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At least 973 records · Page 54

Data-Centric Development of Lignin Structure–Solubility Relationships in Deep Eutectic Solvents Using Molecular Simulations

Lignin is a natural source of aromatic chemicals with significant potential as an abundant, renewable feedstock for value-added products. Deep eutectic solvents (DES)–solvents composed of a hydrogen bond donor (HBD) and acceptor (HBA) in varying ratios–have emerged as a highly tunable class of solvents for lignin solubilization. However, the variety of possible DES compositions and limited molecular-scale understanding of lignin solubility makes solvent selection a challenge without laborious trial-and-error experimentation. To address these challenges, we use classical molecular dynamics (MD) simulations to study the interactions of lignin model compounds with various DES–water systems. Quantitative parameters (descriptors) were calculated by postprocessing the MD results and used to train a regression model that predicts experimentally determined solubilities of lignin model compounds. This approach revealed that the most important descriptors of solubility are the system temperature, solute hydrophilicity, and metrics quantifying hydrogen bonding. Maximizing the interactions between solute–HBD (hydrophobic group), water–HBD (hydrophilic group), and water–HBA molecules led to the highest model compound solubility. Our results support a hydrotropic mechanism in which extensive DES–water hydrogen bonding and favorable HBD interactions with the solute promote high solubility. We applied the regression model derived using model compounds to predict the solubility of representative lignin oligomers. The model predicted lignin oligomers’ solubilities in good agreement with experiments, indicating that the simulations of model compounds can be extended to predict the solubility of larger lignin compounds across a range of solvent compositions and temperatures. Furthermore, these findings provide new molecular-scale insight into lignin solubilization mechanisms and a new method for computationally screening potential solvent systems for lignin valorization.

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Heuristic Computational Model for Predicting Lignin Solubility in Tailored Organic Solvents

Lignin is a random heteropolymer that has been extensively studied as a renewable source of aromatic precursors for high-value chemicals, biofuels, and bioplastics. A key challenge in lignin valorization is the structural and compositional heterogeneity of lignin feedstocks. Solvent-based approaches are commonly used to fractionate lignin to reduce this heterogeneity, but solvent selection can be challenging due to variability in lignin composition. In this work, we developed computational methods to predict good and poor organic solvents as a function of lignin composition. We analyzed 28 different linear pentamer structures, 18 from known libraries and 10 hypothetical polymers, and calculated their activity coefficients in 50 different organic solvents by using the conductor-like screening model for realistic solvents. We used these data to train a regression model that enabled the extensive investigation of the impact of solvent and monolignol compositions on predicted lignin solubility. The exhaustive exploration of solubility trends using model predictions revealed sets of solvents, identified using Kamlet–Taft parameters, that are predicted to promote lignin dissolution regardless of lignin composition. We further identified solvents expected to selectively isolate lignin fractions enriched in certain subunits. Furthermore, these results establish heuristic guidelines for solvent selection that can be used to tailor fractionation processes for lignin feedstocks of distinct composition or to design new processes that isolate fractions with higher proportions of selected subunits.

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A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

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Lignin-Derived Methoxyterephthalates for Performance-Advantaged Polymers and Plasticizers

Lignin-derived aromatic carboxylic acids can be produced from oxidative catalytic processes and are promising building blocks for performance-advantaged bioproducts that leverage their inherent heteroatom functionalities. Here, we synthesize 2-methoxyterephthalate and 2,6-dimethoxyterephthalate derivatives by electrochemical carboxylation of guaiacyl- and syringyl-derived lignin monomers obtained from the oxidative deconstruction of lignin. These methoxylated terephthalates are evaluated as comonomers in poly(ethylene terephthalate) (PET) and as plasticizers that could replace petrochemically-derived isophthalate and phthalate, respectively. Specifically, we copolymerize 2-methoxy- and 2,6-dimethoxyterephthalate with dimethyl terephthalate to form several PET copolymers, both of which enable the properties of PET to be tuned, with an incorporation beyond 25% producing amorphous polyesters. At 10 mol % loading in the copolymers, we demonstrate that the bioderived comonomers exhibit comparable behavior to isophthalic acid, a commonly used comonomer in PET, by lowering the crystallinity and melting temperature. Moreover, methoxyterephthalate esters (2-ethylhexyl and butyl) are compared to phthalate and terephthalate ester counterparts used as poly(vinyl chloride) (PVC) plasticizers. The bioderived plasticizers are comparable to the petroleum-derived incumbents in reducing the glass transition temperature and increasing the thermal stability of PVC. Furthermore, the dimethoxyterephthalic esters are expected to have an extended lifetime in the polymer matrix due to their lower volatility and lower diffusion coefficients calculated by molecular dynamic simulations. These results demonstrate that the isophthalate and phthalate components in polyesters and plasticizers, respectively, could be substituted with biobased methoxyterephthalate derivatives.

