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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Imaging Charged Exciton Localization in van der Waals WSe 2 /MoSe 2 Heterobilayers

Exciton localization in transition-metal dichalcogenide monolayers is behind a variety of interesting phenomena and applications, including broad-spectrum solar cells and singlephoton emissions. Strain fields at the periphery of topographically distinct features such as nanoscopic bubbles were recently associated with localized charge-neutral excitons. Here, we use tip-enhanced photoluminescence (PL) to visualize excitons in WSe 2 /MoSe 2 heterobilayers (HBL). We find strong optical emission from charged excitons, particularly positively charged trions, in HBL supported by interlayer charge transfer. Our results reveal strong trion confinement, with a localization length scale comparable to the trion size, at the apex region inside individual nanoscopic bubbles. Nano-PL mapping also shows sub-10 nm spatial variations in the localized trion emission spectra, which stem from atomic-scale potential energy fluctuations. These findings demonstrate the possibility of confining charged exciton complexes that are electrically tunable, opening up further opportunities to probe many-body exciton physics, and to explore additional possible sites for strong exciton localization that can lead to quantum emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning

X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of the thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity and X-ray diffraction simulations is used to generate training data for machine-learning (Gaussian process regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Model-based Reconstruction for Single Particle Cryo-Electron Microscopy

Single particle cryo-electron microscopy is a vital tool for 3D characterization of protein structures. A typical workflow involves acquiring projection images of a collection of randomly oriented particles, picking and classifying individual particle projections by orientation, and finally using the individual particle projections to reconstruct a 3D map of the electron density profile. The reconstruction is challenging because of the low signal-to-noise ratio of the data, the unknown orientation of the particles, and the sparsity of data especially when dealing with flexible proteins where there may not be sufficient data corresponding to each class to obtain an accurate reconstruction using standard algorithms. In this paper we present a model-based image reconstruction technique that uses a regularized cost function to reconstruct the 3D density map by assuming known orientations for the particles. Our method casts the reconstruction as minimizing a cost function involving a novel forward model term that accounts for the contrast transfer function of the microscope, the orientation of the particles and the center of rotation offsets. We combine the forward model term with a regularizer that enforces desirable properties in the volume to be reconstructed. Using simulated data, we demonstrate how our method can significantly improve upon the typically used approach.

Venkatakrishnan, Singanallur↗
DOE OSTI2022

Kinematic Evidence for an Embedded Planet in the IM Lupi Disk

Abstract We test the hypothesis that an embedded giant planet in the IM Lupi protostellar disk can produce velocity kinks seen in CO line observations as well as the spiral arms seen in scattered light and continuum emission. We inject planets into 3D hydrodynamics simulations of IM Lupi, generating synthetic observations using Monte Carlo radiative transfer. We find that an embedded planet of 2–3 M Jup can reproduce non-Keplerian velocity perturbations, or “kinks”, in the 12 CO J = 2–1 channel maps. Such a planet can also explain the spiral arms seen in 1.25 mm dust continuum emission and 1.6 μ m scattered-light images. We show that the wake of the planet can be traced in the observed peak velocity map, which appears to closely follow the morphology expected from our simulations and from analytic models of planet–disk interaction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

INL Senior Project

What did my team set out to accomplish:? Can I put a custom Machine Learning Model on FPGA?? Can I analyze network traffic in real time?? Can a QSFP port be used with an FPGA?? Does a visual representation of the latent space enhance our understanding of network traffic?? What is QSFP QSFP (Quad Small Form-Factor Pluggable)? QSFP supports transfer speeds generally up to 100Gb/s? Runs 4 parallel lines running up to 28 Gb/s? Why the latent space is important to our project ?Latent space is the compressed mapping of data points in a non-linear fashion? Create an understanding of the relationship of data collected? Can represent that relationship of a single network packet in 3 points (X, Y, Z) Project Outline FPGA? Custom Xilinx Petalinux Image for the operating system? Python program to collect packets and run them through the DPU (Data Processing Unit)? The program then sends the information over a socket to a computer? Display Program? Python Program that collects the information sent from the FPGA and display it in a graph

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Transfer learning of neural surrogates on multifidelity groundwater simulations

