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Controlling Heterogeneous Catalysis with Organic Monolayers on Metal Oxides
A key theme of heterogeneous catalysis research is achieving control of the environment surrounding the active site to precisely steer the reactivity toward desired reaction products. One method toward this goal has been the use of organic ligands or self-assembled monolayers (SAMs) on metal nanoparticles. Metal-bound SAMs are typically employed to improve catalyst selectivity but often decrease the reaction rate as a result of site blocking from the ligands. Recently, the use of metal oxide-bound organic modifiers such as organophosphonic acid (PA) SAMs has shown promise as an additional method for tuning reactions on metal oxide surfaces as well as modifying oxide-supported metal catalysts. In this Account, we summarize recent approaches to enhance catalyst performance with oxide-bound monolayers. These approaches include (1) modification of metal oxide catalysts to tune surface reactions, (2) formation of SAMs on the oxide component of supported metal catalysts to modify sites at the metal–support interface, and (3) enhancement of catalyst performance (e.g., stability) through modification of sites remote from the active sites. Further, both the headgroups and organic tail groups of PA SAMs or other ligands can influence reactions on metal oxide surfaces. Binding of the headgroup can selectively poison certain active sites, altering the selectivity in a manner analogous to metal-bound ligands (at the expense of active site quantity). Moreover, tail groups can be functionalized to interact favorably with reactants and intermediates, for instance through dipole–dipole interactions. On supported metal catalysts like Pt/Al 2 O 3 , PA SAMs can selectively form on the oxide support. This selective deposition allows for modification of the metal–support interface with minimal blockage of metal sites. PA headgroups were shown to provide tunable acid sites at the interface, dramatically improving hydrodeoxygenation rates of various alcohols. Additionally, organic tail functionality was used to activate or stabilize specific reactants at the interface, such as with the use of amine-functionalized PAs to stabilize chemisorption of CO 2 during the reverse water gas shift reaction. PAs have also been found to affect the electronic properties of bulk metal sites through long-range electron withdrawal via the oxide, providing an additional avenue to tune catalytic behavior. Finally, organic modifiers were shown to enhance catalytic performance without directly modifying the active site. For instance, in biphasic liquid environments the modification of catalyst particles with hydrophobic or hydrophilic SAMs shifts the selectivity of multipath reactions on the basis of the hydrophobicities of different intermediates and products. As another “long-range” effect, the deposition of ligands on oxide supports improved catalyst stability through both improved resistance to sintering and suppression of active site poisoning. The recent contributions discussed in this Account demonstrate the versatility and significant potential for the approach of modifying catalysts with oxide-bound organic monolayers.
Direct Measurement of Water Permeation in Submerged Alkyl Thiol Self-Assembled Monolayers on Gold Surfaces Revealed by Neutron Reflectometry
Self-assembled monolayers (SAMs) of alkyl thiols are frequently used to chemically functionalize gold surfaces for applications throughout materials chemistry, electrochemistry, and biotechnology. Despite this, a detailed understanding of the structure of the SAM–water interface generated from both formation and use of the SAM in an aqueous environment is elusive, and analytical measurements of the structure and chemistry of the SAM–water interface are an ongoing experimental challenge. To address this, we used neutron reflectometry (NR) to measure water association with both hydrophobic and hydrophilic SAMs under both wet and dry conditions. SAMs used for this study were made from hydrophobic decanethiol mixed with hydrophilic 11-azido-1-undecanethiol with compositions of 0–100% of the azide-terminated thiol. All SAMs were formed by conventional solution incubation of a Au substrate immersed in ethanol. Each SAM was characterized by grazing incidence angle reflection–absorption Fourier transfer infrared spectroscopy, contact angle goniometry, and electrochemical methods to confirm it was a completely formed monolayer with evidence of extensive crystalline-like domains. NR measured significant absorption of water into each SAM, ranging from 1.6 to 5.7 water molecules per alkyl thiol, when SAMs were immersed in water. Water infiltration was independent of SAM composition and terminal group hydrophilicity. Furthermore these results demonstrate that water accesses defects, fluid regions, and heterogeneous domains inherent to even well-formed SAMs.
