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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 109 records · Page 6

The LED calibration systems for the mDOM and D-Egg sensor modules of the IceCube Upgrade: Design, production, testing and use in module calibration

The IceCube Neutrino Observatory, instrumenting about 1 km 3 of deep, glacial ice at the geographic South Pole, is due to be enhanced with the IceCube Upgrade. The IceCube Upgrade, to be deployed during the 2025/26 Antarctic summer season, will consist of seven new strings of photosensors, densely embedded near the bottom center of the existing array. Aside from a world-leading sensitivity to neutrino oscillations, a primary goal is the improvement of the calibration of the optical properties of the instrumented ice. This calibration will be applied to the entire archive of IceCube data, improving the angular and energy resolution of the detected neutrino events. For this purpose, the Upgrade strings include a host of new calibration devices. Aside from dedicated calibration modules, several thousand LED flashers have been incorporated into the photosensor modules. We describe the design, production, and testing of these LED flashers before their integration into the sensor modules as well as the use of the LED flashers during lab testing of assembled sensor modules.

Analogue electronic circuits↗

Electric Drive Technologies Consortium (EDTC)/ Cost competitive, high-Performance, highly Reliable (CPR) Power Devices on 4H-SiC (Final Report)

4H-Silicon carbide (4H-SiC) is a wide bandgap semiconductor that offers superior material properties over silicon, including higher critical electric field, thermal conductivity, and electron saturation velocity. These advantages make 4H-SiC highly attractive for high-voltage, high-efficiency power electronics. However, realizing the full potential of SiC requires device technologies that are not only high-performing but also manufacturable and reliable under real-world operating conditions. This report summarizes the outcomes of a five-year R&D effort funded by the U.S. Department of Energy (DOE) under the Electric Drive Technologies Consortium (EDTC), focused on developing cost-competitive, high-performance, and highly reliable (CPR) power devices on 4H-SiC substrates. The program targeted scalable and manufacturable 1.2 kV-class SiC MOSFETs optimized for next-generation electric vehicles, renewable energy systems, and industrial power conversion. The project delivered transformative advancements in SiC power device performance and ruggedness. Particularly, Specific on-resistance (R on,sp ) was reduced by up to 37%, from ~4.0 m$\Omega \cdot$cm 2 in earlier designs to an industry-leading 2.40 m$\Omega \cdot$cm 2 , driven by optimized doping, refined JFET widths, and layout engineering. Breakdown voltages (BV) exceeded 1600 V, marking improvement over legacy baselines, and demonstrating the robustness of newly implemented junction profiles and edge terminations. Short-circuit withstand time (SCWT) saw a remarkable 4$\times$ increase, from ~2 $\mu$s to over 8 $\mu$s, achieved through the successful deployment of deep P-well structures (~1.8–2.0 $\mu$m) via channeling implantation. This innovative process breakthrough enabled precise junction formation without MeV-class implantation tools, reduced leakage under high field stress, and allowed even the shortest-channel devices (down to 0.3 $\mu$m) to achieve both high BV and excellent ruggedness—breaking the traditional trade-off between conduction efficiency and blocking capability. Several novel architectures pushed the performance envelope further. JBSFETs—featuring embedded Schottky portions—eliminated bipolar degradation and drastically reduced third-quadrant leakage, while Ladder MOSFETs introduced a clever orthogonal conduction path that achieved a 15.4% reduction in R on,sp over standard linear designs. Switching performance reached new benchmarks: short-channel devices showed a 31% reduction in total switching energy compared to 0.5 $\mu$m counterparts, while maintaining manageable gate drive requirements. Layout-optimized structures not only improved transconductance but also accelerated switching transitions, pointing to real-world benefits in converter-level efficiency. The devices also passed rigorous reliability validation. Stress-tested across TDDB, HTGB, HTRB, HVP, and burn-in, the devices screened under 30 V/10 hr and 43 V/1 s protocols consistently exhibited tighter lifetime distributions and long-term oxide robustness. These screening techniques proved effective in identifying latent defects and ensuring deployment-grade reliability. Meanwhile, advanced 3D TCAD simulations revealed and resolved electric field hotspots—particularly in HEXFET corners—where fields exceeding 4.8 MV/cm were mitigated through geometry-aware layout corrections. Overall, the results of this project demonstrate a manufacturable and scalable SiC power device platform that addresses key DOE performance targets for efficient, robust, and reliable 1.2kV 4H-SiC Power Devices. The developed technologies represent a meaningful step forward in the commercial readiness of high-voltage SiC solutions and provide a strong foundation for continued advancement in wide bandgap power electronics.

