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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Development of an Alternative Approach to Demonstrating Environmental Performance in the UK - 19354

European basic safety standards require that exposure of humans to the potentially harmful effects of ionising radiation are optimised so that they are as low as reasonably achievable, taking account of economic and societal factors. In the United Kingdom the responsibility for ensuring that exposures to humans are optimised is split between two regulatory functions. The Office of Nuclear Regulation regulates exposures to workers in all scenarios and to members of the public in the event of an accident. Environmental regulators in each of the four nations comprising the United Kingdom regulate exposures to members of the public from normal operations. Since the early 1990's, all permissions granted to nuclear sites in the UK for the discharge/disposal of radioactive waste to the environment have contained conditions requiring holders to demonstrate that they minimise the generation of radioactive waste, apply appropriate abatement and discharge/dispose of it in a way that minimises impacts on members of the public and the environment. For many years, the industry standard for providing this demonstration, which was heavily influenced by regulatory expectations and feedback, was to undertake one or a series of multi-attribute decision assessments (MADA). Wood's team of environmental specialists has been involved in undertaking such MADA studies for many years. Since the mid-2000's, our team has become increasingly aware of some of the limitations of undertaking MADA to demonstrate environmental performance for large scale new nuclear build projects and, in particular, the next generation of new nuclear power stations that are being planned and implemented in the UK. We have collaborated with the vendors of reactor technologies, the prospective/owner operators of new nuclear power stations and the developers of new facilities on existing sites to develop an alternative approach to demonstrating environmental performance. Our alternative approach comprises three main pillars which are summarised as: - Evolution - benefit from progressive improvements, operational experience and recent permitting activities. Our alternative approach uses the claim, argument, evidence approach which is commonly used for nuclear safety cases and which allows us to 'tell the story' of how environmental performance has been achieved. We have successfully deployed this approach for all new nuclear power stations proposed in the UK. - Integration - ensure that optimisation is integrated into the project programme and forms part of the 'big picture'. Our alternative approach allows environmental performance to be managed better throughout the life cycle of the project and ensures that our outputs are 'living' documents and do not 'sit on the shelf'. We have successfully demonstrated how this can be applied during rationalisation of major schemes and significant design changes on nuclear new build projects. - Opportunity - identify the best time to deliver elements of the programme. Our alternative approach aims to minimise the overall time, trouble and costs associated with demonstrating environmental performance by ensuring that decisions are made at the time when they offer greatest benefit and by minimising unnecessary rework. Our advice has helped to reduce the project costs and minimise delays associated with regulatory queries and interventions. This paper describes the journey that we have taken with our clients in developing our alternative approach to demonstrating environmental performance. We explain the UK regulatory context and explore the need for change. We present a number of case studies related to new nuclear power stations and other new facilities on existing nuclear sites that demonstrate the successful application of our alternative approach. We provide examples of how we have successfully implemented the outputs of our alternative approach into project and corporate management arrangements. Finally, we examine feedback from clients, regulators and other interested parties to propose future refinements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Where Big Data and Prediction Meet

Our ability to assemble and analyze massive data sets, often referred to under the title of “big data”, is an increasingly important tool for shaping national policy. This in turn has introduced issues from privacy concerns to cyber security. But as IBM’s John Kelly emphasized in the last Innovation, making sense of the vast arrays of data will require radically new computing tools. In the past, technologies and tools for analysis of big data were viewed as quite different from the traditional realm of high performance computing (HPC) with its huge models of phenomena such as global climate or supporting the nuclear test moratorium. Looking ahead, this will change with very positive benefits for both worlds. Societal issues such as global security, economic planning and genetic analysis demand increased understanding that goes beyond existing data analysis and reduction. The modeling world often produces simulations that are complex compositions of mathematical models and experimental data. This has resulted in outstanding successes such as the annual assessment of the state of the US nuclear weapons stockpile without underground nuclear testing. Ironically, while there were historically many test conducted, this body of data provides only modest insight into the underlying physics of the system. A great deal of emphasis was thus placed on the level of confidence we can develop for the predictions. As data analytics and simulation come together, there is a growing need to assess the confidence levels in both data being gathered and the complex models used to make predictions. An example of this is assuring the security or optimizing the performance of critical infrastructure systems such as the power grid. If one wants to understand the vulnerabilities of the system or impacts of predicted threats, full scales tests of the grid against threat scenarios are unlikely. Preventive measures would need to be predicated on well-defined margins of confidence in order to take mitigating actions that could have wide ranging impacts. There is a rich opportunity for interaction and exchange between the HPC simulation and data analytics communities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Increasing dose gradient and uniformity in small fields using modulation: Theory and prototypes for cone-based stereotactic radiosurgery