09 BIOMASS FUELS↗

Investigating Lignin Aggregation and Interactions with Solvents during γ-Valerolactone (GVL) Pretreatment: A Combined Small Angle Neutron Scattering and Molecular Simulations Study

The strong tendency of lignin to aggregate in solution, coupled with limited understanding of how its molecular structure governs this behavior, hinders its effective utilization in biorefineries. Here, in this study, we investigated the solution behavior of lignin extracted from poplar using γ-valerolactone/water (GVL/H2O, 9:1 wt/wt) through combined small-angle neutron scattering (SANS) and molecular dynamics (MD) simulations. Lignin samples obtained at 100 °C (L100) and 120 °C (L120) differed in β–O–4 content, hydroxyl distribution, and S/G ratio, enabling direct assessment of how molecular composition governs solvation and aggregation. SANS showed that L120 formed rigid and elongated cylindrical aggregates at 25 °C that transitioned to more flexible spheroidal structures by 50 °C and remained stable up to 80 °C, whereas L100 adopted globular aggregates that progressively collapsed with increasing temperature. MD simulations reinforced these observations by showing that S-rich (L120-like) oligomers had larger radii of gyration, stronger solvent coordination driven by methoxy groups, and fewer lignin–lignin contacts. In contrast, G-rich (L100-like) oligomers displayed persistent aggregation and lower solubility. Collectively, these results reveal that increased aromatic methoxylation enhances lignin–solvent interactions and suppresses self-association, whereas reduced methoxylation and higher β–O–4 content promote persistent aggregation into colloid-like structures with restricted solvent penetration into the aggregate interior.

biorefinery pretreatment↗

Evaluating the Transport Mechanism for Lignocellulosic Inhibitors in Zymomonas mobilis

The conversion of lignocellulosic biomass to biofuels and bioproducts is limited by small inhibitory molecules generated during the pretreatment process. The diverse compounds within the cell wall hydrolysate including aldehydes, carboxylic acids, phenolics, and alcohols inhibit microbial growth by disrupting microbial membranes. Although Zymomonas mobilis combines high ethanol productivity with hopanoid-rich membranes that confer solvent tolerance, hydrolysate compounds still inhibit microbial growth. One proposed mitigation strategy is to pump these molecules out through active transport processes, reducing direct toxicity. However, if passive permeation rates are high, active transport would create a futile cycle where the exported molecules diffuse back in, creating a net drag on fitness. To investigate this hypothesis, we measure the passive permeation for thirty-three lignocellulose derived compounds using atomistic molecular dynamics simulations with enhanced sampling. Permeability coefficients determined spanned more than eight orders of magnitude driven largely by the balance between hydrophobicity and polarity. Hydrophobic inhibitors such as aromatic acids, aldehydes, and short chain alcohols readily partitioned into the membrane and showed high permeability consistent with rapid passive influx and strong toxicity. Most polar molecules have lower but still appreciable permeation rates, with only the most polar sugars having low permeabilities. By integrating these predictions together with hydrolysate composition information, we find that most of these molecules are so permeable that futile cycles where passive permeation overwhelms efflux is highly likely. These findings provide a predictive framework for strain engineering, highlighting that, for highly permeable inhibitors, reducing extracellular inhibitor concentrations through hydrolysate detoxification, pretreatment optimization, or dilution may be necessary to mitigate futile cycling and improve microbial productivity in industrial biorefineries.

Zymomonas mobilis↗

Isotopic Studies for Tracking Biogenic Carbon during Co-processing of Biomass and Vacuum Gas Oil

Co-processing bio-oils with petroleum-derived feeds in the existing multitrillion dollar refining and distribution infrastructure is an attractive option for introducing renewable energy into the fuels marketplace. Considerable research on co-processing bio-oils and vacuum gas oil (VGO) in fluid catalytic cracking units (FCC) using equilibrium catalysts (E-Cats) demonstrated that biomass-derived molecules decreased activity of and increased carbon laydown on E-Cat. In this work, 13 C-labeled biomass was co-processed with VGO using either an E-Cat or a proprietary zeolite Johnson Matthey catalyst (CP758), which is optimzed for upgrading biomass vapors in a riser reactor, to evaluate the fate of biogenic carbon in FCC products. The results from both catalysts indicate that biogenic carbon was incorporated into alkenes and aromatic hydrocarbons. While no biogenic carbon was found in linear alkanes, it was observed in cycloalkanes during experiments with E-Cat but not with CP758. All produced CO2 was biogenic as was most of the carbon laydown on the catalysts. Several partially deoxygenated biomass compounds, such as alkylphenols and benzofurans, unexpectedly contained small amounts of carbon from VGO. Although this study was conducted with labeled biomass and not bio-oil, it provides insights to reaction mechanisms for co-processing bio-oil and VGO in an FCC, which is required to develop catalysts that efficiently convert bio-oil components during FCC co-processing. This work also demonstrates that comprehensive catalyst development utilizing both biomass- and fossil-derived feeds is required to maximize the incorporation of biogenic carbon into transportation fuels.