42 ENGINEERING↗

Verification of the REBUS Software

Ongoing design activities at Argonne National Laboratory are requiring a thorough verification of the Argonne Reactor Computation codes be performed. REBUS is central to this system. The driver for this effort requires the Triangular-Z and hexagonal-Z core geometry options of REBUS to be verified. Previous work identified the REBUS features required to be verified to support current design activities, features of which are generally applicable to hexagonal-Z fast reactor designs. The scope of this verification effort includes verifying REBUS’s ability to correctly intepret the user input model, verifying that the features identified yield the intended results, and verifying the correctness of the REBUS output tables. The REBUS software verification relies heavily upon the accuracy of the embedded DIF3D software, the verification of which was completed and documented elsewhere. Given that DIF3D produces an accurate solution, the primary focus of the verification in the REBUS software is to ensure that it properly uses the DIF3D solution and that the depletion system (Bateman equations) are correctly implemented. This manuscript reiterates the verification tasks and displays results with respect to the features needed for current design activities. Analytic solutions of the Batemen equations are displayed and the results calculated with REBUS are displayed demonstrating the accuracy. Since coupled Bateman and neutron diffusion/transport solutions are extremely difficult to obtain, much of the focus is placed on how REBUS uses a given DIF3D solution assuming the accuracy of the DIF3D solution. The verification effort identified no issues that are debilitating or otherwise impactful to the design usage of REBUS, and thus REBUS version 11.0, release 3012 is considered verified. It is important to note that several outputs of REBUS are identified to be inaccurate, such as burnup in MWD/MT. Most of the relevant ones for VTR are generally accurate with 10-20% errors which is not impactful as all regular REBUS users are aware of this issue and know how to hand calculate the results. The REBUS manual further makes it clear that these values are consistent with the methodology being used by REBUS and thus the “errors” are more of an inconsistent definition with respect to what a user would expect given a definition in literature. Other issues that were identified included unclear documentation and software bugs all of which were inconsequential to the final results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗

Self-Assembled TiN-Metal Nanocomposites Integrated on Flexible Mica Substrates towards Flexible Devices

The integration of nanocomposite thin films with combined multifunctionalities on flexible substrates is desired for flexible device design and applications. For example, combined plasmonic and magnetic properties could lead to unique optical switchable magnetic devices and sensors. In this work, a multiphase TiN-Au-Ni nanocomposite system with core–shell-like Au-Ni nanopillars embedded in a TiN matrix has been demonstrated on flexible mica substrates. The three-phase nanocomposite film has been compared with its single metal nanocomposite counterparts, i.e., TiN-Au and TiN-Ni. Magnetic measurement results suggest that both TiN-Au-Ni/mica and TiN-Ni/mica present room-temperature ferromagnetic property. Tunable plasmonic property has been achieved by varying the metallic component of the nanocomposite films. The cyclic bending test was performed to verify the property reliability of the flexible nanocomposite thin films upon bending. This work opens a new path for integrating complex nitride-based nanocomposite designs on mica towards multifunctional flexible nanodevice applications.

42 ENGINEERING↗

Quantification of modeling uncertainty in the Rayleigh damping model

Understanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper proposes a systematic approach to quantify the uncertainty associated with Rayleigh's damping model. Bayesian calibration with embedded model error is employed to treat the coefficients of the Rayleigh model as random variables using modal damping ratios. Through a numerical example, we illustrate how this approach works and how the calibrated model can address modeling uncertainty associated with the Rayleigh damping model.

42 ENGINEERING↗

Integrating science for water security governance

Hydrological extremes are intensifying globally, increasing the complexity of decisions required to ensure water security. Advances in hydrological science, modeling, and data systems have expanded the technical frontier of water research, yet uptake of scientific insights in policy and management decisions remains limited. This persistent science–policy gap is not primarily a failure of knowledge generation or robustness, but an institutional challenge shaped by how scientific and governance systems are organized, coordinated, and connected to support the effective use of scientific knowledge. These challenges are particularly pronounced in multi-level and transboundary water governance, where decisions span jurisdictions and require coordination across institutional and political boundaries. We synthesize research at the science–policy interface and evidence from water security initiatives to show how institutional arrangements, scientific tool development, and research practices enable or constrain the sustained use of scientific knowledge in water-security governance processes. Building on these insights, we develop ‘shared decision infrastructure’ as a framing to describe how scientific knowledge is embedded within the institutional, relational, and procedural arrangements that connect science to decision-making processes over time. We translate this framing into a practical intervention roadmap centered on institutional design, tool translation, sustained co-production, and outcome-oriented evaluation to support the integration of science into ongoing governance processes. By positioning science as shared decision infrastructure, the roadmap clarifies how researchers can design scientific efforts that support more coordinated, accountable, and adaptive water security decisions amid deepening uncertainty.