Purpose: To investigate the theoretical limits to the tradeoff between dose gradient and uniformity when modulation is used in the context of cone based SRS, and to design a prototype collimation device that allows for steeper dose gradients and/or higher target uniformity as compared to a standard circular collimator. Methods: An inverse planning optimization is performed in the context of idealized phantom geometry to determine the ideal fluence pattern that best approximates a “rect function” dose distribution. Ideal fluence patterns were approximated in a prototype device and radiochromic film dosimetry was utilized to compare the prototype device to a standard circular collimator. Results: For choices of prescription isodose lines above approximately 50%, utilizing modulation allows for an improved tradeoff between dose gradient index and dose heterogeneity index. Compensators placed within the circular collimator can achieve the necessary modulation. Conclusions: Using modulation with features on a submillimeter distance scale, it is possible to increase the dose gradient and/or uniformity in small fields.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

A Generic Model for Electric Double Layers in Porous Electrodes

The performance of electric-double-layer capacitors (EDLCs) hinges on microscopic charge distributions near the electrode/electrolyte interfaces. Whereas practical EDLCs consist of electrodes made of amorphous porous materials, theoretical understanding of EDLCs is mostly based on EDL structure near a planar surface or on simplistic models that have little relevance to realistic systems. In this work, we propose a spherical shell model to account for both pore size and curvature effects of amorphous porous materials. The EDL structure in spherical shells has been investigated over a broad range of pore sizes and curvatures by use of classic density functional theory. Theoretical results reveal that the curvature effects on convex and concave EDLs are drastically different and that materials with extensive convex surfaces will lead to maximized capacitance. Like a slit pore, the spherical shell model also predicts oscillatory variation of capacitance with pore size, but the oscillatory behavior is magnified as the curvature increases. The joint effects of pore size and curvature identified in this work give new insight into materials design for porous electrodes with optimal EDLC performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

RF design of APEX2 two-cell continuous-wave normal conducting photoelectron gun cavity based on multi-objective genetic algorithm

High brightness, high repetition rate electron beams are key components for optimizing the performance of next generation scientific instruments, such as MHz-class X-ray Free Electron Laser (XFEL) and Ultra-fast Electron Diffraction/Microscopy (UED/UEM). In the Advanced Photo-injector EXperiment (APEX)at Berkeley Lab, a photoelectron gun based on a 185.7 MHz normal conducting re-entrant RF cavity, has been proven to be a feasible solution to provide high brightness, high repetition rate electron beam for both XFEL and UED/UEM. Based on the success of APEX, a new electron gun system, named APEX2, has been under development to further improve the electron beam brightness. For APEX2, we have designed a new 162.5 MHz two-cell photoelectron gun and achieved a significant increase on the cathode launching field and the beam exit energy. For a fixed charge per bunch, these improvements will allow for the emittance reduction and hence to an increased beam brightness. The design of APEX2 gun cavity is a complex problem with multiple design goals and restrictions, some even competing each other. In conclusion, for a systematic and comprehensive search for the optimized cavity geometry, we have developed and implemented a novel optimization method based on the Multi-Objective Genetic Algorithm (MOGA).

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Orientation-selected micro-pillar compression of additively manufactured 316L stainless steels: Comparison of as-manufactured, annealed, and proton-irradiated variants

