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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Crystal Structure and Preparation of Li7La3Zr2O12 (LLZO) Solid-State Electrolyte and Doping Impacts on the Conductivity: An Overview

As an essential part of solid-state lithium-ion batteries, solid electrolytes are receiving increasing interest. Among all solid electrolytes, garnet-type Li7La3Zr2O12 (LLZO) has proven to be one of the most promising electrolytes because of its high ionic conductivity at room temperature, low activation energy, good chemical and electrochemical stability, and wide potential window. Since the first report of LLZO, extensive research has been done in both experimental investigations and theoretical simulations aiming to improve its performance and make LLZO a feasible solid electrolyte. These include developing different methods for the synthesis of LLZO, using different crucibles and different sintering temperatures to stabilize the crystal structure, and adopting different methods of cation doping to achieve more stable LLZO with a higher ionic conductivity and lower activation energy. It also includes intensive efforts made to reveal the mechanism of Li ion movement and understand its determination of the ionic conductivity of the material through molecular dynamic simulations. Nonetheless, more insightful study is expected in order to obtain LLZO with a higher ionic conductivity at room temperature and further improve chemical and electrochemical stability, while optimal multiple doping is thought to be a feasible and promising route. This review summarizes recent progress in the investigations of crystal structure and preparation of LLZO, and the impacts of doping on the lithium ionic conductivity of LLZO.

Raju, Md Mozammal (ORCID:0000000319040523)↗
DOE OSTI2023

Solar–Driven CO 2 Conversion via Optimized Photothermal Catalysis in a Lotus Pod Structure

Photothermal CO 2 reduction is one of the most promising routes to efficiently utilize solar energy for fuel production at high rates. However, this reaction is currently limited by underdeveloped catalysts with low photothermal conversion efficiency, insufficient exposure of active sites, low active material loading, and high material cost. Herein, we report a potassium-modified carbon-supported cobalt (K + –Co–C) catalyst mimicking the structure of a lotus pod that addresses these challenges. As a result of the designed lotus-pod structure which features an efficient photothermal C substrate with hierarchical pores, an intimate Co/C interface with covalent bonding, and exposed Co catalytic sites with optimized CO binding strength, the K + –Co–C catalyst shows a record-high photothermal CO 2 hydrogenation rate of 758 mmol gcat –1 h –1 (2871 mmol gCo –1 h –1 ) with a 99.8 % selectivity for CO, three orders of magnitude higher than typical photochemical CO 2 reduction reactions. Here, we further demonstrate with this catalyst effective CO 2 conversion under natural sunlight one hour before sunset during the winter season, putting forward an important step towards practical solar fuel production.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Fuel Efficient Speed Optimization for Real-World Highway Cruising

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states. The proposed algorithm assumes piecewise constant slopes and speed limits, and it solves the transition vehicle speeds for each junction of such segments. In addition, a method to extract route information from digital maps is presented, which complies with the proposed optimization algorithm. The algorithms are evaluated in simulation, using Autonomie, a simulation tool for vehicle energy consumption modeling. The optimal control trajectory (e.g., torque, gear) is first computed offline. It is then applied in an open-loop fashion in online simulation, using a forward-looking model of the vehicle in Autonomie. Simulation results show over 8% fuel saving for two selected real-world routes.

Simulation and modeling, Optimization, Vehicle net↗
DOE OSTI2023

Solar‐Driven CO 2 Conversion via Optimized Photothermal Catalysis in a Lotus Pod Structure

Abstract Photothermal CO 2 reduction is one of the most promising routes to efficiently utilize solar energy for fuel production at high rates. However, this reaction is currently limited by underdeveloped catalysts with low photothermal conversion efficiency, insufficient exposure of active sites, low active material loading, and high material cost. Herein, we report a potassium‐modified carbon‐supported cobalt (K + −Co−C) catalyst mimicking the structure of a lotus pod that addresses these challenges. As a result of the designed lotus‐pod structure which features an efficient photothermal C substrate with hierarchical pores, an intimate Co/C interface with covalent bonding, and exposed Co catalytic sites with optimized CO binding strength, the K + −Co−C catalyst shows a record‐high photothermal CO 2 hydrogenation rate of 758 mmol g cat −1 h −1 (2871 mmol g Co −1 h −1 ) with a 99.8 % selectivity for CO, three orders of magnitude higher than typical photochemical CO 2 reduction reactions. We further demonstrate with this catalyst effective CO 2 conversion under natural sunlight one hour before sunset during the winter season, putting forward an important step towards practical solar fuel production.

