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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

The Effects of a Dynamic Spectrum Access Overlay in LTE-Advanced Networks

As early as 2014, wireless network operators spectral capacity will be overwhelmed by a data tsunami brought on by new devices and applications. To augment spectral capacity, operators could deploy a Dynamic Spectrum Access (DSA) overlay. In the light of the many planned Long Term Evolution (LTE) network deployments, the affects of a DSA overlay have not been fully considered into the existing LTE standards. Coalescing many different aspects of DSA, this paper develops the Spectrum Accountability (SA) framework. The SA framework defines specific network element functionality, protocol interfaces, and signaling flow diagrams for LTE to support service requests and enforce rights of responsibilities of primary and secondary users, respectively. We also include a network simulation to quantify the benefits of using DSA channels to augment capacity. Based on our simulation we show that, network operators can benefit up to %40 increase in operating capacity when sharing DSA bands to augment spectral capacity. With our framework, this paper could serve as an guide in developing future LTE network standards that include DSA.

42 ENGINEERING↗
DOE OSTI2016

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

58 GEOSCIENCES↗
DOE OSTI2022

Cross Inference of Throughput Profiles Using Micro Kernel Network Method

Dedicated network connections are being increasingly deployed in cloud, centralized and edge computing and data infrastructures, whose throughput profiles are critical indicators of the underlying data transfer performance. Due to the cost and disruptions to physical infrastructures, network emulators, such as Mininet, are often used to generate measurements needed to estimate throughput profiles, typically expressed as a function of the connection round trip time. The profiles estimated using measurements from such emulated networks are usually inaccurate for high bandwidth and high latency connections, since they do not accurately reflect the critical network transport dynamics mainly due to computing and memory constraints of the host. We present a machine learning (ML) method to estimate the throughput profiles using emulation measurements to closely match the testbed and production network profiles. In particular, we propose a micro Kernel Network (mKN) that provides baseline throughput measurements on the host running Mininet emulations, which are used to learn a regression map that converts them to the corresponding testbed measurement estimates. Once initially learned, this map is applied to measurements from subsequent network emulations on the same host. We present experimental measurements to illustrate this approach, and derive generalization equations for the proposed mKN-ML method. Using a four-site scenario emulation, we show the effectiveness of this method in providing accurate concave throughput profiles from inaccurate convex or non-smooth ones indicated by Mininet emulation.

Rao, Nageswara↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗
DOE OSTI2012

Modifications on the hydrogen bond network by mutations of Escherichia coli copper efflux oxidase affect the process of proton transfer to dioxygen leading to alterations of enzymatic activities

Highlights: Black-Right-Pointing-Pointer Proton transfer pathway to dioxygen in CueO was identified. Black-Right-Pointing-Pointer Glu506 is the key amino acid to transport proton. Black-Right-Pointing-Pointer The Ala mutation at Glu506 formed a compensatory proton transfer pathway. Black-Right-Pointing-Pointer The Ile mutation at Glu506 shut down the hydrogen bond network. -- Abstract: CueO has a branched hydrogen bond network leading from the exterior of the protein molecule to the trinuclear copper center. This network transports protons in the four-electron reduction of dioxygen. We replaced the acidic Glu506 and Asp507 residues with the charged and uncharged amino acid residues. Peculiar changes in the enzyme activity of the mutants relative to the native enzyme indicate that an acidic amino acid residue at position 506 is essential for effective proton transport. The Ala mutation resulted in the formation of a compensatory hydrogen bond network with one or two extra water molecules. On the other hand, the Ile mutation resulted in the complete shutdown of the hydrogen bond network leading to loss of enzymatic activities of CueO. In contrast, the hydrogen bond network without the proton transport function was constructed by the Gln mutation. These results exerted on the hydrogen bond network in CueO are discussed in comparison with proton transfers in cytochrome oxidase.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A key route to designing huge eight-fold interpenetrated coordination networks with ths-type topology: Synthesis, structures, and topological characteristics