09 BIOMASS FUELS↗

Monolignol Benzoates Incorporate into the Lignin of Transgenic Populus trichocarpa Depleted in C3H and C4H

Lignin, an abundant renewable aromatic biopolymer, is an essential macromolecule in terrestrial vascular plants. Genetic modifications affecting monolignol biosynthesis, which produces lignin monomers, alter the metabolic flux through the pathway and are becoming increasingly explored for improving the lignin and biomass quality and for producing high-value commodity chemical feedstocks. Benzoate (BA) conjugates are important metabolites in plants that have not been adequately characterized as components of the lignin structure. In addition to finding trace levels of BA conjugates in wild-type (WT) Populus trichocarpa, these become significantly augmented in strategic triple transgenics. Genes for three key cytochrome P450 enzymes, two 4-hydroxylases (PtrC4H1 and PtrC4H2), and one 3-hydroxylase (PtrC3H3), were downregulated to produce the transgenic plants in which monolignol benzoate (ML-BA) conjugates were incorporated into their lignin at a level some 16-fold higher than in WT. The co-downregulation of PtrC4H1/PtrC4H2/PtrC3H3 genes may cause a rerouting or modification of the pathway due to the interaction of the benzoate biosynthetic pathway with the conventional pathway for monolignol biosynthesis. The total lignin content in the transgenics was decreased by 50%, and the lignin H-unit level was elevated some 70-fold. The wood from the transgenic trees showed reduced recalcitrance toward enzymatic saccharification, which has value for the conversion of plant biomass to bioenergy.

2D NMR↗

Performance of Base and Noble Metals for Electrocatalytic Hydrogenation of Bio-Oil-Derived Oxygenated Compounds

Electrocatalytic hydrogenation is a particularly attractive approach for converting the most unstable compounds in biogenic feedstocks at ambient conditions without external hydrogen. Here, we synthesized a variety of carbon-supported transition metal catalysts and characterized their activity for the electrocatalytic hydrogenation of a series of model compounds and pyrolysis oil. Carbonyl compounds, especially aromatic aldehydes, such as furfural and benzaldehyde, are particularly inclined to hydrogenation driven by an applied current. This was verified with pure solutions of the model compounds and with pyrolysis oil, where we achieved stable and steady continuous operation on Pd. By choosing the optimal catalyst composition, the conversion of benzaldehyde shifted from alcohol production (e.g., on Pd and Cu) to dimerization (e.g., on Co, Ni, and Zn). Pd and Cu were shown to offer the best compromise between reaction rates and efficiency although, in general, base metals offer similar conversions but better efficiencies that noble metals. Thus, the present work offers foundational results and guidelines for choosing the optimal metal catalyst and the applied potential for processing organic feedstocks as a function of its composition.

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Carbon Negative Synthesis of Amino Acids Using a Cell-Free-Based Biocatalyst

Biological systems can directly upgrade carbon dioxide (CO 2 ) into chemicals. The CO 2 fixation rate of autotrophic organisms, however, is too slow for industrial utility, and the breadth of engineered metabolic pathways for the synthesis of value-added chemicals is too limited. Biotechnology workhorse organisms with extensively engineered metabolic pathways have recently been engineered for CO 2 fixation. Yet, their low carbon fixation rate, compounded by the fact that living organisms split their carbon between cell growth and chemical synthesis, has led to only cell growth with no chemical synthesis achieved to date. Here, we engineer a lysate-based cell-free expression (CFE)-based multienzyme biocatalyst for the carbon negative synthesis of the industrially relevant amino acids glycine and serine from CO 2 equivalents–formate and bicarbonate–and ammonia. The formate-to-serine biocatalyst leverages tetrahydrofolate (THF)-dependent formate fixation, reductive glycine synthesis, serine synthesis, and phosphite dehydrogenase-dependent NAD(P)H regeneration to convert 30% of formate into serine and glycine, surpassing the previous 22% conversion using a purified enzyme system. We find that (1) the CFE-based biocatalyst is active even after 200-fold dilution, enabling higher substrate loading and product synthesis without incurring additional cell lysate cost, (2) NAD(P)H regeneration is pivotal to driving forward reactions close to thermodynamic equilibrium, (3) balancing the ratio of the formate-to-serine pathway genes added to the CFE is key to improving amino acid synthesis, and (4) efficient THF recycling enables lowering the loading of this cofactor, reducing the cost of the CFE-based biocatalyst. To our knowledge, this is the first synthesis of amino acids that can capture CO 2 equivalents for the carbon negative synthesis of amino acids using a CFE-based biocatalyst. Looking ahead, the CFE-based biocatalyst process could be extended beyond serine to pyruvate, a key intermediate, to access a variety of chemicals from aromatics and terpenes to alcohols and polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multiple Mechanisms Mapped in Aryl Alkyl Ether Cleavage via Aqueous Electrocatalytic Hydrogenation over Skeletal Nickel