Multifidelity data used in the paper published in Advances in Water Resources 206 (2025) 105140, https://doi.org/10.1016/j.advwatres.2025.105140 The code used to process the data is openly available on GitHub at https://github.com/Model-Reduction-and-UQ-Group/Transfer_Learning_K_reconstruction Computationally inexpensive surrogates of process-based models, such as deep neural networks, enable ensemble-based computations used in risk assessment, data assimilation, etc. However, generation of large datasets required to train a neural network can be as expensive as the ensemble simulations themselves. We ameliorate this challenge by using data from multifidelity (MF) groundwater simulations and transfer learning (TL) to reduce data generation costs while maintaining model accuracy. As a computational example, we train a deep convolutional neural network (CNN) to reconstruct permeability fields from saturation maps derived from a multiphase flow model. Starting with very low- and low-fidelity data generated on increasingly coarse meshes, we pretrain the CNN, followed by output-layer training and fine-tuning using only a limited number of high-fidelity samples. We demonstrate the surrogate’s robustness when interpreting low-quality inputs—such as interpolated maps or data affected by noise—which has strong implications for the applicability in practical hydrogeological scenarios. This multilevel MF-TL strategy achieves a favorable trade-off between computational efficiency and predictive accuracy, significantly outperforming high-fidelity-only approaches under the same computational budget.

Chiofalo, Alessia [University of Bologna] (ORCID:0↗
DOE OSTI2020

Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part I - Environment Mapping

When characterizing facilities, advanced autonomous systems are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. One main focus of the research is the visual mapping of the environment.This paper presents a framework for the mapping aspect that utilizes lidar and SLAM technologies coupled with a monocular camera to create a point cloud of the environment. Upon completion, the present research work aims to provide an autonomous mobile robot the capability to localize itself and aid the user with visualizing the radiation data. Challenges: C++ node needs to properly synchronize image and point cloud data. 3D lidar has resolution of ± 3 cm. Proper camera calibration needed. Transforms need to be precise for proper alignment of image and point-cloud. Results: Voxelated point clouds. Accurate colors from calibrated cameras including details smaller than lidar resolution. Developed Robotics Operating System (ROS) package for ease of transfer to other robot platforms.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Kelvin probe force microscopy under ambient conditions

Kelvin probe force microscopy (KPFM) is a technique derived from atomic force microscopy that provides maps of surface potential or work function differences across material systems, with nanometre-scale resolution. KPFM is a useful tool for investigating electrical phenomena such as dipole orientation, interfacial charge transfer, charge accumulation, band bending and doping levels. This Primer aims to provide an overview of typical ambient-condition KPFM measurements, covering their underlying principles, experimental implementations and wide-ranging applications. Key KPFM variants, including amplitude and frequency modulation, heterodyne detection schemes and innovative open loop and pulsed force techniques, are discussed, with practical guidance on optimizing signal acquisition and reducing errors. Specialized approaches, such as time-resolved KPFM and multimodal KPFM, are discussed for their ability to capture dynamic charge processes and chemical information, respectively. Here, we highlight recent advances in KPFM applications, spanning metal alloys, soft matter, ferroelectrics, photovoltaics and 2D materials, showcasing its versatility across research domains. By addressing current limitations and identifying future opportunities, this Primer underscores the transformative potential of KPFM in advancing the understanding of nanoscale electrical phenomena.

Zahmatkeshsaredorahi, Amirhossein [Lehigh Univ., B↗
DOE OSTI2020

Dominant effect of anharmonicity on the equation of state and thermal conductivity of MgO under extreme conditions

Understanding heat transfer in the earth mantle region is of great scientific interest but highly challenging due to dissimilar conditions from our daily lives. Thermal conductivity of highly pressured and thermally activated materials can be completely different from that at ambient condition. In this study we calculate the equation of state and thermal conductivity of cubic crystalline MgO, a major component of the mantle, in such an environment. It is shown that the material properties are not accurately captured unless rigorous treatment of anharmonicity of phonon vibrational motion is explicitly applied. To predict renormalized phonon dispersion relations, interatomic force constants of high-order expansion are required, especially at elevated temperature. The anharmonicity plays an increasingly substantial role for regulating volume expansion as temperature rises, because the Gibbs free energy of MgO considerably deviates from what was predicted by the quasiharmonic approximation. We find that the lattice thermal conductivity of MgO and the heat transfer of the earth's mantle are reliably estimated only when both the temperature-dependent renormalization of phonon frequencies and four-phonon scattering effects are taken into consideration. We map the characteristics at Geotherm into contour diagrams in which equilibrium volumes and the thermal conductivities are described as functions of temperature and pressure. Furthermore, the thermal conductivity values of MgO in this study are smaller than those in previous theoretical studies due to the additional incorporation of four-phonon scatterings in the calculations. Specifically, deviation between the values in previous studies and in this study becomes larger as thermodynamic variables reach those near the core-mantle boundary. For instance, thermal conductivities of 52.15 and 23.67 Wm -1 K -1 are estimated at ambient and extreme conditions (T = 4000K and P = 135 GPa), respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accelerated thermal property mapping of TRISO advanced nuclear fuel