Wang, Hongmin↗
DOE OSTI2018

Effect of annealing after strip casting on microstructure, precipitates and texture in non-oriented silicon steel produced by twin-roll strip casting

Highlights: • Non-oriented silicon steel with superior magnetic properties was produced by twin-roll strip casting. • The effect of annealing after strip casting on microstructure, precipitates and texture was investigated. • Role of precipitates in microstructure and texture evolution along the entire route was explored. • High magnetic induction and strong λ-fiber texture was obtained by optimizing strip annealing process. - Abstract: A 3.1 mm thick non-oriented Fe-1.0% Si steel as-cast strip was produced by twin-roll strip casting. Samples with/without strip annealing were cold rolled to 0.5 mm and then annealed. The development of microstructure, precipitate and texture was characterized along the entire processing route. It was found that annealing after strip casting had a minor influence on the average grain size and texture of the as-cast strip. A large number of particles precipitated and coarsened during the strip annealing process. Meanwhile, precipitates hindered dislocation slip and crystal rotation such that more significant in-grain shear bands were formed in cold rolled sheets. During recrystallization annealing process, the newly formed fine precipitates in directly cold-rolled sheets consumed stored strain energy and also hindered grain growth. But the initial precipitates in the annealed strip grew further and the effect of pinning of dislocations and grain boundary was lost. Therefore, annealed sheets with strip annealing process showed homogeneous equiaxed grains and the recrystallization texture was characterized by strong λ-fiber texture, which led to high permeability.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Joint optimal scheduling for electric vehicle battery swapping-charging system based on wind farms

Insufficiencies in charging facilities limit the broad application of electric vehicles (EVs). In addition, EV can hardly represent a green option if its electricity primarily depends on fossil energy. Considering these two problems, this paper studies a battery swapping-charging system based on wind farms (hereinafter referred to as W-BSCS). In a W-BSCS, the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station (CCS), which can centrally charge EV batteries and then distribute them to multiple battery swapping stations (BSSs). The operational framework of the W-BSCS is analyzed, and some preprocessing technologies are developed to reduce complexity in modeling. Then, a joint optimal scheduling model involving a wind power generation plan, battery swapping demand, battery charging and discharging, and a vehicle routing problem (VRP) is established. Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem. Numerical results verify the effectiveness of the joint optimal scheduling model, and they also show that the W-BSCS has great potential to promote EVs and wind power.

17 WIND ENERGY↗
DOE OSTI2021

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

drone battery swapping↗
DOE OSTI2020

Leveraging Sequential Doping of Semiconducting Polymers to Enable Functionally Graded Materials for Organic Thermoelectrics