SU-F-T-152: Experimental Validation and Calculation Benchmark for a Commercial Monte Carlo Pencil BeamScanning Proton Therapy Treatment Planning System in Heterogeneous Media
Purpose: Eclipse AcurosPT 13.7, the first commercial Monte Carlo pencil beam scanning (PBS) proton therapy treatment planning system (TPS), was experimentally validated for an IBA dedicated PBS nozzle in the CIRS 002LFC thoracic phantom. Methods: A two-stage procedure involving the use of TOPAS 1.3 simulations was performed. First, Geant4-based TOPAS simulations in this phantom were experimentally validated for single and multi-spot profiles at several depths for 100, 115, 150, 180, 210 and 225 MeV proton beams, using the combination of a Lynx scintillation detector and a MatriXXPT ionization chamber array. Second, benchmark calculations were performed with both AcurosPT and TOPAS in a phantom identical to the CIRS 002LFC, with the exception that the CIRS bone/mediastinum/lung tissues were replaced with similar tissues that are predefined in AcurosPT (a limitation of this system which necessitates the two stage procedure). Results: Spot sigmas measured in tissue were in agreement within 0.2 mm of TOPAS simulation for all six energies, while AcurosPT was consistently found to have larger spot sigma (<0.7 mm) than TOPAS. Using absolute dose calibration by MatriXXPT, the agreements between profiles measurements and TOPAS simulation, and calculation benchmarks are over 97% except near the end of range using 2 mm/2% gamma criteria. Overdosing and underdosing were observed at the low and high density side of tissue interfaces, respectively, and these increased with increasing depth and decreasing energy. Near the mediastinum/lung interface, the magnitude can exceed 5 mm/10%. Furthermore, we observed >5% quenching effect in the conversion of Lynx measurements to dose. Conclusion: We recommend the use of an ionization chamber array in combination with the scintillation detector to measure absolute dose and relative PBS spot characteristics. We also recommend the use of an independent Monte Carlo calculation benchmark for the commissioning of a commercial TPS. Partially supported by Varian Medical System under the master agreement between Varian and University of pennsylvania.
Imaging single-molecule reaction intermediates stabilized by surface dissipation and entropy
Chemical transformations at the interface between solid/liquid or solid/gaseous phases of matter lie at the heart of key industrial-scale manufacturing processes. A comprehensive study of the molecular energetics and conformational dynamics that underlie these transformations is often limited to ensemble-averaging analytical techniques. In this paper we report the detailed investigation of a surface-catalysed cross-coupling and sequential cyclization cascade of 1,2-bis(2-ethynyl phenyl)ethyne on Ag(100). Using non-contact atomic force microscopy, we imaged the single-bond-resolved chemical structure of transient metastable intermediates. Theoretical simulations indicate that the kinetic stabilization of experimentally observable intermediates is determined not only by the potential-energy landscape, but also by selective energy dissipation to the substrate and entropic changes associated with key transformations along the reaction pathway. Finally, the microscopic insights gained here pave the way for the rational design and control of complex organic reactions at the surface of heterogeneous catalysts.
Cesium-Induced Active Sites for C–C Coupling and Ethanol Synthesis from CO 2 Hydrogenation on Cu/ZnO(000$\bar{1}$) Surfaces
The efficient conversion of carbon dioxide, a major air pollutant, into ethanol or higher alcohols is a big challenge in heterogeneous catalysis, generating great interest in both basic scientific research and commercial applications. Here, we report the facilitated methanol synthesis and the enabled ethanol synthesis from carbon dioxide hydrogenation on a catalyst generated by codepositing Cs and Cu on a ZnO(000$\bar{1}$) substrate. A combination of catalytic testing, X-ray photoelectron spectroscopy (XPS) measurements, and calculations based on density functional theory (DFT) and kinetic Monte Carlo (KMC) simulation was used. The results of XPS showed a clear change in the reaction mechanism when going from Cs/Cu(111) to a Cs/Cu/ZnO(000$\bar{1}$) catalyst. The Cs-promoting effect on C–C coupling is a result of a synergy among Cs, Cu, and ZnO components that leads to the presence of CH x and CH y O species on the surface. Furthermore, according to the DFT-based KMC simulations, the deposition of Cs introduces multifunctional sites with a unique structure at the Cu–Cs–ZnO interface, particularly being able to promote the interaction with CO 2 and thus the methanol synthesis predominantly via the formate pathway. More importantly, it tunes the CHO binding strongly enough to facilitate the HCOOH decomposition to CHO via the formate pathway, but weakly enough to allow further hydrogenation to methanol. The fine-tuning of CHO binding also enables a close alignment of a CHO pair to facilitate the C–C coupling and eventually ethanol synthesis. Our study opens new possibilities to allow the highly active and selective conversion of carbon dioxide to higher alcohols on widely used and low-cost Cu-based catalysts.
