The isomerization of epoxides is a valuable transformation in synthetic chemistry, offering an atom-economical route to aldehydes and ketones from readily available substrates. In this study, we investigate tris(pentafluorophenyl)borane (B(C6F5)3 or BCF) as a Lewis acid catalyst for the Meinwald rearrangement using propylene oxide and other epoxides as models. The effects of residual water, temperature, and solvent properties such as coordination strength, polarity, and hydrogen-bonding ability are systematically examined to determine their impact on catalytic activity and product selectivity. 19F and 2D HOESY NMR spectroscopy, along with density functional theory (DFT) calculations, reveal key BCF-solvent interactions that govern catalytic behavior. In particular, [BCF & sdot;OH2]& sdot;(solvent) pre-catalyst complexes are proposed to explain the activation period observed in strongly coordinating solvents. BCF is very active for aldehyde formation, and these findings highlight the critical roles of water activity and solvent coordination strength in modulating catalytic activity by determining the amount of free BCF. In addition, hydrogen-bonding ability of the solvent governs the selectivity towards Meinwald rearrangement products in BCF-catalyzed reactions. The insights gained are broadly applicable to understanding catalyst-solvent interactions in other organoborane systems.

As customer-driven demand for sustainable solutions increases, so does the clamor for more efficient post-consumer plastic recycling methods. While recycling reactor conditions can be explored experimentally, it is advantageous to employ in silico methods. This Comment focuses on detailed mechanistic approaches for modeling the depolymerization of plastics, the current state of this field and the directions it should take.

In this work, we develop a microkinetic model for the protodeborylation of tris(pentafluorophenyl)borane (BCF) under reaction conditions relevant to epoxide ring opening by 1-propanol. The model builds on density functional theory calculations that capture shells of explicit molecules near the boron center that participate in hydrogen bonding interactions. Decomposition of BCF occurs via both hydrolysis and alcoholysis, and participation of the decomposition product, bis(pentafluorophenyl)borinic acid, as a catalyst is also considered. Given all of the possible multi-body species, 26 distinct reaction pathways are included in the model. The model results are compared with experimental data that was collected as a function of reaction temperature, water content, and 1-propanol concentration. Speciation and net flux analysis based on the model results reveal the interplay between species binding and facile elementary steps for decomposition and rationalize the seemingly counterintuitive result that rates of protodeborylation are lower in 1-propanol than in non-protic solvents like xylenes. Furthermore, this model shows robustness when benchmarked against experimental trials across a broad range of conditions that were not used for training. This work aims to offer a foundation for predicting borane catalyst degradation pathways and designing more robust catalytic systems for sustainable chemical processes such as epoxide ring-opening reactions

Uncharacterized functions of enzymes represent untapped opportunity to develop therapeutics, unlock the sustainable synthesis of materials, and understand the evolution of life-sustaining metabolic networks. Enzymes and de novo reactions (i.e., non-native, promiscuous reactions), generated by protein language models and computer-aided synthesis tools, respectively, make up a large part of this opportunity. Given the technical complexity of high-throughput enzymatic activity screens, predictive models are needed that can pre-screen de novo enzyme-reaction pairs in silico. We present Reaction-Center Graph Neural Network, (RC-GNN) a model capable of predicting whether an enzyme, represented by an amino acid sequence, can significantly catalyze a given reaction, represented by its full set of reactants and products. We explicitly evaluated RC-GNN's generalization to de novo queries. In the most difficult conditions tested, where difficulty is measured by the level of dissimilarity between training and test data points, the model achieves 78.0% and 94.8% accuracy when reaction and enzyme similarity were respectively controlled. The ability to successfully make predictions on enzymes and reactions distinct from those used during training make RC-GNN especially useful for both metabolic engineers and evolutionary biologists who need to reason about uncharacterized enzymatic reactions.

If feasible, introducing chemodiversity into the selective chemical recycling of plastics would provide a resource- and catalyst-efficient means of recovering high-value building blocks from synthetic polymers for diverse recycling applications via straightforward alterations of catalytic conditions. Here we report the application of earth-abundant, readily available lanthanide-organic Ln[N(TMS)2]3 catalysts to the solventless chemodivergent, non-random back-biting depolymerization of poly(cyclohexenecarbonate) (PCHC) in high selectivity and near-quantitative conversion. Varying the lanthanide ionic radius across the 4f series and modifying the reaction conditions creates an efficient switch in PCHC depolymerization pathway to the corresponding epoxide (cyclohexene oxide; >99% selectivity; >94% yield) or the corresponding cyclic carbonate (trans-cyclohexene carbonate; >99% selectivity; 93% yield) monomer, each offering recycling value and closed-loop circularity. Combined experimental and theoretical DFT mechanistic analyses indicate two competing depolymerization pathways: low-energy reversible cyclic carbonate formation and rate-limiting irreversible decarboxylation. These catalysts are recyclable and applicable to plastics mixtures such as PCHC + nylon-6 + polyethylene, enabling sequential monomer capture with a single catalyst in the solvent-free process.