Solvothermal reactions of Cd(II) or Mn(II) ions with 4'-(4-carboxylphenyl)-2,2':6',2{sup -}terpyridine acid (Hcpt) and thiophene-2,5-dicarboxylic acid (H{sub 2}tpd) resulted in the formation of two isostructrual coordination networks ([M{sub 2}(cpt){sub 2}(tpd)]·3.5H{sub 2}O){sub n} (M=Cd, 1; M=Mn, 2). These complexes have been characterized by single-crystal X-ray diffraction analyses, infrared spectra (IR), elemental analyses, and powder X-ray diffraction (PXRD). Complexes 1 and 2, formed by the cpt{sup −} and tpd{sup 2−} bidentate connectors, have a 3D framework with a 3-connected, uninodal 10{sup 3}-ths topology possessing an unusual eight-fold [4*2] interpenetration mode. In addition, the ths cage has a long intracage M⋯M distance. In contrast, only a 1D coordination network ([CdCl(cpt)]·3.75H{sub 2}O){sub n} (3) was obtained under similar conditions while in the absence of the H{sub 2}tpd ligand. Compound 3 is propagated only by the [CdCl(cpt)] unit, which illustrated that the incorporation of the cpt{sup −} and Cl{sup −} ligands to form a 1D network. The distinct ancillary anions (tpd{sup 2−} or Cl{sup −}) play a critical role in determining the coordination features and the network connectivity of metal ions. This work presents a successful route to preparing rare eight-fold [4*2] interpenetrated networks. - Highlights: • A ths-type network with an eight-fold [4*2] interpenetration mode is rare. • Combining elongated ligands facilitate the eight-fold interpenetrated networks. • This ths-type cage features longe intracage M⋯M distances. • The chloride anion cannot help the forming of 3D frameworks. • The integration of bulky and multi-carboxylate ligands helps the forming of ths-type MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

PbO networks composed of single crystalline nanosheets synthesized by a facile chemical precipitation method

Graphical abstract: - Highlights: • Synthesis of PbO networks through a simple chemical precipitation route. • The synthesis method is rapid and low-cost. • Each network is composed of single crystalline PbO nanosheets. • A possible growth mechanism is proposed for synthesized PbO networks. - Abstract: For the field of energy storage, nanostructured lead oxide (PbO) shows immense potential for increased specific energy and deep discharge for lead acid battery technologies. In this work, PbO networks composed of single crystalline nanosheets were synthesized utilizing a simple, low cost and rapid chemical precipitation method. The PbO networks were prepared in a single reaction vessel from starting reagents of lead acetate dehydrate, ammonium hydroxide and deionized water. Lead acetate dehydrate was chosen as a reagent, as opposed to lead nitrate, to eliminate the possibility of nitrate contamination of the final product. X-ray diffraction (XRD) analysis, high resolution scanning electron microscopy (HRSEM) and high resolution transmission electron microscopy (HRTEM) analysis were used to characterize the synthesized PbO networks. The reproducible method described herein synthesized pure β-PbO (massicot) powders, with no byproducts. A possible formation mechanism for these PbO networks is proposed. The growth is found to proceed predominately in the 〈1 1 1〉 and 〈2 0 0〉 directions while being limited in the 〈0 1 1〉 direction.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Network Algorithms for Detection of Radiation Sources