Origin of Selective Production of Hydrogen Peroxide by Electrochemical Oxygen Reduction
Oxygen reduction reaction (ORR) is one of the most important electrochemical reactions. Starting from a common reaction intermediate *–O–OH, the ORR splits into two pathways, either producing hydrogen peroxide (H 2 O 2 ) by breaking the *–O bond or leading to water formation by breaking the O–OH bond. However, it is puzzling why many catalysts, despite the strong thermodynamic preference for the O–OH breaking, exhibit high selectivity for hydrogen peroxide. Moreover, the selectivity is dependent on the potential and pH, which remain not understood. Here, for this work, we develop an advanced first-principles model for effective calculation of the electrochemical reaction kinetics at the solid–water interface, which were not accessible by conventional models. Using this model to study representative catalysts for H 2 O 2 production, we find that breaking the O–OH bond can have a higher energy barrier than breaking *–O, due to the rigidity of the O–OH bond. Importantly, we reveal that the selectivity dependence on potential and pH is rooted into the proton affinity to the former/later O in *–O–OH. For single cobalt atom catalyst, decreasing potential promotes proton adsorption to the former O, thereby increasing the H 2 O 2 selectivity. In contrast, for the carbon catalyst, the proton prefers the latter O, resulting in a lower H 2 O 2 selectivity in acid condition. These findings explain the experiments and highlight the kinetic origins of the selectivity. Our work improves the understanding of ORR by uncovering the proton affinity as a new factor and provides a new model to effectively simulate the atomic-level kinetics of heterogeneous electrochemistry.
Selective Bidentate Coordination Reconstructs Residual PbI 2 to Homogenize Interfacial Energetics in Perovskite Solar Cells
Spatially heterogeneous interfacial energetics, often originating from residual lead iodide (PbI 2 ), represent a fundamental bottleneck to both the efficiency and operational stability of perovskite photovoltaics. Conventional PbI 2 passivation strategies based on monodentate ligands or highly polar solvents either interact weakly with PbI 2 or undesirably perturb the underlying three-dimensional perovskite lattice. Here, we report a diammonium bidentate strategy that selectively reconstructs residual PbI 2 into corner-sharing PbI 6 octahedra while preserving the bulk perovskite. Enabled by dual-site coordination, a newly designed thiophene-based bidentate ligand (MeXT) stabilizes PbI 6 units during passivation and establishes spatially homogeneous interfacial energetics. Perovskite solar cells incorporating MeXT achieve 26.19% power conversion efficiency and retain over 80% of their initial efficiency after 1000 h of continuous 1-sun illumination at 75 °C. This dual-anchor coordination passivation strategy establishes a general design principle for selectively treating residual PbI 2 and creating electronically coherent and operationally stable interfaces in perovskite photovoltaics.
Performance Results on CPU/GPU Exascale Architectures for OMEGA: The Ocean Model for E3SM Global Applications
The US Department of Energy (DOE) conducts climate simulations on some of the world’s largest supercomputers. These exascale machines use heterogeneous architectures with both CPUs and GPUs, and scientific codes must adapt to make full use of this computing power. Los Alamos National Lab is developing Omega: The Ocean Model for E3SM Global Applications, which is specifically designed for modern exascale computers. It uses external libraries that have been optimized for a variety of architectures to run on different supercomputers. Omega is an unstructured-mesh ocean model based on TRiSK numerical methods. It will be the new ocean component of the DOE’s Energy Exascale Earth System Model (E3SM). The algorithms in Omega follow those of the current ocean component, MPAS-Ocean, but it will be written in C++ rather than Fortran to take advantage of the Kokkos performance portability library. Omega spatial operators are written as Kokkos kernels to run efficiently on both CPUs and GPUs. Work on Omega began in 2023 with a new C++ framework for unstructured mesh partitioning, halo exchanges, parallel IO, and Kokkos interfaces. The current version, Omega-0, is being developed to solve the shallow water equations and at present includes all of the tendency terms but not time stepping. Here we share the results of Omega-0 verification and performance testing. Verification includes unit tests implemented with CTest as well as convergence tests in Polaris, an in-house python package with a large suite of test problems. Performance tests compare simulations conducted on CPUs versus GPUs and across different architectures: tests are run on Frontier, which has AMD “Optimized 3rd Gen EPYC” CPUs and AMD MI250X GPUs, as well as Perlmutter, which is composed of AMD EPYC 7763 CPUs and NVIDIA A100 GPUs.
Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement
In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.
PIPER: Performance Insight for Programmers and Exascale Runtimes: Guiding the Development of the Exascale Software Stack
The PIPER project set out to develop methodologies and software for measurement, analysis, attribution, and presentation of performance data for extreme-scale systems. Goals of the project were to support analysis of massive multi-scale parallelism, heterogeneous architectures, multi-faceted performance concerns, and to support both post-mortem performance analysis to identify program features that contribute to problematic performance and on-line performance analysis to drive adaptation. This final report summarizes the research and development activity at Rice University as part of the PIPER project. Producing a complete suite of performance tools for exascale platforms during the course of this project was impossible since both hardware and software for exascale systems is still a moving target. For that reason, the project focused broadly on the development of new techniques for measurement and analysis of performance on modern parallel architectures, enhancements to HPCToolkit’s software infrastructure to support our research goals or use on sophisticated applications, engaging developers of multithreaded runtimes to explore how support for tools should be integrated into their designs, engaging operating system developers with feature requests for enhanced monitoring support, engaging vendors with requests that they add hardware measure- ment capabilities and software interfaces needed by tools as they design new components of HPC platforms including processors, accelerators and networks, and finally collaborations with partners interested in using HPCToolkit to analyze and tune scalable parallel applications.
Developments in Performance and Portability for MadGraph5_aMC@NLO
Event generators simulate particle interactions using Monte Carlo techniques, providing the primary connection between experiment and theory in experimental high energy physics. These software packages, which are the first step in the simulation worflow of collider experiments, represent approximately 5 to 20% of the annual WLCG usage for the ATLAS and CMS experiments. With computing architectures becoming more heterogeneous, it is important to ensure that these key software frameworks can be run on future systems, large and small. In this contribution, recent progress on porting and speeding up the Madgraph5_aMC@NLO event generator on hybrid architectures, i.e. CPU with GPU accelerators, is discussed. The main focus of this work has been in the calculation of scattering amplitudes and "matrix elements", which is the computational bottleneck of an event generation application. For physics processes limited to QCD leading order, the code generation toolkit has been expanded to produce matrix element calculations using C++ vector instructions on CPUs and using CUDA for NVidia GPUs, as well as using Alpaka, Kokkos and SYCL for multiple CPU and GPU architectures. Performance is reported in terms of matrix element calculations per time on NVidia, Intel, and AMD devices. The status and outlook for the integration of this work into a production release usable by the LHC experiments, with the same functionalities and very similar user interfaces as the current Fortran version, is also described.