In this work, irradiation response and deformation mechanisms of additively manufactured (AM) 316L stainless steel were studied by atomic scale characterization and micro-pillar compression. The AM 316L stainless steels were fabricated by direct energy deposition, a laser-based additive manufacturing process. Irradiation with 2 MeV protons at 360°C was performed to create ~1.8 displacements-per-atom (dpa) damage in AM 316L. Deformation behaviors of the as-manufactured, annealed, and proton-irradiated variants were studied, focusing on the effects of manufacturing-induced pores, residual stress, and irradiation-introduced defects (dislocation loops and voids). Micro-pillars were prepared from grains of pre-selected orientation, avoiding contributions of grain boundaries and allowing determination of resolved shear stress on {111} glide planes. Transmission electron microscopy was used to characterize the pre- and post-deformation microstructure. It was found that in the as-manufactured alloy variant, moving dislocations were the major deformation carrier, with noticeable blocking by fabrication-induced pores, In the annealed variant, hardness was reduced, and deformation was also accomplished by dislocation gliding. In the proton-irradiated variant, significant twinning was observed. Comparing measured resolved shear stress and predicted critical stress for dislocation dissociation, we conclude that irradiation hardening became high enough to activate twinning. Therefore, the deformation mechanism changes from dislocation gliding to twinning. The study is important for both processing optimization and performance evaluation of AM alloys for reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Solar light driven photocatalytic degradation of Ofloxacin based on ultra-thin bismuth molybdenum oxide nanosheets

Highlights: • Facile synthesis of ultra-thin Bi{sub 2}MoO{sub 6} nanosheets. • Solar-light driven photocatalytic degradation of antibiotic Ofloxacin. • Almost 70% photocatalytic degradation of Ofloxacin under optimized conditions. - Abstract: Herein, we report the facile synthesis, characterization and solar-light driven photocatalytic degradation of ultra-thin bismuth molybdenum oxide (Bi{sub 2}MoO{sub 6}) nanosheets. The nanosheets were synthesized by hydrothermal process and characterized using several techniques such as powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDS), UV-diffusive reflectance spectroscopy (UV-DRS), transmission emission microscopy (TEM) and X-ray photon electron spectroscopy (XPS). The detailed morphological and structural studies confirmed the growth of ultra-thin Bi{sub 2}MoO{sub 6} nanosheets in large density with orthorhombic crystal structure. The calculated bandgap energy from Tauc’s plot for the synthesized nanosheets was found to be 2.73 eV, which shows maximum absorption toward higher wavelength. The XPS studies confirmed the successful formation Bi{sub 2}MoO{sub 6} nanosheets. The synthesized material was used as efficient solar light driven photocatalyst for the photocatalytic degradation of Ofloxacin. The mineralization of ofloxacin was confirmed using total organic carbon (TOC) analysis and photocatalytic experiments were performed under optimized conditions. It was observed that TOC value decreased with increasing the irradiation time. Under optimized photocatalytic degradation conditions, the observed extent of degradation was found to be ∼ 71%. The observed results confirm that the synthesized Bi{sub 2}MoO{sub 6} nanosheets are efficient and promising visible light responsive catalyst for the degradation of persistent organic pollutants.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Implementing computer-based procedures: thinking outside the paper margins - 236

In the past year there has been increased interest from the nuclear industry in adopting the use of electronic work packages and computer-based procedures (CBPs) in the field. The goal is to incorporate the use of technology in order to meet the Nuclear Promise requirements of reducing costs, and improving efficiency and decreasing human error rates in plant operations. Researchers, together with the nuclear industry, have been investigating the benefits of an electronic work package system and specifically CBPs would have over current paper-based procedure practices. There are several classifications of CBPs ranging from a straight copy of the paper-based procedure in PDF format to a more intelligent dynamic CBP. A CBP system offers a vast variety of potential improvements, such as context driven job aids, integrated human performance tools (e.g., place-keeping and correct component verification), and dynamic step presentation. However, the implementation of a CBP system does not automatically improve the quality of procedures. Utilities should review, standardize format and update current procedure content before transitioning to a computerized process. Implementation of a CBP system can be a time to break out of traditional procedure writing processes and create new processes and procedures that take advantage of the capabilities a CBP system. This paper summarizes INL research findings related to challenges with implementing a CBP system and provides suggestions to take into consideration. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Group structure selection with random forests

