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At least 91 records · Page 5

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗

Optically multiplexed neutron time-of-flight technique for inertial confinement fusion

Neutron time-of-flight (nTOF) detectors are crucial in diagnosing the performance of inertial confinement fusion (ICF) experiments, which implode targets of deuterium–tritium fuel to achieve thermonuclear conditions. These detectors utilize the fusion neutron energy spectrum to extract key measurements, including the hotspot ion temperature and fuel areal density. Previous work [Danly et al., Rev. Sci. Instrum. 94, 043502 (2023)] has demonstrated adding 1D spatial resolution to an nTOF-like detector using a neutron aperture and streak camera to measure the ion temperature profile of an ICF implosion. By contrast, the study presented herein explores modifying the 1D detector to use a fast photomultiplier tube (PMT) to validate the design of a 2D spatially resolved instrument based on reconstruction from 1D profiles. The modification would collect time-of-flight traces from separate scintillators in an imaging array with one PMT using optical fibers of varying lengths to time-multiplex the signals. This technique has been demonstrated in ride-along experiments on the OMEGA laser with 20 fiber-coupled scintillator channels connected to a Photek PMT210. Results provide constraints on the fiber lengths and PMT gating requirements to promote pulse fidelity throughout all channels. Calibration of the detector to fixed nTOFs can provide a preliminary estimate of the instrument response function (IRF), although measurement of the IRF is currently under way. These results suggest that nTOF signals can potentially be time-multiplexed with fibers so long as the design is strategic to mitigate signal-to-noise reduction, modal dispersion, and charge build-up in the PMT, which has implications beyond ion temperature imaging.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO 3 , CaZrN 2 , and YMnO 3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Development of MOSCATO: A CFD-Level Electrochemistry and Corrosion Simulator for Molten Salt Systems

For both coolant and fueled variants of molten salt reactors (MSRs), the corrosion of structural materials is a significant challenge. The corrosion stems from chemical and electrochemical reactions initiated by fissile material, fission products, and impurities in the salt. Lower-fidelity models rely on empirical correlations for mass transfer, simplified lumped temperature profiles, and similar assumptions. They do not capture detailed spatial variations in complex geometries, creating the need for high-fidelity modeling to bridge this gap.As we approach the demonstration and possible deployment of MSRs in this decade, the development of a high-fidelity, high-performance simulator becomes imperative. To simulate the complex electrochemical environment and corrosion within molten salt systems, we have developed the Molten Salt Chemistry And TranspOrt (MOSCATO) code. This endeavor is comprised of three essential components. First, mass transfer equations are coupled with the Navier-Stokes equations in order to account for the transport of species in the salt. Second, the diffusion of alloy constituents, such as Cr, Fe, Ni, etc. is simulated within the structural metals. Third, the alloy and salt domains are coupled to account for the heterogeneous chemical and electrochemical reactions that occur at the salt-alloy interface.MOSCATO manages all three components within the framework of the highly scalable, open-source spectral element method computational fluid dynamics code Nek5000/NekRS. This integration enables MOSCATO to harness the immense computational power of modern high-performance computing resources, ensuring both high fidelity and computational speed.In addition to code development, we have initiated a comprehensive verification and validation campaign, utilizing data from diverse sources. First, MOSCATO's electrochemical solver was verified with reference numerical data. Then validation occurred against experiments: one of a thermal galvanic cell and the other for corrosion in flowing molten salt of FLiNaK (LiF-NaF-KF). This campaign verified and validated MOSCATO as a reliable tool for simulating electrochemical environments and corrosion in molten salt systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Characterization of Nitrate, Nitrite, Ammonia, and Tritium in D0220 Cores

