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At least 1,171 records · Page 65

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗

Description of a Neotropical gall inducer on Araceae: Arastichus, gen. nov. (Hymenoptera, Eulophidae) and two new species

A new genus of a Neotropical gall inducing tetrastichine eulophid on Araceae is described and confirmed using Ultraconserved Elements (UCE) phylogenomic data. Arastichus Gates, Hanson, Jansen-González & Zhang, gen. nov. , includes two new species and one species transferred from Aprostocetus Westwood: A. capipunctata Gates, Hanson, Jansen-González & Zhang, sp. nov. , A. gallicola (Ferrière), comb. nov. , and A. gibernau , Gates, Hanson, Jansen-González & Zhang, sp. nov.

Zhang, Y. Miles (ORCID:0000000348018624)↗

Maps of ice wedge thermokarst pool expansion from twenty-seven circumpolar survey areas

This repository includes data and code to accompany the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. The data include satellite imagery and derived maps of thermokarst pools from twenty-seven survey areas in North America and Siberia. The code, written in MATLAB (R2021a), contains demonstrations of the workflow for generating the maps. The demonstrations include training a generalized UNet for mapping thermokarst pools using data from three survey areas, 'fine tuning' the UNet for use at a specific survey area using transfer learning, applying a trained UNet to infer thermokarst pool extent within satellite imagery, and performing histogram matching as a pre-processing step to improve satellite imagery contrast. Contains MATLAB script files and M files, TIF files, shape files, XML, Excel, TXT, and CSV files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Development of A Hardware-In-the-Loop (HIL) Testbed for Cyber-Physical Security in Smart Buildings

As smart buildings move towards open communication technologies, providing access to the Building Automation System (BAS) through the building's intranet, or even remotely through the Internet, has become a common practice. However, BAS was historically developed as a closed environment and designed with limited cyber-security considerations. Thus, smart buildings are vulnerable to cyber-attacks with the increased accessibility. This study introduces the development and capability of a Hardware-in-the-Loop (HIT) testbed for testing and evaluating the cyber-physical security of typical BASs in smart buildings. The testbed consists of three subsystems: (1) a real-time HIL emulator simulating the behavior of a virtual building as well as the Heating, Ventilation, and Air Conditioning (HVAC) equipment via a dynamic simulation in Modelica; (2) a set of real HVAC controllers monitoring the virtual building operation and providing local control signals to control HVAC equipment in the HIL emulator; and (3) a BAS server along with a web-based service for users to fully access the schedule, setpoints, trends, alarms, and other control functions of the HVAC controllers remotely through the BACnet network. The server generates rule-based setpoints to local HVAC controllers. Based on these three subsystems, the HIL testbed supports attack/fault-free and attack/fault-injection experiments at various levels of the building system. The resulting test data can be used to inform the building community and support the cyber-physical security technology transfer to the building industry.

Li, Guowen↗

Precision Measurements of the Neutron Magnetic Form Factor to High Momentum Transfer using Durand’s Method

Protons and neutrons, collectively known as nucleons, along with electrons, constitute the fundamental building blocks of the visible universe. Understanding their internal structure is crucial for addressing key scientific questions about our origin and existence. Elastic electron-nucleon scattering provides insights into the spatial distributions of charge and current within nucleons through their electromagnetic form factors. Accurate knowledge of these form factors over a broad range of Q2, the squared four-momentum transfer in the scattering process, reveals details about the nucleon's internal structure. However, high-Q2 data of the nucleon electromagnetic form factor is scarce due to the challenges associated with such measurements. This thesis reports preliminary results from high-precision measurements of the neutron magnetic form factor (GMn) to unprecedented Q2 using Durand's method, also known as the "ratio" method. Systematic errors are greatly reduced by ext

Datta, Provakar↗

Precision Measurements of the Neutron Magnetic Form Factor to High Momentum Transfer using Durand's Method