Most plastics recycled today are recycled mechanically, often referred to as downcycling due to the inevitable degradation of the polymer material. One alternative is to chemically recycle these materials back to a monomer, but this works most efficiently for intrinsically circular polymers (iCPs) that exhibit appropriate depolymerization thermodynamics and kinetics. In order to help design such iCP materials, modeling can provide insight into the effect of reaction conditions on their polymerization and depolymerization characteristics. Most iCPs reported are linear polymers, so architecturally complex hyperbranched polymers that exhibit complete chemical circularity are rare, and modeling on hyperbranched iCPs has not been reported. Here, we report a mechanistic model that incorporates chain-length-dependent transport phenomena and tracks the full polymer structure during the reversible polymerization of a hydroxyl-functionalized lactone leading to this hyperbranched polyester. This lays the groundwork for future modeling of this material’s depolymerization behavior and provides a framework that can be employed to study other iCPs.

Synthetic biology offers the promise of manufacturing chemicals more sustainably than petrochemistry. Yet, both the rate at which biomanufacturing can synthesize these molecules and the net chemical accessible space are limited by existing pathway discovery methods, which can often rely on arduous literature searches. Here, we introduce BioPKS pipeline, an automated retrobiosynthesis tool combining multifunctional type I polyketide synthases (PKSs) and monofunctional enzymes via two complementary tools: RetroTide and DORAnet. Monofunctional enzymes are valuable for carefully decorating a substrate's carbon backbone while PKSs are unique in their ability to iteratively catalyze carbon-carbon bond formation reactions, thereby expanding carbon backbones in a predictable fashion. We evaluate the performance of BioPKS pipeline using a previously reported set of 155 biomanufacturing candidates, achieving exact synthetic designs for 93 compounds and generating chemically similar pathways for most remaining targets. Furthermore, BioPKS pipeline can propose pathways for the complex therapeutic natural products cryptofolione and basidalin.

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

Replacing non-recyclable thermosets with covalent adaptable networks (CANs) that recover cross-link density after reprocessing will reduce waste and contribute to a circular polymer economy. Many CANs undergoing associative dynamic exchange require catalysis. External catalysis often leads to harmful effects, e.g., increased creep, accelerated material aging, and catalyst leaching. Herein, internally catalyzed siloxane dynamic chemistry is demonstrated resulting from amides covalently linked through alkyl chains to siloxanes. Small-molecule studies show the formation of exchange products resulting from the reaction of two amide-containing siloxane molecules. From the rubbery plateau modulus, each siloxane-exchange-based CAN exhibits a cross-link density that is temperature-invariant, or nearly so, characteristic of associative CANs. The alkyl length in the siloxane-containing monomer tunes the network cross-link density. Cross-link density recovery after reprocessing is achieved, with the required reprocessing time and temperature decreasing with increasing cross-link density. Stress relaxation is also faster with increasing cross-link density. The faster dynamics and reprocessability with increasing cross-link density arise because associative exchange is second order in siloxane (i.e., cross-linker) concentration. Capitalizing on this, the melt extrusion of the highest cross-link density CAN is demonstrated, achieving the same cross-link density in extruded and compression-molded CANs. Using identical conditions, the next-highest cross-link density CAN is not extrudable.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTCelebrating ACS Engineering Au's 2023 Rising Stars in Chemical EngineeringVivek V. Ranade*Vivek V. Ranade*Email: [email protected]More by Vivek V. Ranadehttps://orcid.org/0000-0003-0558-6971 and Linda J. Broadbelt*Linda J. Broadbelt*Email: [email protected]More by Linda J. Broadbelthttps://orcid.org/0000-0003-4253-592XCite this: ACS Eng. Au 2024, 4, 1, 1–3Publication Date (Web):February 21, 2024Publication History Received2 February 2024Published online21 February 2024Published inissue 21 February 2024https://doi.org/10.1021/acsengineeringau.4c00002Copyright © 2024 American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0. License Summary*You are free to share (copy and redistribute) this article in any medium or format within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is Open Access under the license indicated. Learn MoreArticle Views-Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (4 MB) Get e-AlertscloseSUBJECTS:Biomass,Chemical engineering and industrial chemistry,Environmental chemistry,Gold,Materials Get e-Alerts

