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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Process intensification of CO 2 capture by low-aqueous solvent

Low-energy solvent-based CO 2 absorption processes have drawn attention as a next-generation post-combustion CO 2 capture technology to reduce CO2 emissions from fossil fuel– or biomass-fired power generation and industrial flue gas streams. A low-aqueous (or water-lean) solvent process may substantially reduce the thermal energy consumption for solvent regeneration. Low-aqueous solvent–based processes are thermally sensitive, requiring a delicate temperature control within the absorber because of the fast exothermic amine-CO 2 reaction and low heat capacity organic diluent. This reaction may result in heat accumulation in a packed absorption column and undesirable CO 2 desorption occurring as the solvent moves through the column, reducing the solvent’s CO 2 capture efficiency if its temperature is not controlled. Using a 3D printed intensified packing device, enhanced heat and mass transfer were demonstrated in an amine-CO 2 scrubbing process using low-aqueous solvent. The multifunctional intensified device facilitates contact of the reactive solvent and gas phases in a single stage and heat removal by a cooling fluid flowing through channels in the interior of the corrugated plates of the device. These functionalities led to effective thermal management along the column via intrastage cooling and significant improvement in CO 2 uptake under a wide range of operating conditions. Intrastage cooling effectively reduced the solvent average temperature along the column by ~10 °C and, as a result, the solvent’s capture efficiency improved by up to 25%.

42 ENGINEERING↗
DOE OSTI2022

Light-Duty Vehicle Transportation Policy and Implication on Greenhouse Gas Emissions

The transportation sector accounts for 16% of global greenhouse gas (GHG) emissions and is under formidable pressure to decarbonize. With a growing number of countries making commitments to achieve carbon neutrality or “net-zero” emissions within the next few decades, it is imperative for transportation researchers and policymakers to understand the viable pathways towards achieving carbon neutrality for light-duty transport. This chapter discusses the transportation policies and GHG emissions of the three largest markets in the world—the U.S., China, and the European Union. The life cycle GHG emissions of various vehicle technologies are evaluated while highlighting the regional and temporal differences. We then use market penetration and fleet models, developed specifically for each market, to comprehensively assess the light-duty transport energy demand and GHG emissions under various scenarios. The modeling results show that battery electric vehicles (BEVs) will increase in market share, but internal combustion engine vehicles (ICEV) will continue to dominate the passenger vehicle stock in the next 20 years under most scenarios. Improving ICEV efficiency can play a critical role in meeting GHG regulations in the near- and medium-term. BEVs, whose GHG emissions are highly dependent on the source of electricity generation, will play an essential role in the long-term as the electric grid becomes cleaner. In summary, transportation policies should be technology agnostic and consider emissions based on the whole life cycle. Moreover, a holistic approach to reducing transportation GHG emissions is key to achieving global environmental goals.

Ou, Shawn↗
DOE OSTI2025

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Carruthers, David N↗
DOE OSTI2021

Impact on Cycle Efficiency of Small Combined Heat and Power Plants from Increasing Firing Temperature Enabled by Additive Manufacturing of Turbine Blades and Vanes

The results of this study show that a 180°F (100°C) increase in firing temperature can increase the gas turbine efficiency by 1 percentage point without improving cooling effectiveness and add 2 additional percentage points in efficiency by using advanced turbine blades with higher internal cooling efficiency. The engine upgrades evaluated in this study show potential for increasing the CHP cycle efficiency by 3 percentage points while increasing the steam generation rate by 8%.

Uysal, Selcuk Can↗
DOE OSTI2022

Powertrain Performance and Total Cost of Ownership Analysis for Class 8 Yard Tractors and Refuse Trucks