Protons and neutrons, collectively known as nucleons, along with electrons, constitute the funda- mental building blocks of the visible universe. Understanding their internal structure is crucial for addressing key scientific questions about our origin and existence. Elastic electron-nucleon scatter- ing provides insights into the spatial distributions of charge and current within nucleons through their electromagnetic form factors. Accurate knowledge of these form factors over a broad range of Q2, the squared four-momentum transfer in the scattering process, reveals details about the nucleon’s internal structure. However, high-Q2 data of the nucleon electromagnetic form factor is scarce due to the challenges associated with such measurements. This thesis reports preliminary results from high-precision measurements of the neutron magnetic form factor (Gn M ) to unprecedented Q2 using Durand’s method, also known as the “ratio” method. Systematic errors are greatly reduced by extracting Gn M from the ratio of neutron-coincident (D(e, e'n)) to proton-coincident (D(e, e'p)) quasi-elastic electron scattering from deuteron. The scattered electrons were detected in the BigBite spectrometer, which features multiple Gas Elec- tron Multiplier (GEM) layers with large active area for high-precision tracking at very high rates. Simultaneous nucleon detection was performed by the Super BigBite spectrometer, which utilizes a dipole magnet with large solid angle acceptance at forward angles and a novel hadron calorimeter with very high and comparable detection efficiencies for both protons and neutrons. This setup could handle very high luminosity, making high-Q2 measurements feasible. Data were collected at five Q2 points: 3, 4.5, 7.4, 9.9, and 13.6 (GeV/c)2. Preliminary results are reported for all, with the lowest two Q2 points in good agreement with existing world data, while the higher points significantly extend the Q2 range in which Gn M is known accurately. The precision of the highest Q2 point is expected to remain unmatched for years to come.

Datta, Provakar↗

Magmatic, hydrothermal and ore element transfer processes of the southeastern Archean Superior Province implied from electrical resistivity structure

Although magmatic- and metamorphic- derived fluids are widely recognized mineralizing agents, the role of crustal architecture in defining source and sink zones within the middle to lower crust and upper mantle of ancient orogens remains enigmatic. The globally largest and best-preserved Archean greenstone belts lie in the Superior Province, Canada. They provide an ideal location to investigate the influence of igneous construction and subsequent syn-deformational plutonism and metamorphism on the localization of metal-rich melts and fluids throughout the crustal column. Integration of three-dimensional magnetotelluric modelling and seismic reflection sections across the Abitibi subprovince reveals details of a 'whole-of-crust' magmatic and hydrothermal system. East-west low resistivity structures broadly underlie the surface traces of the major deformation zones that are host to significant gold endowment (>200 Moz), while mid-crustal cross trends suggest mineralized fluids flowed along 'pipes' within fault planes. Most low resistivity structures are inferred to represent domains containing interconnected zones of graphite and/or sulfide. Furthermore, these delineate relict mantle source/transit domains and crustal pathways enriched by the flow of magmas or metamorphic fluids genetically related to a late-stage pulse of ore-bearing magmatism, possibly as a result of slab break-off or delamination. Using the combined MT and seismic data, we develop a 3-D crustal-scale model which highlights how evolving orogenic architecture-controlled mass transfer and metallogenic processes developed.

58 GEOSCIENCES↗

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗

Evaporator Temperature Transient Testing of a High-Performance Sodium Filled Heat Pipe

Heat pipes are two-phase heat transfer devices that enable passive removal of heat from the reactor core to the power conversion system in heat pipe-cooled microreactor designs. Experimental investigations of heat pipe transients are needed for technology demonstration, verification and validation of numerical codes, and the establishment of regulatory requirements. The Single Primary Heat Extraction and Removal Emulator (SPHERE) facility at Idaho National Laboratory (INL) serves as a platform for evaluating the dynamic response of high-temperature heat pipes under a variety of operating conditions. The present work details the experimental investigation of a high-performance, defined as over 2 kW sodium heat pipe subjected to rapid input power fluctuations induced by sudden changes in the evaporator temperature setpoint. In addition, the heat pipe was subjected to an asymmetrical heat load where a subset of heaters operated at 30% and 70% below their nominal power. These experimental conditions were chosen to simulate thermal and operational stresses expected to be encountered in microreactors to provide data on heat pipe behavior during such important transient events. Key data and performance metrics, including time series of temperatures and strains, axial temperature profiles, thermal response times, and heat transfer capabilities, the thermal output over thermal input, were reported and discussed. The results highlight the resilience of heat pipes, revealing their potential to maintain thermal stability and efficiency under varying power loads. Lastly, the paper concludes with a discussion on the significance of the results and their implications for future research.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Exploring Ligand-to-Metal Charge-Transfer States in the Photo-Ferrioxalate System Using Excited-State Specific Optimization