This report describes characterization of nitrogen species (nitrate, nitrite, ammonia) in aqueous and solid phases in cores taken in borehole D0220 (well 299-E25-245) under crib 216-A-37-1 at the Hanford Site in 2022 to evaluate (a) the types of nitrogen species currently in the vadose zone and (b) the migration of nitrogen species in the vadose zone. Ammonia and tritium from PUREX decladding condensate were sporadically discharged to the crib from 1977 to 1989. During discharge operations, the estimated travel time through the vadose zone to groundwater was 2.5 to 9 months. By 2003, a characterization borehole (C4106) showed residual pore water with elevated tritium and nitrate in the first 100 ft of the vadose zone, which is likely from the crib. However, there was significantly more nitrate present at the shallowest depth (15 to 22 ft), which may indicate a different source for nitrate, such as adsorbed ammonia slowly being oxidized or nitrogen species precipitates slowly dissolving. In this study, more extensive nitrogen species characterization was done on D0220 cores (drilled in 2022) at 40- and 262-ft depths, which included (a) aqueous nitrate, nitrite, and tritium; (b) adsorbed ammonia; (c) nitrogen in carbonates (or other minerals dissolved in acidic acid); (d) nitrogen in iron oxides (or other minerals dissolved in oxalic acid); and (e) nitrogen in hard-to-extract minerals (minerals dissolved in nitric acid). High pore water nitrate (226 to 331 mg/L) at 40-ft depth measured in D0220 (2022) compared to 60 mg/L at 40-ft depth in C4106 (2003) may indicate nitrate is migrating deeper. Tritium concentrations (pore water 132,000 to 148,000 pCi/L) measured in D0220 at 40-ft depth in 2022 were considerably higher than in C4106 at 40-ft depth (160 pCi/L). Additional nitrogen species mass was present in adsorbed and precipitated phases in D0220 cores. Low adsorbed ammonia was measured at 40.1- and 261.7-ft depths. Low concentrations of carbonate associated nitrogen and iron oxide-associated nitrogen were present at 40.1 and 40.6 ft depths. Nitrogen species in solid phase extractions indicate nitrogen precipitates or aqueous nitrate or ammonia trapped in sediment microfractures that are coated by precipitates. Overall, the nitrogen species and tritium characterization at two depths in D0220 showed that additional nitrogen species were present in the vadose zone. In addition, elevated pore water nitrate in D0220 at 40 ft depth from 2022 may indicate vertical migration compared to C4106 nitrate profile from 2003. Analysis of additional depths in D0220 and spatial variability of the nitrate plume along the length of the crib (from surface electrical resistivity) would be extremely useful for this comparison.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations

This paper presents a method for optimizing the layout of floating wind farms that accounts for realistic seabed variations and the consequent adjustments to the mooring systems required for different turbine positions. The mooring lines of floating wind farms create large spatial constraints that are depth-dependent, since mooring designs must adapt to variations in seabed conditions over the array area. We develop a layout optimization methodology that addresses this, adjusting mooring system designs based on the local seabed characteristics as the layout changes and using steady-state models for the wake effects and mooring lines. The approach includes design algorithms that adjust the anchor positions and line length to achieve the desired mooring line profile for different water depths, and a layout optimization framework that implements spatial constraints between the turbines, mooring lines, and lease area boundaries. Demonstrating the method on several cases shows the effect of the seabed and spatial-constraint factors, as well as their interactions, on the optimal array layout. This demonstration paves the way for scaling up the method, using more powerful optimization algorithms to handle larger farm sizes and situations with more intensely varied seabed conditions.