Choosing an appropriate group structure for multigroup transport is far from an exact science. For some applications, one blindly uses a group structure developed years ago by forgotten methods. Furthermore, one sometimes uses the same group structure for a variety of problems, even if the group structure was originally developed with a certain application in mind. In this work, we create optimized group structures with simulated annealing for critical assembly test problems and apply a random forest regressor with bagging to choose the best group structure based on parameters of the different test problems. The optimized group structures were generated using a simulated annealing optimizer for several simple, spherical, and unreflected problems. The optimization was performed to minimize a cost function that included fission rate, absorption rate, leakage, and k{sub eff}. A random forest regressor was then trained on a set of International Criticality Safety Benchmark Evaluation Project inputs and used to select one of these six group structures. The trained machine learning model chose the best group structure 65% of the time, and one of the three best 89% of the time. Furthermore, it decreased the L2 error over all test problems by a factor of 25 when compared to the standard Los Alamos 70-group structure. In other words, the model chose group structure that were far more appropriate for the test problems than the traditional LANL group structure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Consideration for the application of artificial neural networks to criticality code validation - 14270

The artificial neural network (ANN) is a consistent example for non-parametric parameter estimation and regression, with powerful universal approximation properties, that is distribution independent. ANN application to criticality code validation centers on aligning mathematical relationships between code validation parameters and neural network prediction for those parameter values. Two specific goals are: (1) an appropriate ANN architecture to determine validation parameter values, and (2) bootstrapping individual ANNs as a 'Prediction Interval' (PI) technique to estimate the Calculation Margin and Upper Subcritical Limit. The individual ANN workflow is segregated into Training, Validation (network), and Testing Sets, where the network predictive properties and performance are expressed in the latter set. The Bootstrap ANN replicates a large set of individual ANNs (taken from the original data distribution) where (1-α/2)100% PI estimates can be determined. Results from the ANN method are compared to a variety of validation examples: a 'toy example' from ANSI/ANS-8.24, Appendix D, a variety of methods noted in a Nuclear Criticality Safety Division 2009 paper, and several validation campaigns for highly enriched uranium and plutonium systems. The ANN method is robust and can be applied to single or multivariate inputs. A trained ANN can provide both an average estimate (e.g., single value) and an estimate over a range of independent variable values where the kUSL can be formed in a regression relationship suitable for extrapolation. The input dependence has been tested, ranging from 15 to over 400 sample size distributions. An expanded Shapiro-Wilk test (for sample sizes up to 5000) was implemented to confirm that the ANN method is transparent to distribution function. The Principle Component Analysis method was applied as an input preprocessing step embedded within the ANN in order to reduce dimensionality and correlation for multivariable inputs. This can potentially aid in determining accurate margins over applying arbitrary margins. The automatic optimization of the Bootstrap ANN PI is left for future consideration. (authors)

42 ENGINEERING↗
DOE OSTI2021

A Data-Driven Multi-Period Importance Sampling Strategy for Stochastic Economic Dispatch

Power systems with high penetrations of renewable energy (e.g., wind power) require sophisticated approaches to optimize system performance due to uncertainty in short-term system generation capacity. In this paper, we combine a data-driven analog scenario selection method with importance sampling to create a novel scenario construction approach for two-stage stochastic economic dispatch problems with a large number of wind farms on a network. The proposed method produces scenarios with realistic physics by finding high-fidelity analogs that can describe future states of the system. We show how to extend this method to multi-period operations and demonstrate the effectiveness of this technique by simulating economic dispatch operations on a synthetic test system over the course of a week.

data-driven forecasting↗
DOE OSTI2021

Electrical Energy Storage Data Submission Guidelines, Version 2

Energy storage technologies are positioned to play a substantial role in power delivery systems. They have the potential to serve as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, because of their relative infancy, there is a lack of knowledge about how these resources truly operate over time. A data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and a data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verify the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data that the provider supplies for the analysis. After a review of energy storage data received from several providers, some of these data have clearly shown to be inconsistent and incomplete, raising the question of their efficacy for a robust analysis. This report reviews and proposes general guidelines, such as sampling rates and data points, that providers must supply for a robust data analysis to take place. Consistent guidelines are the basis of a proper protocol and ensuing standards to (1) reduce the time that it takes for data to reach those who are providing the analysis; (2) allow them to better understand the energy storage installations; and (3) enable them to provide a high-quality analysis of the installations. The report is intended to serve as a starting point for what data points should be provided when monitoring. Readers are encouraged to use the guidance in the report to develop specifications for new systems, as well as enhance current efforts to ensure optimal storage performance. As battery technologies continue to advance and the industry expands, the report will be updated to remain current.