The photo-ferrioxalate system (PFS), [Fe(III)(C 2 O 4 )] 3– , more than an exact chemical actinometer, has been extensively applied in wastewater and environment treatment. Despite many experimental efforts to improve clarity, important aspects of the mechanism of ferrioxalate photolysis are still under debate. In this paper, we employ the recently developed WΓ-CASSCF to investigate the ligand-to-metal charge-transfer states that are key to ferrioxalate photolysis. This investigation provides a qualitative picture of these states and key potential energy surface features related to the photolysis. Furthermore, our theoretical results are consistent with the prompt charge-transfer picture seen in recent experiments and clarify some features that are not visible in experiments. Two ligand-to-metal charge-transfer states contribute to the photolysis of ferrioxalate, and the avoided crossing barrier between them is low compared with the initial photoexcitation energy. Our data also clarify that one Fe–O bond cleaves first, followed by the C–C bond and the other Fe–O bond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

No Galaxy Left Behind: Measuring the Transfer Function of the Dark Energy Survey with Balrog

In this dissertation, we describe a calibration and diagnostic framework called Balrog which was used to directly sample the selection and photometric biases of the Dark Energy Survey (DES) Year 3 (Y3) dataset. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with traditional generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful diagnostic and calibration tool for a variety of DES Y3 science, particularly for the calibration of the photometric redshifts of distant "source" galaxies and magnification biases of nearer "lens" galaxies. The recovered Balrog injections are shown to closely match the photometric property distributions of the fiducial Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ~1-8 millimagnitudes, but for a small subset of objects we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. Finally, we discuss approaches to extend the current methodology to capture more aspects of the transfer function for future analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

High-Resolution Frequency-Domain Spectroscopic and Modeling Studies of Photosystem I (PSI), PSI Mutants and PSI Supercomplexes

Photosystem I (PSI) is one of the two main pigment–protein complexes where the primary steps of oxygenic photosynthesis take place. This review describes low-temperature frequency-domain experiments (absorption, emission, circular dichroism, resonant and non-resonant hole-burned spectra) and modeling efforts reported for PSI in recent years. In particular, we focus on the spectral hole-burning studies, which are not as common in photosynthesis research as the time-domain spectroscopies. Experimental and modeling data obtained for trimeric cyanobacterial Photosystem I (PSI 3 ), PSI 3 mutants, and PSI 3 –IsiA 18 supercomplexes are analyzed to provide a more comprehensive understanding of their excitonic structure and excitation energy transfer (EET) processes. Detailed information on the excitonic structure of photosynthetic complexes is essential to determine the structure–function relationship. We will focus on the so-called “red antenna states” of cyanobacterial PSI, as these states play an important role in photochemical processes and EET pathways. The high-resolution data and modeling studies presented here provide additional information on the energetics of the lowest energy states and their chlorophyll (Chl) compositions, as well as the EET pathways and how they are altered by mutations. We present evidence that the low-energy traps observed in PSI are excitonically coupled states with significant charge-transfer (CT) character. The analysis presented for various optical spectra of PSI 3 and PSI 3 -IsiA 18 supercomplexes allowed us to make inferences about EET from the IsiA 18 ring to the PSI 3 core and demonstrate that the number of entry points varies between sample preparations studied by different groups. In our most recent samples, there most likely are three entry points for EET from the IsiA 18 ring per the PSI core monomer, with two of these entry points likely being located next to each other. Therefore, there are nine entry points from the IsiA 18 ring to the PSI 3 trimer. We anticipate that the data discussed below will stimulate further research in this area, providing even more insight into the structure-based models of these important cyanobacterial photosystems.