17 WIND ENERGY↗

Massive red spiral galaxies in SDSS-IV MaNGA survey

ABSTRACT Massive red spiral galaxies (MRSGs) are supposed to be the possible progenitors of lenticular galaxies (S0s). We select a large sample of MRSGs ($M_*\gt 10^{10.5}\rm {\rm M}_{\odot }$) from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) DR17 using the g − r colour versus stellar mass diagram, along with control samples of blue spirals and S0s. Our main results are as follows: (1) After comparing the Sérsic index, concentration parameter, asymmetry parameter distribution, size–mass relation, and Σ1 (stellar mass surface density within the central 1 kpc)−mass relation, we find MRSGs are similar to S0s and have more compact and symmetric structures than blue spirals. MRSGs also resemble S0s in Dn4000, metallicity, Mgb/$\rm \left\langle Fe \right\rangle$, and V/σ radial profile. (2) By using MaNGA 2D spectra data, we separate the spatial regions into inner (R < 0.8Re) and outer (0.8 < R < 1.5Re) regions, and detect residual star formation in the outer regions of MRSGs. (3) When we select a sub-sample of MRSGs with NUV − r > 5, we find that they are completely star formation quenched in both inner and outer regions. Compared to optically selected MRSGs, NUV − r selected MRSGs appear to be more concentrated and have more massive dark matter haloes. The similarities between S0s and MRSGs suggest the possible evolutionary trend between MRSGs and S0s.

Astronomy & Astrophysics↗

UV–Vis–NIR Reflectance Spectroscopy and Chemometrics for Monitoring Pu Directly on an Ion Exchange Column

Here, we present a fiber-optic UV–vis–NIR reflectance spectroscopy method for direct, noninvasive monitoring of Pu(IV) in a glass ion exchange column during dynamic loading and elution in a glovebox. A movable probe enables spatially resolved spectral acquisition along the column axis, capturing distinct features associated with Pu(IV) nitrate complexes during loading and free ions during elution. Principal component analysis was applied to extract the dominant spectral variance and resolve relative concentration profiles without requiring precise knowledge of optical penetration depth or species identity. This in situ approach reveals spatial gradients and speciation dynamics in real time, which provides actionable insight into Pu(IV) ion migration, resin saturation, and breakthrough behavior under evolving flow conditions. The method offers a practical, fiber-compatible strategy to monitor glass column–based separations for Pu and other lanthanides or actinides and to characterize metal–resin interactions in flow-through systems.

actinide↗

Stress due to electric charge density distribution in a dielectric slab

The spatial distribution of electric field due to an imposed electric charge density profile in an infinite slab of dielectric material is derived analytically by integrating Gauss’s law. Various charge density distributions are considered, including exponential and power-law forms. Here, the Maxwell stress tensor is used to compute a notional static stress in the material due to the charge density and its electric field. Characteristics of the electric field and stress distributions are computed for example cases in polyethylene, showing that field magnitudes exceeding the dielectric strength would be required in order to achieve a stress exceeding the ultimate tensile strength.

42 ENGINEERING↗

Implementation of low temperature spectrometers for the JET high resolution Thomson scattering diagnostic for disruption plasma measurements

This work presents a system upgrade of the High Resolution Thomson Scattering (HRTS) diagnostic on JET that allows it to measure low temperature (1–500 eV) plasma pre- and post-Thermal quench (TQ), which would help us further understand the Shattered Pellet Injection (SPI) physics. The upgrade was done by connecting optic fibers from the original HRTS system to four spectrometers specialized in measuring low temperature plasmas. The upgraded system allows for the measurement of low temperature plasma at up to 12 spatial points, which can be flexibly distributed throughout the JET outer midplane profile during the dedicated SPI experiments. In other JET experiments, four previously unused groups of fibers were used to measure the central plasma to provide disruption data without changing the standard HRTS system. The low temperature Thomson scattering system was installed, commissioned, and cross-calibrated against the standard HRTS diagnostic in a calibration pulse. The system worked reliably during the JET experiments and provided electron density and temperature profiles for pre- and post-TQ low temperature plasmas in the SPI campaign.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A dynamic protein interactome drives energy conservation and electron flux in Thermococcus kodakarensis