Drivers and ecological impacts of a wildfire outbreak in the southern Appalachian Mountains after decades of fire exclusion
During one of the warmest and driest droughts of the last century, the southern Appalachian Mountains experienced a regional outbreak of over a dozen large wildfires in late fall of 2016. We provide a synthesis of long-term forest changes leading up to the 2016 wildfires, examine the climatic setting and patterns of burn severity in relationship to topography, and discuss the ecological and management implications of these and future fires. During the pre- and post-European settlement periods, frequent low- and mixed-severity wildfires interacted with complex topographic gradients and maintained heterogenous landscapes dominated by several species of oak (Quercus spp.), pine (Pinus spp.), and the American chestnut (Castanea dentata). Land-use changes associated with European settlement, loss of the American chestnut, and 20th century fire exclusion resulted in large scale shifts towards mesic, fire-intolerant species (i.e. mesophication). Wildfire activity began increasing in the early 1980s, but most fires in the region are small (<1,000 ha). In Fall of 2016, deciduous tree leaf fall occurred concurrently with a period of anomalously dry and warm weather, creating ideal conditions for fire ignition and spread. Thousands of ignitions across the region strained suppression resources and eight fires grew to greater than 5,000 ha. The 2016 fires were larger and burned more area than in the previous three decades combined. In one unique landscape setting, the Chimney Tops 2 Fire, a synoptic wind event drove extreme fire behavior and burned large, high-severity patches resulting in devastating effects in the wildland urban interface. However, immediate post-fire burn severity mosaics for other fires were composed primarily of low- (73 %) and moderate-severity (21 %) fire effects. High-severity fire comprised only 6 % of the area burned, and occurred mostly on steep upper slopes and ridges on south-facing aspects, reflecting the importance of bottom-up topographic drivers in this region. Although the fires will likely enhance biodiversity by restoring fire-dependent species and creating early seral habitat, invasions of non-native plant species, delayed mortality of mature pines and oaks, and rapid re-sprouting of pyrophyllic shrubs pose significant management challenges. Although similar large fire outbreaks may become more common under future climatic conditions, they are unlikely to reverse the effects of mesophication and significantly alter forest dynamics at broad spatial scales. Furthermore, the 2016 fires exposed the vulnerability of the region to wildfire during acute fall drought and demonstrate the potential ecological effects of future wildfires in mixed pine-hardwood landscapes of the southern Appalachians.
Capacitive Detection of Low-Enthalpy, Higher-Order Phase Transitions in Synthetic and Natural Composition Lipid Membranes
In-plane lipid organization and phase separation in natural membranes play key roles in regulating many cellular processes. Highly cooperative, first-order phase transitions in model membranes consisting of few lipid components are well understood and readily detectable via calorimetry, densitometry, and fluorescence. However, far less is known about natural membranes containing numerous lipid species and high concentrations of cholesterol, for which thermotropic transitions are undetectable by the above-mentioned techniques. We demonstrate that membrane capacitance is highly sensitive to low-enthalpy thermotropic transitions taking place in complex lipid membranes. Specifically, we measured the electrical capacitance as a function of temperature for droplet interface bilayer model membranes of increasing compositional complexity, namely, (a) a single lipid species, (b) domain-forming ternary mixtures, and (c) natural brain total lipid extract (bTLE). We observed that, for single-species lipid bilayers and some ternary compositions, capacitance exhibited an abrupt, temperature-dependent change that coincided with the transition detected by other techniques. In addition, capacitance measurements revealed transitions in mixed-lipid membranes that were not detected by the other techniques. Most notably, capacitance measurements of bTLE bilayers indicated a transition at ~38 °C not seen with any other method. Likewise, capacitance measurements detected transitions in some well-studied ternary mixtures that, while known to yield coexisting lipid phases, are not detected with calorimetry or densitometry. These results indicate that capacitance is exquisitely sensitive to low-enthalpy membrane transitions because of its sensitivity to changes in bilayer thickness that occur when lipids and excess solvent undergo subtle rearrangements near a phase transition. Our findings also suggest that heterogeneity confers stability to natural membranes that function near transition temperatures by preventing unwanted defects and macroscopic demixing associated with high-enthalpy transitions commonly found in simpler mixtures.
Archival, anonymization and presentation of HTCondor logs with GlideinMonitor
GlideinWMS is a pilot framework to provide uniform and reliable HTCondor clusters using heterogeneous and unreliable resources. The Glideins are pilot jobs that are sent to the selected nodes, test them, set them up as desired by the user jobs, and ultimately start an HTCondor schedd to join an elastic pool. These Glideins collect information that is very useful to evaluate the health and efficiency of the worker nodes and invaluable to troubleshoot when something goes wrong. This data, including local stats, the results of all the tests, and the HTCondor log files, is packed and sent to the GlideinWMS Factory. To access this information, developers and troubleshooters must exchange emails with Factory operators and dig manually into files. Furthermore, these files contain also information like email and IP addresses, and user IDs, that we want to protect and limit access to. GlideinMonitor is a Web application to make these logs more accessible and useful: it organizes the logs in an efficient compressed archive; it allows to search, unpack, and inspect them, all in a convenient and secure Web interface; via plugins like the log anonymizer, it can redact protected information preserving the parts useful for troubleshooting.