TRistructural ISOtropic (TRISO) fuel is a leading-edge nuclear fuel form representing a departure from the more traditional nuclear fuel forms utilized in the reactor fleet of today. Rather than a monolithic fuel pellet of uranium dioxide, integral fuel forms containing TRISO fuel are composed of thousands of microencapsulated uranium-bearing fuel kernels and individually coated with multiple layers of pyrolytic carbon and silicon carbide. These multilayered ceramic coatings serve as an environmental barrier to ensure radioactive and chemically reactive fission products are contained within the reactor fuel elements, but also participate in the transfer of heat generated in the nuclear fuel to the coolant – the primary purpose of a nuclear reactor. Since traditional thermal property measurement techniques, such as laser flash analysis, would be unable to resolve the thermal properties of the individual TRISO coating layers, a simplified frequency-domain thermoreflectance technique has been developed to rapidly map the thermal properties of TRISO particles. Using this technique, the thermal properties of TRISO particles have been mapped from room temperature up to 1000 °C to examine the spatial variation and temperature-dependency of the thermal properties within each layer. Additionally, spatial-domain thermoreflectance was used to examine the anisotropy of the thermal properties for each layer at different locations within a single TRISO particle, and across multiple TRISO particles to assess the intra- and inter-particle uniformity of thermal properties, respectively. To elucidate the underlying causes for the measured variations in thermal properties, scanning electron microscopy and Raman spectroscopy were used to examine variations in microstructure and chemical bonding within the different coating layers. Results from this work are then compared with previous examinations of TRISO fuel particles and microstructurally driven mechanisms for the variations in the measured thermal properties of the different carbonaceous layers are discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Trust-Enhancing Probabilistic Transfer Learning for Sparse and Noisy Data Environments

There is an increasing aspiration to utilize machine learning (ML) for various tasks of relevance to national security. ML models have thus far been mostly applied to tasks and domains that, while impactful, have sufficient volume of data. For predictive tasks of national security relevance, ML models of great capacity (ability to approximate nonlinear trends in input-output maps) are often needed to capture the complex underlying physics. However, scientific problems of relevance to national security are often accompanied by various sources of sparse and/or incomplete data, including experiments and simulations, across different regimes of operation, of varying degrees of fidelity, and include noise with different characteristics and/or intensity. State-of-the-art ML models, despite exhibiting superior performance on the task and domain they were trained on, may suffer detrimental loss in performance in such sparse data environments. This report summarizes the results of the Laboratory Directed Research and Development project entitled Trust-Enhancing Probabilistic Transfer Learning for Sparse and Noisy Data Environments. The objective of the project was to develop a new transfer learning (TL) framework that aims to adaptively blend the data across different sources in tackling one task of interest, resulting in enhanced trustworthiness of ML models for mission- and safety-critical systems. The proposed framework determines when it is worth applying TL and how much knowledge is to be transferred, despite uncontrollable uncertainties. The framework accomplishes this by leveraging concepts and techniques from the fields of Bayesian inverse modeling and uncertainty quantification, relying on strong mathematical foundations of probability and measure theories to devise new uncertainty-aware TL workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Orbital Engineering Mediated by Cation Conjugation in Luminescent Uranyl–Organic Hybrid Materials

A series of compounds of the form [HAr]2[UO2X4] is reported here, wherein Ar is systematically varied between pyridine (1-X), quinoline (2-X), acridine (3-X), 2,5-dimethylpyrazine (4-X), quinoxaline (5-X), and phenazine (6-X), and X = Cl or Br. With greater conjugation in the organic cation, a larger quenching in uranyl luminescence is observed in the solid state. Supporting our luminescence experiments with computation, we map out the potential energy diagrams for the singlet and triplet states of both the [HAr]+ cations and [UO2Cl4]2– anion in the crystalline state, and of the assembly. The distinct energy transfer pathways in each compound are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Dissolution of periodic arrays of grains: Upscaling of pore-scale simulations with fast reactions