M whitney, Kristen [NASA Goddard Space Flight Cent↗

Covalent Drug Binding in Live Cells Monitored by Mid-Infrared Quantum Cascade Laser Spectroscopy: Photoactive Yellow Protein as a Model System

The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Here, using this approach, we detect small-molecule binding in Escherichia coli, with particular focus on the interaction between para-Coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm –1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometer’s ability to enhance sensitivity for monitoring drug–protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research.

chromophores↗

Investigating resource-efficient neutron/gamma classification ML models targeting eFPGAs

There has been considerable interest and resulting progress in implementing machine learning (ML) models in hardware over the last several years from the particle and nuclear physics communities. A big driver has been the release of the Python package, hls4ml, which has enabled porting models specified and trained using Python ML libraries to register transfer level (RTL) code. So far, the primary end targets have been commercial field-programmable gate arrays (FPGAs) or synthesized custom blocks on application specific integrated circuits (ASICs). However, recent developments in open-source embedded FPGA (eFPGA) frameworks now provide an alternate, more flexible pathway for implementing ML models in hardware. These customized eFPGA fabrics can be integrated as part of an overall chip design. In general, the decision between a fully custom, eFPGA, or commercial FPGA ML implementation will depend on the details of the end-use application. In this work, we explored the parameter space for eFPGA implementations of fully-connected neural network (fcNN) and boosted decision tree (BDT) models using the task of neutron/gamma classification with a specific focus on resource efficiency. We used data collected using an AmBe sealed source incident on Stilbene, which was optically coupled to an OnSemi J-series silicon photomultiplier (SiPM) to generate training and test data for this study. We investigated relevant input features and the effects of bit-resolution and sampling rate as well as trade-offs in hyperparameters for both ML architectures while tracking total resource usage. The performance metric used to track model performance was the calculated neutron efficiency at a gamma leakage of 10 -3 . The results of the study will be used to aid the specification of an eFPGA fabric, which will be integrated as part of a test chip.

47 OTHER INSTRUMENTATION↗

Decision-tree structures utilizing a phase-transition material

The rich internal physics due to competing electronic phases present in phase-transition materials such as VO2 offer the potential for compact building block design for emerging non-von Neumann computing technologies. Here, based on the relaxation dynamics of an insulator-metal phase transition, we demonstrate experimentally a decision-tree classifier embedded within a single volatile resistive switching device. The tree is constructed by the combination of the voltage pulse and relaxation time and can adapt to different tasks. We use machine learning to analyze the relaxation process, enabling a predictive voltage-relaxation time phase diagram for the electrical resistance state. Classification of the etiology of the chronic cough is presented as a proof-of-principle use case. Further, our approach can be generalized to broader classes of solid-state and solid-liquid interfacial systems that demonstrate a variety of phase relaxations.

36 MATERIALS SCIENCE↗

AI-Powered Knowledge Graphs for Neuromorphic and Energy-Efficient Computing

The surge in scientific literature obscures breakthroughs and hinders the discovery of new research paths. We propose an artificial intelligence (AI) powered framework using large language models (LLMs) and knowledge graphs (KGs) to automate parts of scientific discovery, focusing on energy-efficient AI circuits. Our hybrid approach combines LLMs, structured data, and ontology-based reasoning to construct a comprehensive knowledge graph that integrates insights across computational neuroscience, spiking neuron models, learning rules, architectural motifs, and neuromorphic device technologies. This multi-domain representation enables the generation of hypotheses that connect biological function with implementable, energy-efficient hardware architectures. Using KG embeddings and graph neural networks, the framework generates hypotheses for novel circuits, validates them through optimization on exascale HPC systems, and with tools like SuperNeuro and Fugu, the most promising designs will be prototyped in hardware. This open-source system aims to accelerate discoveries and bridging neuroscience with hardware innovation, drive collaboration, and unlock new opportunities in low-power AI computing.