59 BASIC BIOLOGICAL SCIENCES↗

Deconvoluting experimental decay energy spectra: The O 26 case

In nuclear reaction experiments, the measured decay energy spectra can give insights into the shell structure of decaying systems. However, extracting the underlying physics from the measurements is challenging due to detector resolution and acceptance effects. The Richardson-Lucy (RL) algorithm, a deblurring method that is commonly used in optics and has proven to be a successful technique for restoring images, was applied to our experimental nuclear physics data. The only inputs to the method are the observed energy spectrum and the detector's response matrix also known as the transfer matrix. We demonstrate that the technique can help access information about the shell structure of particle-unbound systems from the measured decay energy spectrum that is not immediately accessible via traditional approaches such as χ-square fitting. For a similar purpose, we developed a machine learning model that uses a deep neural network (DNN) classifier to identify resonance states from the measured decay energy spectrum. We tested the performance of both methods on simulated data and experimental measurements. Then, we applied both algorithms to the decay energy spectrum of 26 O → 24 O + n + n measured via invariant mass spectroscopy. Here, the resonance states restored using the RL algorithm to deblur the measured decay energy spectrum agree with those found by the DNN classifier. Both deblurring and DNN approaches suggest that the raw decay energy spectrum of 26 O exhibits three peaks at approximately 0.15 MeV, 1.50 MeV, and 5.00 MeV, with half-widths of 0.29 MeV, 0.80 MeV, and 1.85 MeV, respectively.

Spectrometers & spectroscopic techniques↗

Mechanistic Studies of the Oxidation of Cyclohexene to 2-Cyclohexen-1-one over ALD Prepared Titania Supported Vanadia

Selective oxidation of cyclohexene to 2-cyclohexen-1-one over titania supported vanadia (VO x /TiO 2 ) has been studied using temperature dependent in-situ FTIR spectroscopy in both the presence and absence of oxygen. The VO x /TiO 2 samples were prepared using one atomic layer deposition (ALD) cycle and characterized by Raman spectroscopy. In-situ FTIR data for the oxidation of cyclohexene and perdeuterocyclohexene allow for the formulation of a molecular level reaction mechanism, which is initiated by the transfer of an allyl hydrogen. Oxidation of perdeuterocyclohexene provides a direct probe of the formation of OD and HDO moieties that support the involvement of specific steps in the proposed mechanism. The presence of gas phase oxygen does not lead to a change in the products versus anaerobic conditions. However, gas phase oxygen is significantly incorporated in the CO 2 over-oxidation product above ~250 °C. Data were also obtained with cyclohexene epoxide as the reactant in an effort to determine whether there is a parallel reaction pathway, which is initiated by C=C activation in cyclohexene, that involves cyclohexene epoxide as an intermediate. Furthermore, though a minor pathway involving a cyclohexene epoxide intermediate cannot be ruled out, these data demonstrate that, under experimental conditions, the dominant pathway from cyclohexene to cyclohexene-1-one is initiated by an allyl-H activation step and does not involve an epoxide intermediate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data driven investigation to understand the influence of total solids on biological biogas upgrading

In situ biogas upgrading achieves CO 2 conversion to CH 4 via hydrogenotrophic methanogenesis; however, gas-liquid mass transfer constraints limit the upgrading performance. Recognizing that optimization studies often underrepresent the effects of total solids (TS) and organic loading rate (OLR), this study undertook a holistic, statistics driven assessment of operating conditions for in situ H 2 assisted biogas upgrading, centering the analysis on TS and OLR. A dataset of 31 studies was compiled and comprised 99 observations. A rigorous analytical framework was employed, combining data standardization, fixed- and random-effects (REML) weighted regressions with cluster-robust errors, stratified analyses, and machine learning. Mixed-effects meta regression indicated that TS was the main factor explaining differences of methane fraction (CH 4 %) when considering the between studies heterogeneity. Focusing on a near-stoichiometric subset (H 2 /CO 2 ≈ 4:1), TS remained significant. Stratified results showed a stronger negative relationship between TS and CH 4 % in UASB reactors than in CSTRs, with a negative effect under mesophilic conditions and no significant effect under thermophilic conditions. A Random Forest model corroborated the statistical findings, consistently ranking H 2 /CO 2 ratio, OLR, TS, and hydrogen injection rate (HIR) as the most influential predictors. These findings delineate trends across increasing TS levels, particularly between 1% and 10%, and provide preliminary insights for TS above 15% in in situ biogas upgrading. They further provide insights for the influence of TS by reactor type and temperature, thereby advancing the evidence base for implementing biological CO 2 conversion to CH 4 in practice.