Advanced powertrain technologies, specifically fuel cell electric powertrains, have gained attention as viable alternatives for medium- and heavy-duty (M/HD) vehicles. However, it is unclear how these alternative powertrain vehicles stack up against their diesel counterparts in terms of performance and total cost of ownership (TCO). Furthermore, there are vehicle segments within the M/HD sector that have remained unstudied for fuel cell electric applications. This analysis aims to provide a comparative scoping-level TCO and performance analysis for two heavy-duty vocation vehicles (Class 8 U.S. port-side yard tractor and Class 8 U.S.-based refuse truck) for both conventional diesel and heavy-duty fuel cell electric (HDFC) powertrains. The refuse truck analysis also considered compressed natural gas powertrains (CNG) for comparison. The analysis includes seven timeframes (2020, 2025, 2030, 2035, 2040, 2045, and 2050) for comparison. This simplified TCO analysis includes only direct costs (fuel price, glider purchase price, and operating & maintenance costs) and excludes any associated indirect cost (e.g., dwell time costs and other opportunity based costs). Representative drive cycles for each vehicle were based on on-board GPS logged data and chosen by the analysis team to represent average, non-extreme driving conditions. At the time the analysis was performed, the Inflation Reduction Act was not in effect and therefore any potential subsidies and future cost reductions enacted under the Inflation Reduction Act were not included. Based on the operational setpoints used in this analysis, HDFC powertrains for both yard tractors and refuse trucks have the potential to achieve TCO advantages over conventional diesel powertrains (and CNG for refuse truck applications) in the near- to mid-term future while meeting the necessary duty cycle performance requirements. Yard tractors and refuse trucks spend a significant amount of time operating at low speeds, with long durations of idling, and experience numerous start/stop occurrences. These operational characteristics favor fuel cell performance as fuel cells operate with higher efficiencies at lower percentages of total power output. Conversely, conventional diesel and CNG engines are most efficient at higher percentages of total power output. This helps HDFC powered yard tractors and refuse trucks realize improved fuel economy when compared to their diesel counterparts, which helps reduce total fuel costs and therefore, total TCO. The analysis demonstrates that fuel prices play a significant role in determining TCO for each vehicle and should remain an R&D focus area. Overall, under the analysis' specified conditions, HDFC yard tractors have the potential to achieve cost parity with diesel yard tractors as early as 2025. For refuse trucks, HDFC refuse trucks have the potential to achieve cost parity with diesel and CNG refuse trucks in 2030 and 2040, respectively.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Simulation-Based Assessment of Energy Consumption of Alternative Powertrains in Agricultural Tractors

The objectives of this research were to develop simulation models for agricultural tractors with different powertrain technologies and evaluate the energy consumption in typical agricultural operations. Simulation models were developed for conventional, parallel hybrid electric, series hybrid electric, fuel cell hybrid, and battery electric powertrains. Autonomie vehicle simulation software (version 2022) was used for the simulations and the tractor models were simulated in two tilling cycles and in a road transport cycle with a trailer. The alternative powertrains were configured to have at least the same tractive performance as the conventional, diesel engine-powered tractor model. The simulation results showed that the potential of the parallel and series hybrid powertrains to improve energy efficiency depends heavily on the tractor size and the operating cycle conditions. The fuel cell hybrid and battery electric powertrains have a higher potential to reduce energy consumption and emissions but still have inherent technical challenges for practical operation. The battery-powered electric tractor would require improvements in the storage energy density to have a comparable operational performance in comparison to other powertrains. The fuel cell hybrid tractor already provided an adequate operating performance but the availability of hydrogen and refueling infrastructure could be challenging to resolve in the farming context.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Heavy-Duty Nonroad Material Handler Electrification Part 2: Energy Efficiency and Performance Evaluation

Decarbonization efforts achieved through electrification in nonroad mobile machinery can realize a reduction in fuel consumption of more than 20%, thanks to concepts familiar to light-duty passenger vehicles. This case study compares the results of a hybrid-electric material handler to its conventional counterpart, utilizing machine-specific drive cycles presented in part one of this paper series. The hybrid prototype features an extended-range electric vehicle (EREV) powertrain that demonstrated substantial energy efficiency improvements. Specifically, there was a reduction in equivalent fuel consumption of 75% when operating in electric-only mode, and 33% when maintaining the battery by charging with an on-board generator. Together, the efficiency improvements can be extrapolated over a low-intensity, 8-h shift characterized by significant idle time and highly dynamic engine load for a 47% reduction in net energy consumption. Key technologies that led to this improvement included engine downsizing and decoupling, regenerative braking, and an electrohydraulic pump unit with advanced controls. Finally, his study explains details of the powertrain architecture and subsystems that were implemented on a demonstration vehicle, control strategies used to meet project goals, and an analysis of energy consumption from testing on a closed course. Also included in this study is a discourse on comparison metrics that can be used for quantifying the energy consumption differences between hybrid-electric and conventional diesel powertrains in nonroad mobile machinery.