In support of national defense, Domestic Nuclear Detection Office’s (DNDO) Intelligent Radiation Sensor Systems (IRSS) program supported the development of networks of radiation counters for detecting, localizing and identifying low-level, hazardous radiation sources. Industry teams developed the first generation of such networks with tens of counters, and demonstrated several of their capabilities in indoor and outdoor characterization tests. Subsequently, these test measurements have been used in algorithm replays using various sub-networks of counters. Test measurements combined with algorithm outputs are used to extract Key Measurements and Benchmark (KMB) datasets. We present two selective analyses of these datasets: (a) a notional border monitoring scenario that highlights the benefits of a network of counters compared to individual detectors, and (b) new insights into the Sequential Probability Ratio Test (SPRT) detection method, which lead to its adaptations for improved detection. Using KMB datasets from an outdoor test, we construct a notional border monitoring scenario, wherein twelve 2”*2” NaI detectors are deployed on the periphery of 21*21meter square region. A Cs-137 (175 uCi) source is moved across this region, starting several meters from outside and finally moving away. The measurements from individual counters and the network were processed using replays of a particle filter algorithm developed under IRSS program. The algorithm outputs from KMB datasets clearly illustrate the benefits of combining measurements from all networked counters: the source was detected before it entered the region, during its trajectory inside, and until it moved several meters away. When individual counters are used for detection, the source was detected for much shorter durations, and sometimes was missed in the interior region. The application of SPRT for detecting radiation sources requires choosing the detection threshold, which in turn requires a source strength estimate, typically specified as a multiplier of the background radiation level. A judicious selection of this source multiplier is essential to achieve optimal detection probability at a specified false alarm rate. Typically, this threshold is chosen from the Receiver Operating Characteristic (ROC) by varying the source multiplier estimate. ROC is expected to have a monotonically increasing profile between the detection probability and false alarm rate. We derived ROCs for multiple indoor tests using KMB datasets, which revealed an unexpected loop shape: as the multiplier increases, detection probability and false alarm rate both increase until a limit, and then both contract. Consequently, two detection probabilities correspond to the same false alarm rate, and the higher is achieved at a lower multiplier, which is the desired operating point. Using the Chebyshev’s inequality we analytically confirm this shape. Then, we present two improved network-SPRT methods by (a) using the threshold off-set as a weighting factor for the binary decisions from individual detectors in a weighted majority voting fusion rule, and (b) applying a composite SPRT derived using measurements from all counters.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Reprocessable polyhydroxyurethane networks reinforced with reactive polyhedral oligomeric silsesquioxanes (POSS) and exhibiting excellent elevated temperature creep resistance

The rapid development of covalent adaptable networks or vitrimers shows promise for addressing the long-standing recycling issues associated with conventional, permanently cross-linked thermosets. At the same time, it is important to demonstrate that properties of reprocessable polymer networks can be optimized to meet the ongoing demand for high-performance materials. We have fabricated reprocessable polyhydroxyurethane (PHU) network composites reinforced with reactive polyhedral oligomeric silsesquioxanes (POSS). With functionalized POSS serving as a fraction of the cross-linkers, the PHU–POSS network nanocomposites exhibit significantly enhanced storage modulus at the rubbery plateau region relative to the neat PHU network. With up to 10 wt% POSS loading, these network composites can undergo melt-state reprocessing at 140 °C with 100% property recovery associated with cross-link density. We also show that hydroxyurethane dynamic chemistry leads to excellent creep resistance at elevated temperature up to 90 °C and is unaffected by reactive incorporation of POSS. In conclusion, this study demonstrates the effectiveness of POSS as nanofillers for designing high-performance, organic-inorganic dynamic PHU networks with excellent reprocessability.

42 ENGINEERING↗
DOE OSTI2025

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies

This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage–power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50 % reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50–250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75 % of N-2 contingency network structures, even without historical N-2 outage data. Furthermore, the proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement

Highlights: • We propose a reverse logistics network for MSW involving waste pickers. • A generic facility location mathematical model was validated in a Brazilian city. • The results enable to predict the capacity for screening and storage centres (SSC). • We minimise the costs for transporting MSW with screening and storage centres. • The use of SSC can be a potential source of revenue and a better use of MSW. - Abstract: This study proposes a reverse logistics network involved in the management of municipal solid waste (MSW) to solve the challenge of economically managing these wastes considering the recent legal requirements of the Brazilian Waste Management Policy. The feasibility of the allocation of MSW material recovery facilities (MRF) as intermediate points between the generators of these wastes and the options for reuse and disposal was evaluated, as well as the participation of associations and cooperatives of waste pickers. This network was mathematically modelled and validated through a scenario analysis of the municipality of São Mateus, which makes the location model more complete and applicable in practice. The mathematical model allows the determination of the number of facilities required for the reverse logistics network, their location, capacities, and product flows between these facilities. The fixed costs of installation and operation of the proposed MRF were balanced with the reduction of transport costs, allowing the inclusion of waste pickers to the reverse logistics network. The main contribution of this study lies in the proposition of a reverse logistics network for MSW simultaneously involving legal, environmental, economic and social criteria, which is a very complex goal. This study can guide practices in other countries that have realities similar to those in Brazil of accelerated urbanisation without adequate planning for solid waste management, added to the strong presence of waste pickers that, through the characteristic of social vulnerability, must be included in the system. In addition to the theoretical contribution to the reverse logistics network problem, this study aids in decision-making for public managers who have limited technical and administrative capacities for the management of solid wastes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Synergistic Combination of Living Ring-Opening Metathesis Polymerization and Atom Transfer Radical Polymerization to Synthesize Structurally Tailored and Engineered Macromolecular Networks

Structurally tailored and engineered macromolecular (STEM) networks are attractive materials for soft robotics, stretchable electronics, tissue engineering, and 3D printing due to their tunable properties. To date, STEM networks have been synthesized by atom transfer radical polymerization (ATRP) or the combination of reversible addition–fragmentation chain-transfer (RAFT) polymerization and ATRP. RAFT polymerization could have limited selectivity with ATRP inimer sites that can participate in radical-transfer processes. On the other hand, living ring-opening metathesis polymerization (ROMP) can produce a polymeric network with latent ATRP initiator sites in high selectivity. Herein, for the first time, we report the syntheses of STEM zero-generation (STEM-0) networks using a monomer, a cross-linker, and an ATRP/ROMP inimer via living ROMP, followed by their modification using a second monomer via ATRP to synthesize STEM first-generation (STEM-1) networks. The mechanical property and swelling capacity analyses of these networks were carried out. A change in mechanical properties and swelling capacity of these networks was observed due to their structural modification.

Absorption↗
DOE OSTI2025

Structural and Dynamical Insights into the Formation Process of a Cross-Linked Polymer Network in Acrylic Adhesives During Thermal Curing

Many modern adhesives, sealants, and coatings rely on the controlled transition from a liquid to a solid state by forming a three-dimensional cross-linked polymer network, often referred to as curing. The curing process, which is initiated by the mixing of reactive components or an external trigger, defines the structure of a network and further controls the final mechanical properties of cured materials. However, the curing mechanism is not fully understood yet due to the lack of experimental tools capable of directly probing the structure and dynamics of a network over relevant time- and length scales. Here, in this paper, we report the curing process of a commercial two-component methyl methacrylate (MMA) adhesive using in operando X-ray photon correlation spectroscopy (XPCS), a method that closely simulates the target manufacturing environment of the adhesive. The results are then integrated with those obtained by rheology, differential scanning calorimetry (DSC), and transmission electron microscopy to establish the structure–dynamics–process–property relationship. The XPCS results identify four distinct stages in the curing process after the mixing, extrusion, and deposition of the acrylic adhesive: (i) At a cure time (or “aging time”, t age ) of less than 1 min, nanodomains of polymerized MMA are formed within a liquid monomeric MMA matrix. The average size is several nm and remains constant over t age , while the dynamics of the nanodomains are slowed down with t age due to an increase in the viscosity of the MMA matrix. (ii) After t age > 1 min, the size of the nanodomains increases with t age until the gel point (= 6.3 min after mixing as determined by rheology). The dynamics of the nanodomains also increase due to the heat generated by the exothermic reaction. (iii) At the gel point, the nanodomains begin to interconnect each other, resulting in a network structure with a characteristic length of about 100 nm. This characteristic network size does not change for the rest of the curing process up to t age = 500 min. The dynamics of the network structure, however, show a rapid slowing down with t age up to t age ≈ 12 min, corresponding to the onset of vitrification (as determined by rheology). (iv) At t age > 12 min, when the DSC and rheology data can no longer provide meaningful information, the XPCS data show a further slowing down of the network dynamics associated with vitrification. Our results provide rich and complex insights into the physics and material design of thermosets in commercially relevant processes, which are essential for future industrial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Impact of an Emergent Hierarchical Filler Network on Nanocomposite Dynamics