25 ENERGY STORAGE↗
DOE OSTI2020

Parametric study of optical transmission through plasmonic hole arrays modulated by the phase transition of vanadium dioxide

We have performed comprehensive electromagnetic simulations and preliminary experiments to explore the effects of geometrical and material parameters on the extraordinary optical transmission (EOT) through periodic arrays of subwavelength holes in a bilayer stack consisting of a gold or silver film atop a vanadium dioxide film (Au/Ag + VO 2 ), where the latter undergoes a semiconductor-to-metal phase transition. Using the finite-difference time-domain (FDTD) and finite-element methods (FEM), we vary iteratively the array periodicity, VO 2 film thickness and hole diameters, as well as the refractive index inside the VO 2 -layer holes and the VO 2 optical constants. For each variation, we compare the metallic-to-semiconducting ratios of the zero-order transmission ( T 00 ) peaks and find sharp maxima in these ratios within narrow parameter ranges. The maxima arise from Fabry-Perot and Fano-type resonances that minimize T 00 in the semiconducting phase of the perforated bilayers. At a fixed array period, the primary factors controlling the VO 2 -enabled EOT modulation are the VO 2 thickness, diameter of the VO 2 -layer holes, and absorption in the two VO 2 phases. Besides uncovering the origins of the higher metallic-phase T 00 , this study provides a protocol for optimizing the performance of the bilayer hole arrays for potential uses as dynamically tunable nano-optical devices.

Donev, Eugenii U. (ORCID:000000030555219X)↗
DOE OSTI2022

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Light-Duty Vehicle Attribute Projections (Years 2015-2030)

This report describes the approach and results associated with projecting vehicle attributes for future light-duty vehicle classes and powertrains, from year 2015 to 2030. The focus is the California light-duty vehicle market and the projected attributes include fuel economy, acceleration, driving range (total and all-electric), and manufacturer suggested retail price from year 2015 to year 2030. Attributes are weighted by California vehicle sales, which are projected using a historically validated consumer choice model - the Automotive Deployment Option Projection Tool (ADOPT) - integrated with a modeling framework that simulates vehicle fuel economy, cost, and acceleration performance while optimizing vehicle components - the Future Automotive Systems Technology Simulator model (FASTSim). Both models were developed at the National Renewable Energy Laboratory and have been adapted to represent the future California light-duty vehicle market for the present report. The analysis includes several scenarios pertaining to electricity and, essentially, electric vehicle demand in California, as established by the Energy Commission. Results suggest that implementation of certain policies, such as the Corporate Average Fuel Economy standards, impacts vehicle attribute projections. The results also show that standards and policy targets are not exclusively met by changes in vehicle attributes, but also through shifts in market demand and sales for certain vehicle powertrains.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Facet-Dependent Kinetics and Energetics of Hematite for Solar Water Oxidation Reactions

Currently, the performance of a photoelectrochemical (PEC) system is highly dependent on the charge separation, transport and transfer characteristics at the photoelectrode|electrolyte interface. Of the factors that influence the charge behaviors, the crystalline facets of the semiconductor in contact with the electrolyte play an important role but has been poorly studied previously. Here, we present a study aimed at understanding how the different facets of hematite affect the charge separation and transfer behaviors in a solar water oxidation reaction. Specifically, hematite crystallites with predominantly {012} and {001} facets exposed were synthesized. Density functional theory (DFT) calculations revealed that hematite {012} surfaces feature higher OH coverage, which was confirmed by X-ray photoelectron spectroscopy (XPS). These surface OH groups act as active sites to mediate water oxidation reactions, which plays a positive role for the PEC system. These surface OH groups also facilitate charge recombination, which compromises the charge separation capabilities of hematite. Moreover, intensity modulated photocurrent spectroscopy (IMPS) confirmed that hematite {012} surfaces exhibit higher rate constants for both charge transfer and recombination. Open circuit potential (OCP) measurements revealed that the hematite {012} surface reflects a greater degree of Fermi level pinning effect. Our results shed light on how different surface crystal structures may change surface kinetics and energetics. The information is anticipated to contribute to efforts on optimizing PEC performance for practical solar fuel synthesis.

36 MATERIALS SCIENCE↗