We present here detailed mechanistic studies of electrocatalytic hydrogenation (ECH) in aqueous solution over skeletal nickel cathodes to probe the various paths of reductive catalytic C–O bond cleavage among functionalized aryl ethers relevant to energy science. Heterogeneous catalytic hydrogenolysis of aryl ethers is important both in hydrodeoxygenation of fossil fuels and in upgrading of lignin from biomass. The presence or absence of simple functionalities such as carbonyl, hydroxyl, methyl, or methoxyl groups is known to cause dramatic shifts in reactivity and cleavage selectivity between sp3 C–O and sp2 C–O bonds. Specifically, reported hydrogenolysis studies with Ni and other catalysts have hinted at different cleavage mechanisms for the C–O ether bonds in α-keto and α-hydroxy β-O-4 type aryl ether linkages of lignin. Our new rate, selectivity, and isotopic labeling results from ECH reactions confirm that these aryl ethers undergo C–O cleavage via distinct paths. For the simple 2-phenoxy-1-phenylethane or its alcohol congener, 2-phenoxy-1-phenylethanol, the benzylic site is activated via Ni C–H insertion, followed by beta elimination of the phenoxide leaving group. But in the case of the ketone, 2-phenoxyacetophenone, the polarized carbonyl π system apparently binds directly with the electron rich Ni cathode surface without breaking the aromaticity of the neighboring phenyl ring, leading to rapid cleavage. Substituent steric and electronic perturbations across a broad range of β-O-4 type ethers create a hierarchy of cleavage rates that supports these mechanistic ideas while offering guidance to allow rational design of the catalytic method. Furthermore, on the basis of the new insights, the usage of cosolvent acetone is shown to enable control of product selectivity.

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Chirality Amplified: Long, Discrete Helicene Nanoribbons

Here we report the synthesis of two polyhelicene frameworks consisting, from end-to-end, of 18 and 24 fused benzene rings. The latter exhibits the largest electronic circular dichroism in the visible spectrum of any molecule. These shape-persistent helical nanoribbons incorporate multiple helicenes, a class of contorted polycyclic aromatic molecules consisting of ortho-annulated rings. These conjugated, chiral molecules have interesting chemical, biological, and chiroptical properties; however, there are very few helicenes with extraordinary chiroptical response over a broad range of the visible spectrum—a key criterion for applications such as chiral optoelectronics. In this report, we show that coupling the polyhelicene framework with multiple perylene-diimide subunits elicits a significant chiroptic response. Notably, the molar circular dichroism increases faster than the absorptivity of these molecules as their helical axis lengthens. Computational analysis reveals that the greatly amplified circular dichroism arises from exciton-like interactions between the perylene-diimide and the helicene moieties. As a result, we predict that even greater chiroptic enhancement will result from further axial elongation of these nanoribbons, which can be readily enabled via the iterative synthetic method presented herein.

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A Tunable Multivariate Metal–Organic Framework as a Platform for Designing Photocatalysts