ABSTRACT Life is supported by energy gains fueled by catabolism of a wide range of substrates, each reliant on the selective partitioning of electrons through redox ( red uction and ox idation) reactions. Electron flux through tunable and regulated protein interactions provides dynamic routes for energy conservation, but how electron flux is regulated in vivo , particularly for archaeal metabolisms that support rapid growth at the thermodynamic limits of life, is poorly understood. Identification of bona fide in vivo protein assemblies and how such assemblies dictate the totality of electron flux is critical to our understanding of the regulation imposed on metabolism, energy production, and energy conservation. Here, 25 key proteins in central metabolic redox pathways in the model, genetically accessible, hyperthermophilic archaeon Thermococcus kodakarensis , were purified to reveal an extensive, dynamic, and tightly interconnected network of protein interactions that responds to environmental cues (such as the availability of various reductive sinks) to direct electron flux to maximize energetic gains. Interactions connecting disparate functions suggest many catabolic and anabolic activities occur in spatial proximity in vivo , and while protein complexes have been historically defined under optimal conditions, many of these complexes appear to maintain alternative partnerships in changing conditions. The totality of the results obtained redefines our understanding of in vivo assemblies driving ancient metabolic strategies supporting the growth of modern Archaea. IMPORTANCE Given the potential for rational genetic manipulations of biofuel- and biotech-promising archaea to yield transformative results for major markets, it is a priority to define how the metabolisms of such species are controlled, at least in part, by in vivo protein assemblies, and from such, define routes of energy flux that can be most efficiently altered toward biofuel or biotechnological gains. Proteinaceous electron carriers (PECs, such as ferredoxins) offer the potential for specific protein–protein interactions to coordinate selective reductive flow. Employing the model, genetically accessible, hyperthermophilic archaeon, Thermococcus kodakarensis , we establish the metabolic protein interactome of 25 key redox proteins, revealing that each redox active protein has a dynamic partnership profile, suggesting catabolic and anabolic activities may occur in concert and in temporal and spatial proximity in vivo . These results reveal critical importance in evaluating the newly identified partnerships and their role and utility in providing regulated redox flux in T. kodakarensis .

Williams, Sere A. (ORCID:0000000235509590)↗

Optimization and active stabilization of a far-infrared laser for NSTX-U high poloidal wavenumber scattering diagnostics

The far-infrared (FIR) laser output beam power and profile are important parameters in the laser-aided diagnostics, directly influencing the spatial resolution and signal-to-noise ratio of measurements. Here, this work focuses on developing a systematic control method to enhance FIR laser beam quality through optimized mirror alignment and real-time feedback-based precision cavity length tuning. A 150 W CO 2 laser, aligned with the waveguide axis using a HeNe reference laser, serves as the pump source. The sensitivity of FIR beam intensity to pump gas pressure and thermal expansion is investigated, revealing that even a 1 µm cavity expansion can significantly degrade output power stability to about two-thirds of its original value. To address this, a feedback control module has been designed and implemented for active cavity length adjustment, stabilizing the output power at ∼30 mW. In addition, maintaining a high formic acid gas pressure ($>$190 mTorr) within the cavity ensures reliable operation. The optimized FIR laser will be deployed on the National Spherical Torus eXperiment-U high poloidal wavenumber scattering system for studying electron-scale turbulence in tokamak plasmas.

Xu, Xinhang [Univ. of California, Davis, CA (Unite↗

Modeling of injected current stream-induced 3D perturbations in local helicity injection plasmas

