constants of complexes b and f2 were almost invariant with time; the rate constant of complex f1 showed an unusual double sigmoidal shape. The pKa values of these six carbonic anhydrase mimics as well as three additional mimics were calculated. A correlation between pKa values and the binding free energy of OH-was obtained by fitting data from five zinc(II) aza-macrocyclic complexes. The reaction rate constants were found to increase linearly with the pKa value, indicating CO2 adsorption is the rate-limiting step.

The modeling of the molecular weight distribution of polymers is critical in polymerization modeling. Moment-based models and kinetic Monte Carlo are two methods used to calculate the number and weight average molecular weight during simulation of polymerization. While these two methods are commonly considered to yield equivalent results in the molecular weight distribution, we show that there can be significant differences between these two methods when copolymerization of multiple monomers is modeled. In particular, clear differences in results exist for low molecular weight polymers where the molar mass of one monomer is two or more times greater than the others. Detailed analysis is provided for the cause of the difference, and we demonstrate the importance of tracking the exact number of each type of monomer in every polymer chain when calculating the moments of molecular weight.