Catalysts for photochemical reactions underlie many foundations in our lives, from natural light harvesting to modern energy storage and conversion, including processes such as water photolysis by TiO 2 . Recently, metal–organic frameworks (MOFs) have attracted large interest within the chemical research community, as their structural variety and tunability yield advantages in designing photocatalysts to address energy and environmental challenges. Here, we report a series of novel multivariate metal–organic frameworks (MTV-MOFs), denoted as MTV-MIL-100. They are constructed by linking aromatic carboxylates and AB 2 OX 3 bimetallic clusters, which have ordered atomic arrangements. Synthesized through a solvent-assisted approach, these ordered and multivariate metal clusters offer an opportunity to enhance and fine-tune the electronic structures of the crystalline materials. Moreover, mass transport is improved by taking advantage of the high porosity of the MOF structure. Combining these key advantages, MTV-MIL-100(Ti,Co) exhibits a high photoactivity with a turnover frequency of 113.7 mol H2 gcat. –1 min –1 , a quantum efficiency of 4.25%, and a space time yield of 4.96 × 10 –5 in the photocatalytic hydrolysis of ammonia borane. Bridging the fields of perovskites and MOFs, this work provides a novel platform for the design of highly active photocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Solid-State Pathway Control via Reaction-Directing Heteroatoms: Ordered Pyridazine Nanothreads through Selective Cycloaddition

Nanothreads are one-dimensional nanomaterials comprised of a primarily sp 3 hydrocarbon backbone. They are typically formed through cycloaddition reactions induced by compression of small molecules to high pressures. Although nanothreads have been synthesized from a range of small molecule precursors (e.g., benzene, pyridine, aniline), the ability to control reaction pathways to produce atomically precise nanothreads with distinct chemical structures remains a difficult challenge. In this study, we show how heteroatoms incorporated within starting precursors can serve as “thread directing” groups by selecting for specific cycloaddition reaction pathways. By using a less-reactive diazine group within a six-membered aromatic ring, we successfully predict and synthesize the first carbon nanothread material derived from pyridazine (1,2-diazine, C 4 H 4 N 2 ). Compared with previous nanothreads, the synthesized polypyridazine, shows a predominantly uniform chemical structure with exceptional long-range order, allowing for structural characterization using vibrational spectroscopy and X-ray diffraction. The results demonstrate how thread-directing groups can be used for precise reaction pathway control and the formation of chemically precise nanothreads with a high degree of structural order.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Bioenabled Platform to Access Polyamides with Built-In Target Properties

The diversification of platform chemicals is key to today’s petroleum industry. Likewise, the flourishing of tomorrow’s biorefineries will rely on molecules with next-generation properties from biomass. Herein, we explore this opportunity with a novel approach to monomers with custom property enhancements. Cyclic diacids with alkyl and aromatic decorations were synthesized from muconic acid by Diels-Alder cycloaddition and copolymerized with hexamethylenediamine and adipic acid to yield polyamides with built-in hydrophobicity and flame retardancy. Testing shows a 70% reduction in water uptake and doubling of char production while largely retaining other key properties of the parent Nylon-6,6. In conclusion, the present approach can be generalized to access a wide range of performance-advantaged polyamides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Highly Active and Selective Sites for Propane Dehydrogenation in Zeolite Ga-BEA

A highly selective Ga-modified zeolite BEA for propane dehydrogenation has been synthesized by grafting Ga on Zn-BEA followed by removal of Zn in the presence of H 2 . A propene selectivity of 82% at 19% propane conversion illustrates the high selectivity at 813 K. The kinetic model of the catalyzed dehydrogenation including the elementary steps of propane adsorption, first and second C–H bond cleavage, and propene and H 2 desorption demonstrates that the propane dehydrogenation rate is determined by the first C–H bond cleavage at low pC 3 H 8 , while at high pC 3 H 8 , the rate is limited by the desorption of H 2 . The active sites have been identified as dehydrated and tetrahedrally coordinated Ga 3+ in the *BEA lattice. Furthermore, the low selectivity toward aromatics is concluded to be associated with the high Lewis acid strength of lattice Ga 3+ and the low Brønsted acid strength of the hydrated Ga sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Remote Hydroamination of Disubstituted Alkenes by a Combination of Isomerization and Regioselective N–H Addition

Remote hydrofunctionalizations of alkenes incorporate functional groups distal to existing carbon–carbon double bonds. While remote carbonylations are well-known, remote hydrofunctionalizations are most common for addition of relatively nonpolar B–H, Si–H, and C–H bonds with alkenes. We report a system for the remote hydroamination of disubstituted alkenes to functionalize an alkyl chain selectively at the subterminal, unactivated, methylene position. Critical to the high regioselectivity and reaction rates are the electronic properties of the substituent on the amine and the development of the ligand DIP-Ad-SEGPHOS by evaluating the steric and electronic effects of ligand modules on reactivity and selectivity. The remote hydroamination is compatible with a broad scope of alkenes and aminopyridines and enables the regioconvergent synthesis of amines from an isomeric mixture of alkenes. In conclusion, the products can be derivatized by nucleophilic aromatic substitution on the amino substituent with a variety of nucleophiles.

Hydrocarbons↗