Developing effective catalysis to address end -of -life Nylon pollution is urgent yet remains underdeveloped. Nylon -6 is a resilient synthetic plastic and a major contributor to ocean pollution. Here, we report a metallocene catalytic system based on earth -abundant early transition and lanthanide metals that mediates Nylon -6 depolymerization at unprecedented rates up to 810 (epsilon-caprolactam)center dot mol(Cat.)-1 center dot h-1 at 240 degrees C in R99% yield. This solventless process operates with catalyst loadings as low as 0.04 mol % at temperatures as low as 220 degrees C-the mildest Nylon -6 depolymerization conditions reported to date. This metallocene catalysis can be carried out in a simulated continuous process, and the resulting epsilon-caprolactam can be re -polymerized to higher -quality Nylon -6. Experimental and DFT analyses identify effective depolymerization pathways involving catalytic intra-Nylon-chain "unzipping"assisted by p-ligand effects and inter -chain "hopping."A robust chelating ansa-yttrocene is particularly effective in depolymerizing diverse commodity end -of -life articles, such as fishing nets, carpets, clothing, and plastic mixtures.

Biomass incorporates carbon captured from the atmosphere and can serve as a renewable feedstock for producing valuable chemicals and fuels. Here we look at how electrochemical approaches can impact biomass valorization, focusing on identifying chemical transformations that leverage renewable electricity and feedstocks to produce valorized products via electro-privileged transformations. First, we recommend that the field should explore widening the spectrum of platform chemicals derived from bio-feedstocks, thus offering pathways to molecules that have historically been derived from petroleum. Second, we identify opportunities in electrocatalytic production of energy-dense fuels from biomass that utilize water as the hydrogen source and renewable electricity as the driving force. Finally, we look at the potential in electrochemical depolymerization to preserve key functional groups in raw feedstocks that would otherwise be lost during harsh pre-treatments in traditional depolymerization routes. On the basis of these priorities, we suggest a roadmap for the integration of biomass and electrochemistry and offer milestones required to tap further into the potential of electrochemical biomass valorization. Biomass is a renewable source of carbon that can be exploited to produce valuable chemicals and fuels. This Perspective discusses the electrochemical valorization of biomass, identifying specific chemical transformations in which the approach can excel.

The rise of polymeric materials marks a notable achievement of the past century, yet challenges in recycling have led to their accumulation in various environments. Efforts to address this include advancements in mechanical recycling, degradation processes, and chemical recycling techniques, particularly chemical recycling to monomer, which offers a path toward a circular economy for plastics. In this perspective, we discuss how ceiling temperature (Tc) can be used as a design parameter for circular (closed-loop recyclable) polymers and provide an overview of typical experimental approaches for deriving Tc, focusing on ΔHp and ΔSp as the key parameters for prediction. The concept of Tc is heavily embedded in the polymer literature and provides a simple but still useful way of quickly ranking different polymers in terms of their relative thermodynamic stability of polymer versus monomer states. While Tc in the bulk state as an intrinsic value is a desirable quantity, it is infeasible in many cases to measure equilibrium states in the bulk; thus, many researchers have focused on investigating Tc in solution, where there may be dependencies of Tc on the solvent, concentration, or other factors, resulting in a family of apparent Tc values at each set of conditions. We thus explore computational studies as a complement to experimental measurements of Tc. To this end, we focus here on the advantages, obstacles, and outlook of the establishment of predictive computational approaches to calculate key thermodynamic parameters related to polymer circularity, namely ΔHp, ΔSp, ΔGp, and Tc values.

The thermal oligomerization of ethylene is an intriguing reaction for the production of fuel-range products. Often viewed as a detrimental reaction leading to polymeric deposits in pipelines, recent work suggests that it can be exploited to convert ethylene to higher hydrocarbons in the C5-C12 range. Despite the long history surrounding this reaction, quantum chemical simulations have not been fully deployed to provide full mechanistic understanding, and kinetic models to optimize reaction conditions are lacking. In this work, a microkinetic model based on quantum chemical calculations was developed to unravel the primary drivers of initiation and understand the formation of a variety of products of different carbon number, including odd-numbered products. Through flux analysis, the main driver of initiation was identified to be hydrogen abstraction from ethylene by a 1,4-butyl diradical produced from the reaction of two ethylene molecules. As conversion increased, the primary initiation mode switched to hydrogen abstraction by a butene diradical as 1-butene began to be produced in high quantities. Odd-numbered carbon species were seen to originate from the beta-scission of C8 radical species, with the radical position varied due to intramolecular hydrogen shift reactions from terminal radicals formed via radical addition reactions. The significant quantity of linear terminal olefins in the experimental product distribution was identified to originate from the formation of vinyl radicals through hydrogen abstraction reactions involving ethylene that were then propagated via radical addition reactions to ethylene. The insights from this work can aid in the development of intensified reactor systems to valorize ethane streams from shale gas production, converting waste streams directly to usable fuel products. Microkinetic model quantitatively captures conditions under which thermal oligomerization of ethylene leads to broad product distribution of linear alkenes with both even and odd carbon numbers.