With the ability to modulate electronic properties through molecular doping coupled with ease in processability, semiconducting polymers are at the forefront in enabling organic thermoelectric devices for thermal energy management. In contrast to uniform thermoelectric material properties, an alternative route focuses on functionally graded materials (FGMs) where one spatially controls and optimizes transport properties across the length of a thermoelectric material. While primarily studied in the context of inorganic materials, the concept of FGMs for organic thermoelectrics has not been explored. Herein, we introduce how molecular doping of semiconducting polymers enables spatial compositional control of thin-film FGMs. Specifically, we utilize sequential vapor doping of poly[2,5-bis(3-tetradecylthiophen-2-yl) thieno [3,2-b]thiophene] (PBTTT) with the small molecule acceptor 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) to fabricate the simplest form of FGMs – double- segmented thin films. The two thin-film segments are of equal length (7.5 mm), but each set to different doping levels. Our study focuses on understanding the thermoelectric properties (Seebeck coefficient, α, and electronic conductivity, σ) and structural properties (through X-ray scattering, UV-vis-NIR spectroscopy, and Raman spectroscopy) within and across the two segments. We observe the presence of a small diffuse interfacial region of 0.5 – 1 mm between the two segments where the doping level and transport properties vary continuously. Despite the diffuse interface, the measured effective Seebeck coefficient (αeff) across the two segments is simply the average of α within each segment. Importantly, this experimental result is consistent with reported mathematical models describing αeff in graded thermoelectric materials. Finally, our results demonstrate the facile fabrication and characterization of functionally graded organic thermoelectric materials, providing guidelines for further development on more complex FGMs.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Complete active space second order perturbation theory (CASPT2) study of N({sup 2}D) + H{sub 2}O reaction paths on D{sub 1} and D{sub 0} potential energy surfaces: Direct and roaming pathways

We report reaction paths starting from N({sup 2}D) + H{sub 2}O for doublet spin states, D{sub 0} and D{sub 1}. The potential energy surfaces are explored in an automated fashion using the global reaction route mapping strategy. The critical points and reaction paths have been fully optimized at the complete active space second order perturbation theory level taking all valence electrons in the active space. In addition to direct dissociation pathways that would be dominant, three roaming processes, two roaming dissociation, and one roaming isomerization: (1) H{sub 2}ON → H–O(H)N → H–HON → NO({sup 2}Π) + H{sub 2}, (2) cis-HNOH → HNO–H → H–HNO → NO + H{sub 2}, (3) H{sub 2}NO → H–HNO → HNO–H → trans-HNOH, are confirmed on the D{sub 0} surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Quasiepitaxial Growth of Organic Crystalline Thin-Films

The presence of excitons at room temperature in organic semiconductors distinguish them from traditional semiconductors, providing exceptional opportunities for manipulating energy in a range of structures from LEDs, polariton lasers, transparent photovoltaics, and optical switches. However, control over crystalline order, orientation, and defect formation are crucial to the fabrication and optimization of these excitonic electronics. In this work we have demonstrated new routes and new understanding in the growth of ordered organic thin film crystals and multilayer crystals. These crystals are composed of incommensurate organic semiconductors with sustained azimuthal registry and controlled crystalline orientation grown from the bottom up. By probing a range of molecular pairings with in-situ and real-time diffraction, we have demonstrated that one of the driving forces behind this growth is the requirement of surface energy matching of the lowest energy crystalline planes, which is distinct from the requirements of lattice matching for inorganic epitaxy. We have also demonstrated a new route to non-equilibrium quasiepitaxial growth of crystalline organic multilayers through selective edge nucleation dynamics that could provide new control over molecular orientation in organic quantum wells. This work provides a more universal understanding of the key requirements for growth regimes that have been historically inaccessible and will enable enhanced excitonic devices

36 MATERIALS SCIENCE↗
DOE OSTI2025

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Olson, Daniel↗
DOE OSTI2023

Symmetric Random Butterfly Transform (SRBT) Based Preconditioner

Summary of work using Symmetric Random Butterfly Transformation (SRBT) in conjunction with Incomplete LDL T factorization as a preconditioner for FGMRES solver as a way of solving linear systems arising from interior point methods applied to power system problems. These linear systems have proven difficult to parallelize and this represents a possible route forward.

interior point optimization↗
DOE OSTI2024

Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods

The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats (Integration of Additive Manufacturing and Reinforced Polymer Composite)