In situ biogas upgrading↗

A Methodology for Simulating Supercritical CO2 Heat Transfer Experiments Using Machine Learning Models

In an effort to support the growth of supercritical carbon dioxide (sCO2) power cycles in the energy industry, this study seeks to train a machine learning model to mirror experimental data to inform future efforts and design features for both sCO2 heat exchangers and sCO2 turbine thermal management. There is a need for large amounts of experimental testing as there is less established literature about sCO2 used as a working medium in these cycles, as well as due to the influx of novel heat transfer designs presented by the advent of additive manufacturing.

Grabowski, Owen↗

Iron Single‐Atom Catalyzed N‐Alkylation of Amines with Alcohols via Solvent‐Free Borrowing Hydrogen Strategy

Abstract Industrial hydrogenation is a pivotal process in chemical synthesis. However, it has significant drawbacks, including high cost, safety risks associated with the use of molecular hydrogen gas, and substantial energy demands due to the need for elevated temperatures and pressures to achieve satisfactory yields. The borrowing hydrogen synthesis, which enables the transfer of hydrogen between molecules, offers a promising approach for green, one‐pot synthesis of industrially important chemicals and intermediates. Despite its potential, the broad application remains limited due to the reliance on toxic solvents, expensive noble metal catalysts, and the still restricted efficiency and substrate scope. In this study, the first solvent‐free strategy for theN‐alkylation of amines with alcohols is presented, employing an N‐doped graphene‐supported Fe single‐atom catalyst (Fe SA @N‐G; 1.06 wt.%). This approach achieves superior conversion and selectivity (up to 99%) along with record values for turnover number (TON, 1032.7) and turnover frequency (TOF, 413.1 h -1 ) for the coupling reaction of aniline with benzyl alcohol, surpassing all previously reported catalysts. DFT calculations, combined with experimental data, elucidated the reaction mechanism and identified the Fe 1 (III)‐N 4 active site participating in Fe‐H hydride transfer and containing two pyrrolic and two pyridinic nitrogens bound to the Fe center. The developed technology is further supported by the catalyst's excellent scalability, reusability, and performance under continuous‐flow conditions. Additionally, the exceptional efficiency of the single‐atom catalyst is demonstrated across more than 50 substrates, including reactions involving both aliphatic and aromatic amines with aliphatic and aromatic alcohols. The industrial applicability of this technology is validated through the synthesis of pharmaceutically relevant compounds, including stimulant drugs, antihistamines, and pharmaceutical intermediates.

Chemistry↗

Molecular origin of negative lithium transference in electrolytes with star-shaped multivalent anions

Large-scale molecular dynamics simulations illustrate that highly correlated cation–anion motion leads to negative t 0+ on the order of 1 in lithium electrolytes with star-shaped multivalent anions. Large multivalent anions have gained increasing attention for their potential to improve lithium transference in electrolytes. We employ large-scale molecular dynamics simulations based on the Onsager transport framework to investigate ion transport in a lithium electrolyte with star-shaped multivalent anions. The simulations show that t 0+, the cation transference number with respect to solvent velocity, is negative over a wide range of concentration. This is consistent with experimental data reported previously. The simulation-based Onsager transport coefficients reveal that the magnitudes of the cation–cation, anion–anion, and cation–anion correlations are comparable, a signature of highly correlated motion in the electrolyte. Examination of the cation solvation environment indicates the presence of strong cation–anion association across the entire concentration range, which leads to negative t 0+ on the order of −1. Both simulation and experiment also show that the maximum value of t 0+ reaches 0 when the cation concentration is c + = 0.4 M. This is the concentration at which the anions begin to spatially overlap, and lithium ions serve as dynamic linkers to balance cation–cation and cation–anion correlations. Our results provide molecular-level insights into the origin of transference in multivalent electrolytes.

Fang, Chao↗