Geminal ( gem −) disubstitution in heterocyclic monomers is an effective strategy to enhance polymer chemical recyclability by lowering their ceiling temperatures. However, the effects of specific substitution patterns on the monomer's reactivity and the resulting polymer's properties are largely unexplored. Here we show that, by systematically installing gem -dimethyl groups onto ϵ-caprolactam (monomer of nylon 6) from the α to ϵ positions, both the redesigned lactam monomer's reactivity and the resulting gem -nylon 6’s properties are highly sensitive to the substitution position, with the monomers ranging from non-polymerizable to polymerizable and the gem -nylon properties ranging from inferior to far superior to the parent nylon 6. Remarkably, the nylon 6 with the gem -dimethyls substituted at the γ position is amorphous and optically transparent, with a higher T g (by 30 °C), yield stress (by 1.5 MPa), ductility (by 3×), and lower depolymerization temperature (by 60 °C) than conventional nylon 6.

Retrobiosynthesis tools harness the inherent promiscuities of enzymes for the de novo design of novel biosynthetic pathways to key small molecules. Many existing pathway search algorithms rely on exhaustively enumerating the space of all possible enzymatic reactions using generalized rules, followed by an extensive analysis of the ensuing reaction network to extract candidate pathways for experimental validation. While this approach is comprehensive, many false positive reactions are often generated given the permissiveness of such reaction rules. Here, we have developed DORA-XGB, a enzymatic reaction feasibility classifier. DORA-XGB can be used within our DORAnet framework to assess whether newly enumerated enzymatic reactions and pathways would be feasible. To curate a training dataset for our model, we extracted enzymatic reactions from public databases and screened them for their general thermodynamic feasibility. We then considered alternate reaction centers on known substrates to strategically generate infeasible reactions with high confidence, thereby circumventing the lack of negative data in the literature. In training our model, we also experimented with various molecular fingerprinting techniques and configurations for assembling reaction fingerprints, taking into account not just primary substrate and primary product structures, but cofactor structures as well. Our model's utility is demonstrated through favorable benchmarking against a previously published classifier, the successful recovery of newly published reactions, and the ranking of previously predicted pathways for the biosynthesis of propionic acid from pyruvate.

Synthetic biology offers the promise of manufacturing chemicals more sustainably than petrochemistry. Yet, both the rate at which biomanufacturing can synthesize these molecules and the net chemical accessible space are limited by existing pathway discovery methods which rely on arduous literature searches. Here, we present an automated retrobiosynthesis tool, Biosynth Pipeline, that simultaneously tackles both problems by integrating multifunctional type I polyketide synthases (PKSs) with monofunctional enzymes to propose the synthesis of desired target chemicals via two new tools: DORAnet and RetroTide. While monofunctional enzymes are valuable for carefully decorating a substrate's carbon backbone, they typically cannot expand the backbone itself. PKSs can, instead, predictably do this through their unique ability to catalyze carbon-carbon bond formation reactions iteratively. We evaluated the performance of Biosynth Pipeline against a previously published set of 155 molecules of interest for biomanufacturing, and report that Biosynth Pipeline could produce exact designs for 93 of them, as well as pipelines to a chemically similar product for most of the remaining molecules. Furthermore, Biosynth Pipeline successfully proposes biosynthetic routes for complex therapeutic natural products (cryptofolione and basidalin) for which no known biosynthetic pathway currently exists. ### Competing Interest Statement J.D.K. has financial interests in Amyris, Ansa Biotechnologies, Apertor Pharma, Berkeley Yeast, Cyklos Materials, Demetrix, Lygos, Napigen, ResVita Bio and Zero Acre Farms. The other authors declare no competing interests.

This is the approved Final Technical Report for DOE Award No. DE-EE0008492. In this work, two technology areas were advanced: a) novel molecules with improved performance in the end-use application of organic corrosion inhibitors and flame retardant nylon polymers, and b) development of a systematic process for identifying biomass-derived molecules with improved performance in end-use applications.