Lightweight automotive seats offer multiple benefits to original equipment manufacturers in terms of cost savings from various aspects, including less material usage, more integrated processes, and compliance with Corporate Average Fuel Economy Standards. Original equipment manufacturers have been focusing on innovative ways to produce light weight automotive seats. The commercially available automotive seats are currently made of multiple metal components combined through welding and fasteners. The use of additive manufacturing and composite structures is particularly useful for light weighting the automotive components. Additive manufacturing (AM) offers multiple advantages over traditional manufacturing processes such as freedom of design thereby enabling complex structural geometries, mass customization and waste minimization, and control over the fiber alignment through deposition in a predetermined pattern. Combining metal inserts with polymer composites through a novel manufacturing process allows design of lightweight and high-performance materials for automotive components. However, fabricating these metal polymer composite structures through traditional manufacturing processes limits their mechanical properties due to limited design freedom, lack of control over fiber orientation in composite parts, and poor interfacial bonding between the constituent materials. It is essential to develop a novel manufacturing process to enable high throughput production of lightweight automotive seats using metal and polymer composites. As such it is important to design the automotive seat suitable for manufacturing via this process and perform mechanical characterization on various subcomponents of the seat to ensure that the design and performance requirements provided by the auto manufacturer are met. The aim of this project is to develop a novel manufacturing technique to produce lightweight automotive seat by combining AM with conventional manufacturing processes. The car seat back panel will be designed via topology optimization and numerical simulations to minimize the overall weight while ensuring it meets all the performance requirements. The optimization of the seat back structure will be based on computational stress analysis to maximize the stiffness and minimize the weight. Materials currently used by Ford Motor Company will be adopted for a few subcomponents while the in-house composite materials will be used for the rest of the seat back. The composite and metallic materials will be tested to determine their mechanical properties as these are necessary for simulations. A novel manufacturing process will be developed to integrate AM metal inserts with discontinuous reinforced composite through large scale additive manufacturing and compression overmolding processes. The developed manufacturing technique will be used to fabricated various subcomponents suitable for the seat back design and mechanically tested to determine their properties. The manufacturing of the lightweight seat back design through this process involves integrated AM metal inserts with the composite structure for recliner connection. The manufacturing of the entire seat back which is lightweight through the novel manufacturing process will be discussed. The performance of the designed seat back will be investigated through numerical simulations and shown to meet all the requirements provided by the auto manufacturer. The final goal of developing a novel manufacturing process for lightweight automotive seats is met through design optimization of seat back, manufacturing of subcomponents, mechanical characterization, and validation through numerical simulations. The routes to achieve the final goal of the project and the depth in which they were investigated changed throughout the project due to personnel changes and the COVID-19 pandemic. The project resulted in the development of a novel manufacturing process to integrate metal inserts with tailored polymer composite preforms through overmolding. Leveraging this proven manufacturing process, a lightweight seat back was designed through topology optimization and numerical simulations. The designed seat back uses AM metal inserts and compression overmolding of tailored polymer composite preforms obtained via large scale additive manufacturing. The metal polymer composite structures fabricated through this process exhibited enhancement in stiffness and improved ductility upon testing. Overall, the project provided an alternative design and manufacturing technique for automotive seat back that enables weight saving while meeting the safety and performance requirements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