25 ENERGY STORAGE↗
DOE OSTI2021

Impact on Cycle Efficiency of Small CHP Plants from Increasing Firing Temperature Enabled by AM of Turbine Blades

Combined Heat and Power (CHP) systems are gaining popularity because of their potential for high overall thermodynamic efficiency and increased need for distributed power generation. Most CHP systems include a gas turbine for electricity generation and a heat recovery steam generator (HRSG) for steam generation. The steam can be used to power a steam turbine for additional electricity generation or to drive rotating equipment, for space heating, for absorption chillers, etc. A CHP cycle configuration is often driven by the ‘principal’ utility for the facility that it services; steam or electricity. In either case, performance improvements in the gas turbine have the potential to increase the steam and power output from the cycle, which is a direct result of improvements in the gas turbine efficiency and power output. One possible opportunity to improve an existing gas turbine’s performance is to increase the firing temperature with improved turbine cooling and increased compression ratio.<br>In this study, the impact on CHP cycle performance from increasing the turbine firing temperature by 100 °C and improving the turbine blade cooling for a 6-MW scale gas turbine is estimated using an analytical cooled gas turbine model and a steam cycle model. A sensitivity analysis was performed to understand the impact of increasing the internal cooling effectiveness, thermal barrier coating performance and blade material upgrades on gas turbine and CHP cycle efficiency. The impacts of turbine blade cooling improvements were studied for three common CHP cycle configurations identified from the literature. Various definitions for CHP cycle efficiency from the literature are used in the comparisons. The results showed that a 100 °C increase in firing temperature can increase the gas turbine efficiency by 1 percentage point without improving cooling effectiveness and add 2 additional percentage points in efficiency by using advanced turbine blades with higher internal cooling efficiency. Studied engine upgrades showed potential for increasing the CHP cycle efficiency by 3 percentage points while increasing the steam generation rate by 8%.<br>

Uysal, Selcuk Can↗
DOE OSTI2021

Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035

From daily commutes to cross-country road trips, millions of light-duty vehicles are on the road every day. The transportation sector is one of the United States’ largest sources of greenhouse gas emissions, and fuel is an important cost for drivers. The period from 2025-2035 could bring the most fundamental transformation in the 100-plus year history of the automobile. Battery electric vehicle costs are likely to fall and reach parity with internal combustion engine vehicles. New generations of fuel cell vehicles will be produced. Connected and automated vehicle technologies will become more common, including likely deployment of some fully automated vehicles. These new categories of vehicles will for the first time assume a major portion of new vehicle sales, while internal combustion engine vehicles with improved powertrain, design, and aerodynamics will continue to be an important part of new vehicle sales and fuel economy improvement.This study is a technical evaluation of the potential for internal combustion engine, hybrid, battery electric, fuel cell, nonpowertrain, and connected and automated vehicle technologies to contribute to efficiency in 2025-2035. In addition to making findings and recommendations related to technology cost and capabilities, Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy - 2025-2035 considers the impacts of changes in consumer behavior and regulatory regimes.

MARCHANT, GARY↗
DOE OSTI2022

Maritime Fuel Cell Generator Project [FY2018]

Fuel costs and emissions in maritime ports are an opportunity for transportation energy efficiency improvement and emissions reduction efforts. Ocean-going vessels, harbor craft, and cargo handling equipment are still major contributors to air pollution in and around ports. Diesel engine costs continually increase as tighter criteria pollutant regulations come into effect and will continue to do so with expected introduction of carbon emission regulations. Diesel fuel costs will also continue to rise as requirements for cleaner fuels are imposed. Both aspects will increase the cost of diesel-based power generation on the vessel and on shore. Although fuel cells have been used in many successful applications, they have not been technically or commercially validated in the port environment. One opportunity to do so was identified in Honolulu Harbor at the Young Brothers Ltd. wharf. At this facility, barges sail regularly to and from neighboring islands and containerized diesel generators provide power for the reefers while on the dock and on the barge during transport, nearly always at part load. Due to inherent efficiency characteristics of fuel cells and diesel generators, switching to a hydrogen fuel cell power generator was found to have potential emissions and cost savings. Deployment in Hawaii showed the unit needed greater reliability in the start-up sequence, as well as an improved interface to the end-user, thereby presenting opportunities for repairing/upgrading the unit for deployment in another locale. In FY2018, the unit was repaired and upgraded based on the Hawaii experience, and another deployment site was identified for another 6-month deployment of the 100 kW MarFC.