Gautam, Ashish [ORNL]↗

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

36 MATERIALS SCIENCE↗

Conditional guide RNA deactivation by mRNA and small molecule triggers in Saccharomyces cerevisiae

CRISPR interference (CRISPRi) technologies have revolutionized bioengineering by providing precise tools for gene expression modulation, enabling targeted gene perturbation and metabolic pathway optimization. Despite these advances, achieving dynamic control over gene expression by CRISPR-based regulation remains a challenge due to its inherently static nature. Utilizing toehold-mediated strand displacement and ligand-responsive ribozymes (aptazymes), this study introduces switchable guide RNAs (gRNAs) that facilitate tunable gene expression mediated by mRNA or small molecule signals. We demonstrate complete silencing of gRNA via strategically designed 5’ or 3’ extensions that impede the gRNA spacer or the dCas9 handle, with subsequent restoration of function through sequestration or cleavage of the obstructive sequence. The resulting toehold-embedded or aptazyme-embedded gRNAs can be deactivated by specific signals, including two full-length translatable mRNAs and two small molecule triggers, thereby lifting CRISPRi repression on targeted genes. This modular approach allows for gRNA-based biocomputing through multi-layer or multi-input genetic logic gates in Saccharomyces cerevisiae . Offering a versatile strategy for post-CRISPR regulation in response to environmental signals or cellular states, this methodology expands the toolkit in eukaryotic systems for reversible control of gene expression.

Aptazyme↗

Modeling separation of lanthanides via heterogeneous ligand binding

Individual lanthanide elements have physical/electronic/magnetic properties that make each useful for specific applications. Several of the lanthanides cations (Ln 3+ ) naturally occur together in the same ores. They are notoriously difficult to separate from each other due to their chemical similarity. Predicting the Ln 3+ differential binding energies (ΔΔE) or free energies (ΔΔG) at different binding sites, which are key figures of merit for separation applications, will help design of materials with lanthanide selectivity. We apply ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) to calculate ΔΔG for Ln 3+ coordinated to ligands in water and embedded in metal–organic frameworks (MOFs), and ΔΔE for Ln 3+ bonded to functionalized silica surfaces, thus circumventing the need for the computational costly absolute binding (free) energies ΔG and ΔE. Perturbative AIMD simulations of water-inundated simulation cells are applied to examine the selectivity of ligands towards adjacent Ln 3+ in the periodic table. Static DFT calculations with a full Ln 3+ first coordination shell, while less rigorous, show that all ligands examined with net negative charges are more selective towards the heavier lanthanides than a charge-neutral coordination shell made up of water molecules. Amine groups are predicted to be poor ligands for lanthanide-binding. Finally, we also address cooperative ion binding, i.e., using different ligands in concert to enhance lanthanide selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An Instrumented Capsule Design to Measure Thermal Conductivity in Miniature UO2 Specimens

Numerous separate effects irradiations of miniature nuclear fuel specimens have been conducted in the High Flux Isotope Reactor (HFIR) under the experimental platform designated as MiniFuel. MiniFuel is a static irradiation capability in which microstructural evolution and fuel performance phenomena are observed during postirradiation examination thereby offering a snapshot of the terminal fuel characteristics. This approach inherently requires fielding an irradiation where the experimental conditions are determined using predictive models and the pertinent outcomes are measured at the end of the test. Static irradiations can provide useful insights to the relationships between fuel performance and the pivotal irradiation conditions, namely temperature and burnup, but the ability to monitor fuel performance in situ would further support fuel development and qualification. To this end, an instrumented experiment design is being developed at Oak Ridge National Laboratory to capture thermal conductivity degradation and fission gas release during HFIR irradiation. These phenomena will be monitored using unique capsule designs that each target a different phenomenon. This paper details the thermal conductivity capsule (TCC) design and its expected performance envelope as determined using computer models. Each TCC will contain a miniature UO2 disc specimen (~0.5 mm thick × 5 mm diameter) sandwiched between metallic slugs with embedded thermocouples. The coupling of in situ temperature measurements, known thermal conductivity of the metallic components, and heat generation rates computed using high-fidelity neutronics models make the thermal conductivity measurement possible. This paper describes the reactor physics and heat transfer models used to predict the capsule’s performance and the methodology for calculating the fuel specimen’s thermal conductivity from the thermocouple measurements.