Aggregated polymer fillers, such as carbon black and silica, at concentrations above the percolation threshold form an emergent structure, the hierarchical filler network, in immiscible systems where dispersion is driven by accumulated strain. It is proposed that the hierarchical filler network is composed of a primary nanoscale network that locally percolates at ~5 vol % of aggregates, associated with changes in the dynamic spectrum at low strain, and a secondary micrometer-scale network that globally percolates at ~20 vol % associated with the Payne effect and electrical conductivity. A model is proposed with an elastomer dominated dynamic response described by Einstein–Smallwood behavior at high frequencies and small sizes and a filler network dominated response at low frequencies and large sizes. The nanoscale mesh size correlates with this transition in low strain dynamic response. The micrometer-scale network displays a gel-like dynamic response at very low frequencies and a corresponding gel-like structural scaling regime at large sizes. In conclusion, the hierarchical filler network is described by two crossover frequencies and associated relaxation times, τ* and τ cc , and two related structural scaling regimes.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Nonlinear Elasticity: From Single Chain to Networks and Gels

Biological and polymeric networks show highly nonlinear stress–strain behavior manifested in materials that stiffen with increasing deformation. Using a combination of the theoretical analysis and molecular dynamics simulations, we develop a model of network deformation that describes nonlinear mechanical properties of networks and gels by relating their macroscopic strain-hardening behavior to molecular parameters of the network strands. The starting point of our approach is a nonlinear force/elongation relation for discrete chains with varying bending rigidity. The derived expression for the network free energy is a universal function of the first deformation invariant and chain elongation ratio that depends on a ratio of the unperturbed chain size to chain dimension in a fully extended conformation. The model predictions for the nonlinear shear modulus and differential shear modulus for uniaxial and shear deformations are in very good agreement with both the results of molecular dynamics simulations of networks and with experimental data for biopolymer networks of actin, collagen, fibrin, vimentin, neurofilaments, and pectin.

Polymer Science↗
DOE OSTI2019

Linking structural and transport properties in three-dimensional fracture networks

Here, we investigate large scale particle motion and solute breakthrough in sparse three-dimensional discrete fracture networks (DFN) characterized by power-law distributed fracture lengths. The three networks we consider have the same fracture intensity values but exhibit different percolation densities, geometric properties and topological structures. We considered two different average transport models to predict solute breakthrough, a streamtube model and a Bernoulli CTRW model, both of which provide insights into the flow fields within the networks. The streamtube model provides acceptable predictions at short distances in two of the networks, but fails in all cases to predict breakthrough times at the outlet plane, which indicates that particle motion in such fracture networks cannot be characterized by a constant velocity between the inlet and control plane at which the breakthrough curve is detected. Rather the structure of the network requires that frequent velocity transitions be made as particles move through the system. Despite the relatively broad distribution of fracture radii and relatively small number of independent velocity transitions, the CTRW approach conditioned on the initial velocity distribution provides reasonable predictions for the breakthrough curves at different distances from the inlet. The application of these averaged transport models provides a richer understanding of the link from the fracture network structure to ow and transport properties.

58 GEOSCIENCES↗
DOE OSTI2022

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

Abstract Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep reaction network exploration at a heterogeneous catalytic interface

Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga 3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