Upscaling methods are frequently used to derive transport equations at the macroscopic scale from more fundamental equations formulated at the pore scale. These methods typically give a suitable structure for the macroscopic equations and can also provide explicit expressions for the constitutive parameters, such as permeability and dispersion coefficients. Introducing chemical reactions complicates upscaling in at least two important ways. First, the interplay between chemical reactions and transport processes introduces a new length scale, which can be much smaller than the convective or dispersive length scales. A small reactive length scale breaks one of the key assumptions in upscaling; that there is a significant separation in length between the pore-scale and macro-scale processes. The second complication is that if reactions take place at mineral surfaces (dissolution or precipitation) then the pore space itself is evolving in time. In this paper we suggest ways in which these difficulties can be approached, based on analysis of pore-scale simulation data. First, we noticed that the concentration field in successive unit cells has an almost identical spatial variation, with a single scaling factor for each unit cell that is proportional to the incoming reactant flux. Using pore-scale simulations to determine the mass transfer coefficient in a few unit cells, we can calculate the concentration field in the whole domain, even when dissolution is entirely transport limited. Second, we have noticed a time-dependent mapping of the grain shapes from different unit cells. From these observations, we can deduce constitutive relations where the only time-varying parameter is the porosity. Here, we show that a model based on these ideas can quantitatively account for the pore-scale simulation data.

58 GEOSCIENCES↗
DOE OSTI2020

In Vivo Molecular Insights into Syntrophic Geobacter Aggregates

Direct interspecies electron transfer (DIET) has been considered as a novel and highly efficient strategy in both natural anaerobic environments and artificial microbial fuel cells. A syntrophic model consisting of Geobacter metallireducens and Geobacter sulfurreducens was studied in this work. We conducted in vivo molecular mapping of the outer surface of the syntrophic community as the interface of nutrients and energy exchange. System for Analysis at the Liquid Vacuum Interface combined with time-of-flight secondary ion mass spectrometry was employed to capture the molecular distribution of syntrophic Geobacter communities in the living and hydrated state. Principal component analysis with selected peaks revealed that syntrophic Geobacter aggregates were well differentiated from other control samples, including syntrophic planktonic cells, pure cultured planktonic cells, and single population biofilms. Our in vivo imaging indicated that a unique molecular surface was formed. Specifically, aromatic amino acids, phosphatidylethanolamine components, and large water clusters were identified as key components that favored the DIET of syntrophic Geobacter aggregates. Moreover, the molecular changes in depths of the Geobacter aggregates were captured using dynamic depth profiling. Our findings shed new light on the interface components supporting electron transfer in syntrophic communities based on in vivo molecular imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Validation of 2D Te and ne measurements made with Helium imaging spectroscopy in the volume of the TCV divertor

Abstract Multi-spectral imaging of helium atomic emission (HeMSI) has been used to create 2D poloidal maps of T e and n e in TCV’s divertor. To achieve these measurements, TCV’s MANTIS multispectral cameras (Perek et al 2019 Rev. Sci. Instrum. 90 123514) simultaneously imaged four He I lines (two singlet and two triplet) and a He II line (468 nm) from passively present He and He + . The images, which were absolutely calibrated and covered the whole divertor region, were inverted through the assumption of toroidal symmetry to create emissivity profiles and, consequently, line-ratio profiles. A collisional-radiative model (CRM) was applied to the line-ratio profiles to produce 2D poloidal maps of T e and n e . The collisional-radiative modeling was accomplished with the Goto helium CRM code (Zholobenko et al 2018 Nucl. Fusion 58 126006, Zholobenko et al 2018 Technical Report , Goto 2003 J. Quant. Spectrosc. Radiat. Transfer 76 331–44) which accounts for electron-impact excitation (EIE) and deexcitation, and electron–ion recombination (EIR) with He + . The HeMSI T e and n e measurements were compared with co-local Thomson scattering measurements. The two sets of measurements exhibited good agreement for ionizing plasmas: ( 5 eV ⩽ T e ⩽ 60 eV , and 2 × 10 18 m − 3 ⩽ n e ⩽ 3 × 10 19 m − 3 ) in the case of majority helium plasmas, and ( 10 eV ⩽ T e ⩽ 40 eV , 2 × 10 18 m − 3 ⩽ n e ⩽ 3 × 10 19 m − 3 ) in the case of majority deuterium plasmas. However, there were instances where HeMSI measurements diverged from Thomson scattering. When T e ⩽ 10 eV in majority deuterium plasmas, HeMSI deduced inaccurately high values of T e . This disagreement cannot be rectified within the CRM’s EIE and EIR framework. Second, on sporadic occasions within the private flux region, HeMSI produced erroneously high measurements of n e . Multi-spectral imaging of Helium emission has been demonstrated to produce accurate 2D poloidal maps of T e and n e within the divertor of a tokamak for plasma conditions relevant to contemporary divertor studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Boiling Transitions During Droplet Contact on Superheated Nano/Micro-Structured Surfaces