Gorton, Jacob [ORNL] (ORCID:0000000269806083)↗

Visualization for Insight and Data Analysis in Energy Research

This talk explores how advanced visualization technologies are transforming analytical reasoning and knowledge discovery in energy research, drawing on recent work at the National Laboratory of the Rockies' Computational Science Center. Through a series of scientific case studies, we demonstrate how immersive and high-resolution visualization environments enable scientists and engineers to identify previously unseen patterns and features - insights that often remain hidden in traditional desktop-based analysis. By embedding richer information into interactive analytics tools, these approaches support the exploration of complex, multivariate parameter spaces, where interaction itself catalyzes understanding. Beyond capability, we emphasize the critical role of visualization design grounded in perception and cognition, showing how visual encodings directly influence analytical outcomes. Spanning applications from materials science to integrated energy systems, these visualization approaches accelerate innovation and improve decision-making by enabling deeper, more reliable insight into increasingly complex energy data.

97 MATHEMATICS AND COMPUTING↗

Hot electron preheat effects in two-photon polymerization plastic lattice laser-driven shock-tube experiments on the OMEGA laser

We present simulations of Omega shock tube experiments designed to investigate hot electron preheat effects in 3D-printed, two-photon polymerization (2PP) plastic lattices. Preheat is inferred in the experiments from the expansion of a plastic witness disk embedded in the lattice. Using the Eulerian radiation-hydrodynamics code xRAGE, we model shock propagation and preheat from both radiative and hot electron energy sources to evaluate their relative impact. To simulate the transport of laser-generated hot electrons, the nonlocal electron heat transport model proposed by Schurtz, Nicolaï, and Busquet (SNB) is extended with a hot electron source term and an energy cascade algorithm. We explore how variations in ablator, lattice geometry, and laser drive affect the shock velocity and witness disk expansion. Simulations show that the inclusion of a 5 μm gold layer reduces shock pressure by 60% and shock speeds by 30%–40% but does not significantly reduce the hot electron preheat, and that different lattice geometries lead to enhanced shock velocities—up to 40% faster than in homogeneous foams. However, radiative and conductive preheat from classical mechanisms alone fail to match experiment. By including a hot electron source term, we reproduce experimental observables such as disk expansion rates and spatial radiographic features. We find that a hot electron population corresponding to 4%–8% of the incident laser energy with T hot = 50 keV produces expansion which agrees with the experimental data, suggesting hot electron preheat is the most plausible explanation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Plasmon-Driven Ammonia Decomposition on Pd(111): Hole Transfer’s Role in Changing Rate-Limiting Steps

Here, ammonia (NH 3 ) has the potential to be a hydrogen carrier because it can be transported and stored with ease, but only if it also can be decomposed easily when needed. Understanding how to control the frequently rate-limiting N–H bond breaking and N–N bond forming on catalytic surfaces may help design efficient means for NH 3 decomposition. Yuan et al. recently demonstrated photocatalytically selective N–H bond breaking in NH 3 on plasmon-driven aluminum–palladium (Al–Pd) antenna–reactor heterostructures. Using embedded correlated wavefunction (ECW) theory, we predict that the rate-determining step (RDS) for NH 3 decomposition on Pd(111) via thermocatalysis (dissociating the first N–H bond, *NH 3 → *NH 2 + *H, in the ground state, where * means adsorbed) differs from that via photocatalysis (dissociating the second N–H bond, *NH 2 → *NH + *H, in the excited state). This result is consistent with the measured catalytic efficiency and selectivity of NH 3 -deuterium (D 2 ) exchange reactions (an indirect way to measure N–H bond breaking) on Al–Pd heterodimers. We also determine the origin of the observed selectivity of thermocatalysis and photocatalysis on Pd(111) toward doubly deuterated (NHD 2 ) and monodeuterated (NH 2 D) products, respectively, and explore viability of the full NH 3 decomposition path, also via ECW theory. Additionally, we predict that the associative desorption of *N as N 2 from Pd(111) is extremely difficult in thermocatalysis at least at low surface coverages; metal-to-adsorbate hole transfer in photocatalysis stabilizes the transition state for the first N–H bond dissociation, shifting the RDS to the second N–H bond breaking. Furthermore, the redistribution of electrons around *N upon excitation reduces the electron density in the Pd–N bonds, which may lower the barrier for N 2 associative desorption in photocatalysis. Thus, light-induced, plasmon-mediated, excited-state hole transfer may provide an efficient mechanism to accelerate NH 3 decomposition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