Solenoid-free tokamak startup techniques are essential for spherical tokamaks and offer a pathway to cost reduction and design simplification in fusion energy systems. Local helicity injection (LHI) is one such approach, employing compact edge current sources to drive open field line current that initiates and sustains tokamak plasmas. The recently commissioned Pegasus-III Experiment provides a platform for advancing this and other solenoid-free startup methods. This study investigates the effect of LHI on magnetic topology in Pegasus-III plasmas. A helical filament model represents the injected current, and the linear plasma response to its three-dimensional field is calculated with M3D-C1. Poincaré mapping reveals substantial flux surface degradation in all modeled cases. The onset of overlapping magnetic structures and large-scale surface deformation begins near Ψ N ≈ 0.37, indicating a broad region of perturbed topology extending toward the edge. In rotating plasmas, both single-fluid and two-fluid models exhibit partial screening of the n = 1 perturbation, with two-fluid calculations showing stronger suppression near the edge. In contrast, the absence of rotation leads to strong resonant field amplification in the single-fluid case, while the two-fluid case with zero electron rotation mitigates this amplification and preserves edge screening. Magnetic probe measurements indicate that modeling the stream with spatial spreading—representing distributed current and/or oscillatory motion—better reproduces measured magnetic power profiles than a rigid filament model. The results underscore the role of rotation and two-fluid physics in screening stream perturbations and point to plasma flow measurements and refined stream models as key steps toward improving predictive fidelity.

Schaefer, Carolyn E. [Univ. of Wisconsin, Madison,↗

A contactless in situ EFISH method for measuring electrostatic potential profile of semiconductor/electrolyte junctions

In photoelectrochemical cells, promising devices for directly converting solar energy into storable chemical fuels, the spatial variation of the electrostatic potential across the semiconductor–electrolyte junction is the key parameter that determines the cell performance. In principle, electric field induced second harmonic generation (EFISH) provides a contactless in situ spectroscopic tool to measure the spatial variation of electrostatic potential. However, the total second harmonic generation (SHG) signal contains the contributions of the EFISH signals of semiconductor space charge layer and the electric double layer, in addition to the SHG signal of the electrode surface. The interference of these complex quantities hinders their analysis. In this work, to understand and deconvolute their contributions to the total SHG signals, bias-dependent SHG measurements are performed on the rutile TiO2(100)–electrolyte junction as a function of light polarization and crystal azimuthal angle (angle of the incident plane relative to the crystal [001] axis). A quadratic response between SHG intensity and the applied potential is observed in both the accumulation and depletion regions of TiO2. The relative phase difference and amplitude ratio are extracted at selected azimuthal angles and light polarizations. At 0° azimuthal angle and s-in–p-out polarization, the SHG intensity minimum has the best match with the TiO2 flatband potential due to the orthogonal relative phase difference between bias-dependent and bias-independent SHG terms. We further measure the pH-dependent flatband potential and probe the photovoltage under open circuit conditions using the EFISH technique, demonstrating the capability of this contactless method for measuring electrostatic potential at semiconductor–electrolyte junctions.

Chemistry↗

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗

Changuinola peat soil characteristics and gas emission raw data October 2019

This dataset comprises radiocarbon and geochemical measurements from peat and porewater samples collected across various depths at a site in Bocas del Toro, Panama. The study focuses on carbon cycling dynamics in tropical peatlands by examining carbon isotopic signatures (¹⁴C and ¹³C) and elemental compositions of bulk peat, dissolved organic carbon (DOC), carbon dioxide (CO₂), and methane (CH₄). Key parameters include radiocarbon ages and isotopic ratios (δ¹³C) of bulk peat, concentrations of carbon (%C) and nitrogen (%N), and radiocarbon content of porewater gases and dissolved organic carbon (DOC). The data provide insights into the vertical and spatial distribution of carbon sources and possible preservation and decomposition processes within tropical peat profiles, offering critical information for understanding carbon storage and greenhouse gas emissions in these ecosystems.This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) carbon isotopic signatures (¹⁴C and ¹³C); (5) concentrations of carbon (%C) and nitrogen (%N); (6) radiocarbon content of porewater carbon dioxide (CO₂), and methane (CH₄) ; (7) porewater DOC; (8) bulk peat sampling protocol; (9) porewater sampling protocol; (10) porewater gas collection methods; and (11) gas extraction methods. All files are in .csv format and can be opened with any software that supports this file types.

54 ENVIRONMENTAL SCIENCES↗