ION Advanced Solvent CO 2 Capture Pilot Project

This final report to DOE/NETL presents all the work and tests performed during ION Engineering’s (ION) carbon dioxide (CO 2 ) capture pilot test campaign: “ION Advanced Solvent CO 2 Capture Project”. This project is comprised of three budget periods (BP): BP1 – Design of a 0.6 MWe system, BP2 – 0.5 MWe National Carbon Capture Center (NCCC) Testing Campaign, and BP3 – 12 MWe Technology Center at Mongstad (TCM) Testing Campaign. The program began in October 2013 and continued until December 2017. The campaign at NCCC, which took place from June to August of 2015, resulted in 1,116 operational hours where ~350 tonnes of CO 2 were captured from the 0.5 MWe coal-fired flue gas slipstream. The follow-up campaign at TCM took place from Oct 2016 to April 2017, where 2,775 hours of operation resulted in the capture of 14,820 tonnes of CO 2 , which is a cumulative of capturing CO 2 from the natural gas-fired Combined Heat and Power (CHP) plant, and a residue fluid catalytic cracker gas (RFCC) from Statoil at Mongstad. ION has conducted a thoughtful, comprehensive, and successful program in close collaboration with all its partners. CO 2 was successfully removed and captured for extended periods of time using three gas sources (NCCC Coal-fired Power plant, TCM CHP flue gas with and without CO 2 recycle, and TCM RFCC flue gas with air dilution) at various slipstream flows ranging from 0.5 MWe to 12 MWe. The data gathered on its solvent continues to strengthen the development of ION’s advanced solvent and positive track record in executing on-site test campaigns in highly industrialized settings. These existing pilot plants do not include the necessary process modifications that ION requires to obtain the lowest possible regeneration energy; processes such as a cold-rich bypass and intercooling, would allow for lowering the demonstrated specific reboiler duty. The resulting data was also used to validate the process model, and, in the absence of testing on a custom test rig specifically designed for ION’s advanced solvent, the results of the process model have shown specific reboiler duties under 2.5 MJ/kg CO 2 (1075 BTU/lb CO 2 ). When these regeneration energies are factored into the Techno-Economic Analysis (TEA), where ION’s technology is compared to the DOE/NETL BBS Case 12, which integrates a highly optimized and commercially available CO 2 capture technology, ION’s technology shows: 38% incremental reduction in capital cost of CO 2 capture; 28% incremental reduction in annual operating and maintenance costs of CO 2 capture; and $35-44 per tonne of CO 2 capture, which is a 20% to 40% reduction in cost of CO 2 capture. Throughout pilot testing, ION has confirmed its understanding of process improvements and analytics that will enable successful operation of its advanced solvent at significantly lower L/G circulation rates, packing heights and regeneration energies than MEA. ION has identified routes to further develop its technology and is looking forward to testing a fully optimized solvent and capture system in the near future.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Performance Study of CXL Memory Topology

This paper presents a comprehensive evaluation of the performance impact of various Compute Express Link (CXL) memory topologies, with a particular emphasis on CXL switches, in the context of High- Performance Computing (HPC) and Large Language Model (LLM) inference workloads. Our study unveils significant performance variations across different topologies, demonstrating that certain configurations yield superior performance for specific workloads. These findings underscore the critical importance of tailored topol- ogy selection in optimizing system performance. Additionally, we address the inherent challenges associated with integrating CXL switches, including overhead considerations and routing complex- ities. Our research highlights the necessity for thorough evalua- tion methodologies to fully leverage CXL technology’s potential in contemporary computing environments. These insights provide valuable guidance for system architects and data center operators in designing and optimizing CXL-based infrastructures for diverse workload requirements.

CXL, memory, Artificial Intelligence (AI), HPC↗
DOE OSTI2015

A novel process route for the production of spherical SLS polymer powders

Currently, rapid prototyping gradually is transferred to additive manufacturing opening new applications. Especially selective laser sintering (SLS) is promising. One drawback is the limited choice of polymer materials available as optimized powders. Powders produced by cryogenic grinding show poor powder flowability resulting in poor device quality. Within this account we present a novel process route for the production of spherical polymer micron-sized particles of good flowability. The feasibility of the process chain is demonstrated for polystyrene e. In a first step polymer microparticles are produced by a wet grinding method. By this approach the mean particle size and the particle size distribution can be tuned between a few microns and several 10 microns. The applicability of this method will be discussed for different polymers and the dependencies of product particle size distribution on stressing conditions and process temperature will be outlined. The comminution products consist of microparticles of irregular shape and poor powder flowability. An improvement of flowability of the ground particles is achieved by changing their shape: they are rounded using a heated downer reactor. The influence of temperature profile and residence time on the product properties will be addressed applying a viscous-flow sintering model. To further improve the flowability of the cohesive spherical polymer particles nanoparticles are adhered onto the microparticles’ surface. The improvement of flowability is remarkable: rounded and dry-coated powders exhibit a strongly reduced tensile strength as compared to the comminution product. The improved polymer powders obtained by the process route proposed open new possibilities in SLS processing including the usage of much smaller polymer beads.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms

The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.

59 BASIC BIOLOGICAL SCIENCES↗