Potential-dependent insights into the origin of high ammonia yield rate on copper surface via nitrate reduction:A computational and experimental study

Focusing on revealing the origin of high ammonia yield rate on Cu via nitrate reduction(NO3RR),we herein applied constant potential method via grand-canonical density functional theory(GC-DFT)with implicit continuum solvation model to predict the reaction energetics of NO3RR on pure copper surface in alkaline media.The potential-dependent mechanism on the most prevailing Cu(111)and the minor(100)and(110)facets were established,in consideration of NO_(2)_(−),NO,NH_(3),NH_(2)OH,N_(2),and N_(2)O as the main products.The computational results show that the major Cu(111)is the ideal surface to produce ammonia with the highest onset potential at 0.06 V(until−0.37 V)and the highest optimal potential at−0.31 V for ammonia production without kinetic obstacles in activation energies at critical steps.For other minor facets,the secondary Cu(100)shows activity to ammonia from−0.03 to−0.54 V with the ideal potential at−0.50 V,which requires larger overpotential to overcome kinetic activation energy barriers.The least Cu(110)possesses the longest potential range for ammonia yield from−0.27 to−1.12 V due to the higher adsorption coverage of nitrate,but also with higher tendency to generate di-nitrogen species.Experimental evaluations on commercial Cu/C electrocatalyst validated the accuracy of our proposed mechanism.The most influential(111)surface with highest percentage in electrocatalyst determined the trend of ammonia production.In specific,the onset potential of ammonia production at 0.1 V and emergence of yield rate peak at−0.3 V in experiments precisely located in the predicted potentials on Cu(111).Four critical factors for the high ammonia yield and selectivity on Cu surface via NO3RR are summarized,including high NO3RR activity towards ammonia on the dominant Cu(111)facet,more possibilities to produce ammonia along different pathways on each facet,excellent ability for HER inhibition and suitable surface size to suppress di-nitrogen species formation at high nitrate coverage.Overall,our work provides comprehensive potential-dependent insights into the reaction details of NO3RR to ammonia,which can serve as references for the future development of NO3RR electrocatalysts,achieving higher activity and selectivity by maximizing these characteristics of copper-based materials.

Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

Ensemble Bayesian method for parameter distribution inference:application to reactor physics

The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering.

Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics

Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are easy to solve using traditional numerical methods albeit still challenging using neural networks for a wide range of practical problems.We present two networks,namely the Generalized Inverse Power Method Neural Network(GIPMNN)and Physics-Constrained GIPMNN(PC-GIPIMNN)to solve K-eigenvalue problems in neutron diffusion theory.GIPMNN follows the main idea of the inverse power method and determines the lowest eigenvalue using an iterative method.The PC-GIPMNN additionally enforces conservative interface conditions for the neutron flux.Meanwhile,Deep Ritz Method(DRM)directly solves the smallest eigenvalue by minimizing the eigenvalue in Rayleigh quotient form.A comprehensive study was conducted using GIPMNN,PC-GIPMNN,and DRM to solve problems of complex spatial geometry with variant material domains from the fleld of nuclear reactor physics.The methods were compared with the standard flnite element method.The applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN and DRM.

Measurement of solar pp neutrino flux using electron recoil data from PandaX-4T commissioning run

The proton-proton(pp)fusion chain dominates the neutrino production in the Sun.The uncertainty of the predicted pp neutrino flux is at the sub-percent level,whereas that of the best measurement is O(10%).In this study,for the first time,we measure solar pp neutrinos in the electron recoil energy range from 24 to 144 keV using the PandaX-4T commissioning data with 0.63 tonne×year exposure.The pp neutrino flux is determined as(8.0±3.9(stat)±10.0(syst))×1010 s^(-1)cm^(-2),which is consistent with the Standard Solar Model and existing measurements,corresponding to an upper flux limit of 23.3×10^(10)s^(-1)cm^(-2)at 90%C.L..

Detecting neutrinos from supernova bursts in PandaX-4T

Neutrinos from core-collapse supernovae are essential for understanding neutrino physics and stellar evolution.Dual-phase xenon dark matter detectors can be used to track explosions of galactic supernovae by detecting neutrinos through coherent elastic neutrino-nucleus scatterings.In this study,a variation of progenitor masses and explosion models are assumed to predict neutrino fluxes and spectra,which result in the number of expected neutrino events ranging from 6.6 to 13.7 at a distance of 10 kpc over a 10-s duration with negligible backgrounds at PandaX-4T.Two specialized triggering alarms for monitoring supernova burst neutrinos are built.The efficiency of detecting supernova explosions at various distances in the Milky Way is estimated.These alarms will be implemented in the real-time supernova monitoring system at PandaX-4T in the near future,which will provide supernova early warnings for the astronomical community.

Waveform simulation in PandaX-4T

Signal reconstruction through software processing is a crucial component of the background and signal models in the PandaX-4T experiment,which is a multi-tonne dark matter direct search experiment.The accuracy of signal reconstruction is influenced by various detector artifacts,including noise,dark count of photomultiplier,photoionization of impurities in the detector,and other relevant considerations.In this study,we presented a detailed description of a semi-data-driven approach designed to simulate a signal waveform.This work provides a reliable model for the efficiency and bias of the signal reconstruction in the data analysis of PandaX-4T.By comparing critical variables that relate to the temporal shape and hit pattern of the signals,we found good agreement between the simulation and data.

MBTIviz: A Visualization System for Research on Psycho-Demographics and Personality

The increasing interest in exploring the correlation between personal-ity traits and real-life individual characteristics has been driven by the growing popularity of the Myers–Briggs Type Indicator(MBTI)on social media plat-forms.To investigate this correlation,we conduct an analysis on a Myers–Briggs Type Indicator(MBTI)-demographic dataset and present MBTIviz,a visualiza-tion system that enables researchers to conduct a comprehensive and accessible analysis of the correlation between personality and demographic variables such as occupation and nationality.While humanities and computer disciplines provide valuable insights into the behavior of small groups and data analysis,analysing demographic data with personality information poses challenges due to the com-plexity of big data.Additionally,the correlation analysis table commonly used in the humanities does not offer an intuitive representation when examining the relationship between variables.To address these issues,our system provides an integrated view of statistical data that presents all demographic information in a single visual format and a more informative and visually appealing approach to presenting correlation data,facilitating further exploration of the linkages between personality traits and real-life individual characteristics.It also includes machine learning predictive views that help nonexpert users understand their personality traits and provide career predictions based on demographic data.In this paper,we utilize the MBTIviz system to analyse the MBTI-demographic dataset,calcu-lating age,gender,and occupation percentages for each MBTI and studying the correlation between MBTI,occupation,and nationality.

Neutron-induced nuclear recoil background in the PandaX-4T experiment

Neutron-induced nuclear recoil background is critical to dark matter searches in the PandaX-4T liquid xenon experiment.In this study,we investigate the features of neutron background in liquid xenon and evaluate its contribution in single scattering nuclear recoil events using three methods.The first method is fully based on Monte Carlo simulations.The last two are data-driven methods that also use multiple scattering signals and high energy signals in the data.In the PandaX-4T commissioning data with an exposure of 0.63 tonne-year,all these methods give a consistent result,i.e.,there are 1.15±0.57 neutron-induced backgrounds in the dark matter signal region within an approximated nuclear recoil energy window between 5 and 100 keV.