The Materials Data Facility: Data Services to Advance Materials Science Research
With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are emerging in the materials community. The materials data facility (MDF) operates two cloudhosted services, data publication and data discovery, with features to promote open data sharing, self-service data publication and curation, and encourage data reuse, layered with powerful data discovery tools. The data publication service simplifies the process of copying data to a secure storage location, assigning data a citable persistent identifier, and recording custom (e.g., material, technique, or instrument specific)and automatically-extractedmetadata in a registrywhile the data discovery service will provide advanced search capabilities (e.g., faceting, free text range querying, and full text search) against the registered data and metadata. TheMDF services empower individual researchers, research projects, and institutions to (I) publish research datasets, regardless of size, from local storage, institutional data stores, or cloud storage, without involvement of thirdparty publishers; (II) build, share, and enforce extensible domain-specific custom metadata schemas; (III) interact with published data and metadata via representational state transfer (REST) application program interfaces (APIs) to facilitate automation, analysis, and feedback; and (IV) access a data discovery model that allows researchers to search, interrogate, and eventually build on existing published data. We describe MDF’s design, current status, and future plans.
The Heavy Photon Search (HPS) Software Environment
The Heavy Photon Search (HPS) is an experiment at the Thomas Jefferson National Accelerator Facility (JLab) designed to search for a hidden sector photon (A’) in fixed-target electro-production. It uses a silicon microstrip tracking and vertexing detector placed inside a dipole magnet to measure charged particle trajectories and a fast lead-tungstate crystal calorimeter located just downstream of the magnet to provide a trigger and to identify electromagnetic showers. The HPS experiment uses both invariant mass and secondary vertex signatures to search for the A’. The experimental collaboration is small and quite heterogeneous: it is composed of members of the nuclear physics as well as particle physics communities, from universities and national labs from around the US and Europe. Enabling such a disparate group to concentrate on the physics aspects of the experiment required that the software be easy to install and use, and having such limited manpower meant that existing solutions had to be exploited. HPS has successfully completed two engineering runs and completed its first physics run in the summer of 2019. We begin with an overview of the physics goals of the experiment followed by a short description of the detector design. We then describe the software tools used to design the detector layout and simulate the expected detector performance. Event reconstruction involving track, cluster and vertex finding and fitting for both simulated and real data was, to first order, adopted from existing software originally developed for Linear Collider studies. Bringing it all together into a cohesive whole involved the use of multiple software solutions with common interfaces.
SU-C-BRC-07: Parametrized GPU Accelerated Electron Monte Carlo Second Check
Purpose: I am presenting a parameterized 3D GPU accelerated electron Monte Carlo second check program. Method: I wrote the 3D grid dose calculation algorithm in CUDA and utilized an NVIDIA GeForce GTX 780 Ti to run all of the calculations. The electron path beyond the distal end of the cone is governed by four parameters: the amplitude of scattering (AMP), the mean and width of a Gaussian energy distribution (E and α), and the percentage of photons. In my code, I adjusted all parameters until the calculated PDD and profile fit the measured 10×10 open beam data within 1%/1mm. I then wrote a user interface for reading the DICOM treatment plan and images in Python. In order to verify the algorithm, I calculated 3D dose distributions on a variety of phantoms and geometries, and compared them with the Eclipse eMC calculations. I also calculated several patient specific dose distributions, including a nose and an ear. Finally, I compared my algorithm’s computation times to Eclipse’s. Results: The calculated MU for all of the investigated geometries agree with the TPS within the TG-114 action level of 5%. The MU for the nose was < 0.5 % different while the MU for the ear at 105 SSD was ∼2 %. Calculation times for a 12MeV 10×10 open beam ranged from 1 second for a 2.5 mm grid resolution with ∼15 million particles to 33 seconds on a 1 mm grid with ∼460 million particles. Eclipse calculation runtimes distributed over 10 FAS workers were 9 seconds to 15 minutes respectively. Conclusion: The GPU accelerated second check allows quick MU verification while accounting for patient specific geometry and heterogeneity.