Manipulating surface topography is one of the most promising strategies for increasing the efficiency of numerous industrial processes involving droplet contact with superheated surfaces. In such scenarios, the droplets may immediately boil upon contact, splash and boil, or could levitate on their own vapor in the Leidenfrost state. In this work we report the outcomes of water droplets coming in gentle contact with designed nano/micro-textured surfaces at a wide range of temperatures as observed using high-speed optical and X-ray imaging. Further, we report a paradoxical increase in the Leidenfrost temperature ($T_{\text{LFP}}$) as the texture spacing is reduced below a critical value (~10 μm) that represents a minima in $T_{\text{LFP}}$. Although droplets on such textured solids appear to boil upon contact, our studies suggest that their behavior is dominated by hydrodynamic instabilities implying that the increase in $T_{\text{LFP}}$ may not necessarily lead to enhanced heat transfer. On such surfaces, the droplets display a new regime characterized by splashing accompanied by a vapor jet penetrating through the droplets before they transition to the Leidenfrost state. We provide a comprehensive map of boiling behavior of droplets over a wide range of texture spacings that may have significant implications towards applications such as electronics cooling, spray cooling, nuclear reactor safety and containment of fire calamities.

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

Mapping Influenza-Induced Posttranslational Modifications on Histones from CD8+ T Cells

T cell function is determined by transcriptional networks that are regulated by epigenetic programming via posttranslational modifications (PTMs) to histone proteins and DNA. Bottom-up mass spectrometry (MS) can identify histone PTMs, whereas intact protein analysis by MS can detect species missed by bottom-up approaches. We used a novel approach of online two-dimensional liquid chromatography-tandem MS with high-resolution reversed-phase liquid chromatography (RPLC), alternating electron transfer dissociation (ETD) and collision-induced dissociation (CID) on precursor ions to maximize fragmentation of uniquely modified species. The first online RPLC separation sorted histone families, then RPLC or weak cation exchange hydrophilic interaction liquid chromatography (WCX-HILIC) separated species heavily clad in PTMs. Tentative identifications were assigned by matching proteoform masses to predicted theoretical masses that were verified with tandem MS. We used this innovative approach for histone-intact protein PTM mapping (HiPTMap) to identify and quantify proteoforms purified from CD8 T cells after in vivo influenza infection. Activation significantly altered PTMs following influenza infection, histone maps changed as T cells migrated to the site of infection, and T cells responding to secondary infections had significantly more transcription enhancing modifications. Thus, HiPTMap identified and quantified proteoforms and determined changes in CD8 T cell histone PTMs over the course of infection.

mass spectrometry↗
DOE OSTI2022

Allosteric control of electron transfer in nitrogenase and nitrogenase-like enzymes (Final Report)

More than a third of all known proteins to date are either oxidoreductases or metalloproteins. The diversity of metal cofactors and redox centers in these proteins make them attractive candidates for engineering protein-based biocatalysts, biosensors, and biofuel cells, among other necessary tools for the bioeconomy. Many of these enzymes function as large multi-subunit complexes arranged as symmetric or asymmetric assemblies, and the purpose of this architecture is unclear. The goal of the project was to address this fundamental question by investigating the mechanism of action of two oxidoreductases: Nitrogenase and DPOR (Dark-Operative Protochlorophyllide Oxidoreductase, a nitrogenase-like oxidoreductase). Nitrogenase and DPOR play key roles in nitrogen fixation and photosynthesis, processes essential to sustaining life. Both proteins are arranged as two symmetrical halves with each half capable of substrate reduction. For more than half a century, substrate reduction by nitrogenase has been presumed to occur independently within the two identical halves. Funded by this DOE grant we have now shown that both nitrogenase and DPOR function asymmetrically. Electron transfer in one half suppresses activity in the other. Thus, we have demonstrated that the two halves do not function in an independent manner; rather, catalytic events in one-half allosterically suppress activity and electron transfer (ET) in the other. This alternating-site mechanism has significant implications on our current understanding of substrate reduction by these enzymes and offers a new mechanistic paradigm for the reactions they catalyze. Our work now provides a mechanistic map of the how the two symmetric catalytic halves work together to establish and maintain the sequential-site mechanism through multiple ET cycles to achieve substrate reduction. There is substantive need for bio-inspired tools that enable reduction of nitrogen and other substrates, as well as photosynthesis, for energy and food production. Understanding the workings of enzymes like nitrogenase and DPOR that mediate multi-electron redox chemistry is vital for designing such synthetic tools. The timing of our research coincides with the biotechnological breakthrough of light-driven artificial N2 reduction by nitrogenase, achieved using the native enzyme with both halves arranged in a mirror-like architecture as catalyst. Together with such discoveries, our research now reveal unknown facets of the reaction mechanism and hold promise for designing more effective catalysts.

59 BASIC BIOLOGICAL SCIENCES↗