08 HYDROGEN↗
DOE OSTI2020

Tailored PEDOT:PSS hole transport layer for higher performance in perovskite solar cells: Enhancement of electrical and optical properties with improved morphology

Precise control over the charge carrier dynamics throughout the device can result in outstanding performance of perovskite solar cells (PSCs). Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is the most actively studied hole transport material in p-i-n structured PSCs. However, charge transport in the PEDOT:PSS is limited and inefficient because of its low conductivity with the presence of the weak ionic conductor PSS. In addition, morphology of the underlying PEDOT:PSS layer in PSCs plays a crucial role in determining the optoelectronic quality of the active perovskite absorber layer. This work is focused on realization of a non-wetting conductive surface of hole transport layer suitable for the growth of larger perovskite crystalline domains. This is accomplished by employing a facile solvent-engineered (ethylene glycol and methanol) approach resulting in removal of the predominant PSS in PEDOT:PSS. The consequence of acquiring larger perovskite crystalline domains was observed in the charge carrier dynamics studies, with the achievement of higher charge carrier lifetime, lower charge transport time and lower transfer impedance in the solvent-engineered PEDOT:PSS-based PSCs. Use of this solvent-engineered treatment for the fabrication of MAPbI3 PSCs greatly increased the device stability witnessing a power conversion efficiency of 18.18%, which corresponds to ~37% improvement compared to the untreated PEDOT:PSS based devices.

14 SOLAR ENERGY↗
DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Layer-Parallel Training of Deep Residual Neural Networks

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers with a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Finally, using numerical examples from supervised classification, we demonstrate that the new approach achieves a training performance similar to that of traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dynamic Species Reduction for Multi-Cycle CFD Simulations (Final Technical Report)

This project primarily sought to address some of the computational cost concerns of detailed simulations by developing improved methods of handling species transport, and chemical kinetics evaluation in a commercial 3D Computational Fluid Dynamics (CFD) environment. Secondary goals were to apply these techniques to fuels and conditions of interest to better understand the key species and reactions required to adequately capture cycle to cycle coupling. Improved modeling will lead to better understanding of these combustion modes and their dependence on fuel composition, which can then enable more clean and efficient engines, ultimately benefiting the consumer as well as the general public. Two approaches were used to address the computational cost. A “Dynamic Species Reduction” (DSR) procedure was developed to remove chemical species from the simulation during periods when chemical reactions were not expected to be important, particularly during gas exchange when temperatures are low and little fuel remains. This modelling procedure automatically detects relevant species to retain in the simulation domain based on their local concentration, removes species below a specified concentration threshold, and then adjusts the remaining species mass to conserve not only the number of H, C, and O atoms in each cell, but also the relative proportions between species and the heating value of the mixture in every cell. The second procedure was a “Product Directed Remapping” (PDR) updates the algorithm used to group individual computational cells for chemical kinetics evaluation to account for non-uniform temperature distributions and the CO to CO 2 ratio in the cells. The model techniques developed in this work successfully demonstrated computational performance improvements for a range of conditions relevant for Highly Dilute SI and HCCI engine operation. Runtime reductions of 10% were observed for small mechanisms, with further reductions of up to 36% observed for larger mechanisms. Improvements were primarily related to reducing the number of chemical species tracked during the gas exchange process using the Dynamic Species Reduction method. With smaller benefits observed from changes to the kinetics binning and evaluation strategy in the post combustion region using the PDR method. The results of this work show the potential for improved computational runtime for complicated simulations. They can and should be extended to additional conditions and new bio-derived and renewable fuels as they are developed and new kinetic mechanisms become available.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Engineering hierarchical nanochannels in graphene oxide membranes by etching and polydopamine intercalation for highly efficient dye recovery

Graphene oxide (GO) membranes have been extensively investigated for dye removal from wastewater, and they are often reduced or cross-linked to improve their stability, which, however, decreases water permeance. This study demonstrates an effective approach to enhance both durability and water permeance and retain dye rejection by synergistically integrating chemical etching using H 2 O 2 and intercalation with polydopamine (PDA) to create hierarchical nanochannels. The nanopores on the holey GO (HGO) shorten the diffusion path, while the PDA intercalation increases the channel sizes and stabilizes the GO nanosheets. Optimized membranes exhibit water permeance of 70–120 LMH/bar (much higher than state-of-the-art commercial polyamide membranes) and a Congo Red rejection of 98.5%, better than GO membranes reported in the literature. Moreover, a membrane with water permeance of 70 LMH/bar exhibits stable performance in a 3-day continuous crossflow filtration test. The versatile approach reported here may be applied to other two-dimensional materials to create hierarchical nanochannels for desired separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses

Designing a high-quality control is crucial for reliable quantum computation. Among the existing approaches, closed-loop leaning control is an effective choice. Its efficiency depends on the learning algorithm employed, thus deserving algorithmic comparisons for its practical applications. Here we assess three representative learning algorithms, including GRadient Ascent Pulse Engineering (GRAPE), improved Nelder-Mead (NMplus), and Differential Evolution (DE), by searching for high-quality control pulses to prepare the Bell state. We first implement each algorithm experimentally in a nuclear magnetic resonance system and then conduct a numerical study considering the impact of some possible significant experimental uncertainties. The experiments report the successful preparation of the high-fidelity target state by the three algorithms, while NMplus converges fastest, and these results coincide with the numerical simulations when potential uncertainties are negligible. However, under certain significant uncertainties, these algorithms possess distinct performance with respect to their resulting precision and efficiency, and DE shows the best robustness. Finally, this study provides insight to aid in the practical application of different closed-loop learning algorithms in realistic physical scenarios.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Insights on the Effects of Magnetic Forces on the Efficiency of Vibration Energy Harvesting Absorbers in Controlling Dynamical Systems

This study investigates the effects of magnetic constraints on a piezoelectric energy harvesting absorber while simultaneously controlling a primary structure and harnessing energy. An accurate forcing representation of the magnetic force is investigated and developed. A reduced-order model is derived using the Euler–Lagrange principle, and the impact of the magnetic force is evaluated on the absorber’s static position and coupled natural frequency of the energy harvesting absorber and the coupled primary absorber system. The results show that attractive magnet configurations cannot improve the system substantially before pull-in occurs. A rigorous eigenvalue problem analysis is performed on the absorber’s substrate thickness and tip mass to effectively design an energy harvesting absorber for multiple initial gap sizes for the repulsive configurations. Then, the effects of the forcing amplitude on the primary structure absorber are studied and characterized by determining an effective design of the system for a simultaneous reduction in the primary structure’s motion and improvement in the harvester’s efficiency.

42 ENGINEERING↗
DOE OSTI2021

Redundancy in Aromatic O -Demethylation and Ring-Opening Reactions in Novosphingobium aromaticivorans and Their Impact in the Metabolism of Plant-Derived Phenolics

ABSTRACT Lignin is a plant heteropolymer composed of phenolic subunits. Because of its heterogeneity and recalcitrance, the development of efficient methods for its valorization still remains an open challenge. One approach to utilize lignin is its chemical deconstruction into mixtures of monomeric phenolic compounds, followed by biological funneling into a single product. Novosphingobium aromaticivorans DSM 12444 has been previously engineered to produce 2-pyrone-4,6-dicarboxylic acid (PDC) from depolymerized lignin by simultaneously metabolizing multiple aromatics through convergent routes involving the intermediates 3-methoxygallic acid (3-MGA) and protocatechuic acid (PCA). We investigated enzymes predicted to be responsible for O -demethylation and oxidative aromatic ring opening, two critical reactions involved in the metabolism of phenolic compounds by N. aromaticivorans . The results showed the involvement of DesA in O -demethylation of syringic and vanillic acids, LigM in O- demethylation of vanillic acid and 3-MGA, and a new O- demethylase, DmtS, in the conversion of 3-MGA into gallic acid (GA). In addition, we found that LigAB was the main aromatic ring-opening dioxygenase involved in 3-MGA, PCA, and GA metabolism and that a previously uncharacterized dioxygenase, LigAB2, had high activity with GA. Our results indicate a metabolic route not previously identified in N. aromaticivorans that involves O -demethylation of 3-MGA to GA. We predict that this pathway channels ∼15% of the carbon flow from syringic acid, with the rest following ring opening of 3-MGA. The new knowledge obtained in this study allowed for the creation of an improved engineered strain for the funneling of aromatic compounds that exhibits stoichiometric conversion of syringic acid into PDC. IMPORTANCE For lignocellulosic biorefineries to effectively contribute to reduction of fossil fuel use, they need to become efficient at producing chemicals from all major components of plant biomass. Making products from lignin will require engineering microorganisms to funnel multiple phenolic compounds to the chemicals of interest, and N. aromaticivorans is a promising chassis for this technology. The ability of N. aromaticivorans to efficiently and simultaneously degrade many phenolic compounds may be linked to having functionally redundant aromatic degradation pathways and enzymes with broad substrate specificity. A detailed knowledge of aromatic degradation pathways is thus essential to identify genetic engineering targets to maximize product yields. Furthermore, knowledge of enzyme substrate specificity is critical to redirect flow of carbon to desired pathways. This study described an uncharacterized pathway in N. aromaticivorans and the enzymes that participate in this pathway, allowing the engineering of an improved strain for production of PDC from lignin.

Biotechnology & Applied Microbiology↗