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DOE OSTI2024

Studying microbially induced corrosion on glass using ToF-SIMS

Microbially induced corrosion (MIC) is an emerging topic that has huge environmental impacts, such as long-term evaluation of microbial interactions with radioactive waste glass, environmental cleanup and disposal of radioactive material, and weathering effects of microbes. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a powerful mass spectral imaging technique with high surface sensitivity, mass resolution, and mass accuracy, can be used to study biofilm effects on different substrates. Understanding how to prepare biofilms on MIC susceptible substrates is critical for proper analysis via ToF-SIMS. We present here a step-by-step protocol for preparing bacterial biofilms for ToF-SIMS analysis, comparing three biofilm preparation techniques: no desalination, centrifugal spinning (CS), and water submersion (WS). Comparisons of two desalinating methods, CS and WS, show a decrease in the media peaks up to 99% using CS and 55% using WS, respectively. Proper desalination methods also can increase biological signals by over four times for fatty acids using WS, for example. ToF-SIMS spectral results show chemical compositional changes of the glass exposed in a Paenibacillus polymyxa SCE2 biofilm, indicating its capability to probe microbiologically induced corrosion of solid surfaces. This represents the proper desalination technique to use without significantly altering biofilm structure and substrate for ToF-SIMS analysis. ToF-SIMS spectral results showed chemical compositional changes of the glass exposed by a Paenibacillus bacterial biofilm over 3-month inoculation. Finally, possible MIC products include various phosphate phase molecules not observed in any control samples with the highest percent increases when experimental samples were compared with biofilm control samples.

36 MATERIALS SCIENCE↗

DOE OSTI2025

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗

DOE OSTI2024

Novel microbial based enzymatic CO2 fixation mechanisms: Conformational control of enzymatic reactivity

The broad, long-term goal of the research is to provide insight into the mechanism of novel carboxylation and electron bifurcation reactions as paradigms to study how enzyme utilized protein conformational change to protect reactive intermediates and/or control the pathway in a catalytic cycle. catalyzed by enzymes involved in bacterial alkene, ketone, and epoxide metabolism. We are examining two paradigms in which conformational change is a key mechanistic feature of 1) protecting reaction intermediates that are sensitive to breakdown in the presence of water and 2) directing electron flow in reactions that split electron pairs along two pathways in reactions that fine tune metabolic efficiency in microbial metabolism. Both paradigms are of key importance in defining the molecular determinants of reactions relevant to CO 2 capture and fuel upcycling. In the first paradigm the compounds acetone and bicarbonate are activated by the addition of phosphate creating reaction intermediates (phosphoenolacetone and carboxyphosphate) that are very sensitive to breakdown in the presence of water. We are studying the mechanism of how the enzyme protects these reactive intermediates from water by encapsulating them upon their formation within a hydrophobic protein channel leading to an enzyme active site approximately 40 Å away. In the second paradigm, we are examining how the direction of electron transfer reactions can be modulated through protein conformational change that alters either the difference driving force or distance or both between electron transfer pathways in enzymes that catalyze electron bifurcation. We are using a combination of enzyme kinetics, mass spectrometry, spectroscopy, and structural analysis through X-ray diffraction and scattering methods to characterize the mechanism of conformational changes along the catalytic path. Within the context of the mission of the Department of Energy and the core activities of the Energy Biosciences, the results obtained in the study will reveal new insights into the fundamentals of novel carboxylation / CO 2 capture chemical transformations and electron transfer reactions that equate to biological fuel upcycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Facet-dependent growth and dissolution of hematite resulting from autocatalytic interactions with Fe(II) and oxalic acid

The ability to simultaneously monitor the flux of iron atoms within the solution and solid phases can provide considerable insight into mechanisms of iron oxide mineral transformations. The autocatalytic interaction between hematite and Fe(II)-oxalate has long been of interest for its environmental and industrial relevance. In this study we take advantage of iron isotopic labelling and mass-sensitive imaging at the single particle scale to determine how changes in solution composition correlate with the morphologic evolution of faceted, micrometer-sized hematite platelets. Net dissolution is confirmed through analyses of aqueous iron chemistry, as well as by quantitative atomic force microscopy. Isotopic mapping techniques show that Fe(II) readily adsorbs to (001) and (012) surfaces in the absence of oxalate, but when oxalate is present selective dissolution of the (001) surface prevails and 57Fe deposition via recrystallization is not observed. Comparison between particle microtopographies following reaction with Fe(II), oxalate, and Fe(II)-oxalate show substantially different behavior, consistent with distinct mechanisms of interaction with hematite surfaces. The extensive characterization conducted on the coupled solution/solid dynamics in this system provides new insight for distinguishing crystal growth, dissolution, and recrystallization processes.

Taylor, Sandra D. [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2026

Fading study of Harshaw LiF:Mg,Ti thermoluminescence dosimeters exposed to neutron doses

Sensitivity and signal fading were tested in Harshaw LiF:Mg,Ti thermoluminescence dosimeters (TLDs) exposed to neutron and beta/photons doses. A significant difference in the signal fading rate was observed for TLDs exposed to neutrons compared with TLDs exposed to betas. This difference may have been related to differences in glow curves for dosimeters exposed to different beams and to differences in glow curve evolution after exposure. A fading-correction algorithm was developed to correct the cumulative sensitivity + signal fading of TL doses for the fading time t using the formulae: y = 0.147∗exp(-t/4.36) + 0.308∗exp(-t/34.1) + 0.73 for neutrons, and: y = 0.132∗exp(-t/10.72) + 0.174∗exp(-t/31.5) + 0.8 for betas/photons. The formula was validated using the dose recovery test, where the average deviation of fading-corrected doses from delivery doses was within 1%. In conclusion, the proposed fading-correction approaches may significantly improve the accuracy of Harshaw TL dosimetry with LiF:Mg,Ti cards for both neutron and beta/gamma irradiation.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Results from a multi-laboratory ocean metaproteomic intercomparison: effects of LC-MS acquisition and data analysis procedures

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from seven laboratories through a common bioinformatic pipeline identified a shared set of 1056 proteins from 1395 shared peptide constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R 2 values averaged 0.62±0.11, and a Sørensen similarity analysis of the top 1000 proteins revealed 70 %–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. A bioinformatic intercomparison study, involving 10 laboratories using eight software packages, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Lessons learned and potential improvements in methods were described. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research, including integration into global-scale ocean surveys and ocean biogeochemical models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Shake-Down Spectroscopy as State- and Site-Specific Probe of Ultrafast Chemical Dynamics

Tracking the multifarious ultrafast electronic and structural changes occurring in a molecule during a photochemical transformation is a challenging endeavor that benefits from recent experimental and computational progress in time-resolved techniques. Measurements of valence electronic states, which provide a global picture of the bonding structure of the molecule, and core electronic states, which provide insight into the local environment, traditionally require different approaches and are often studied separately. Here, we demonstrate that X-ray pulses from a seeded free-electron laser (FEL) enable the measurement of high-resolution, time-resolved X-ray photoelectron spectra (XPS) that capture weak satellite states resulting from shake-down processes in a valence-excited molecule. This approach effectively combines the advantages of both valence- and core-state investigations. We applied this method to investigate photoexcited CS2 molecules, where the role of internal conversion (IC) and intersystem crossing (ISC) in determining the predissociation dynamics is controversial. We present XPS spectra from photoexcited CS2, obtained at the FERMI FEL. High-resolution measurements, compared to the corresponding spectra obtained from accurate multireference quantum chemical calculations, reveal that shake-down satellite channels are highly sensitive to both valence electronic and geometric changes. Previous studies of the predissociation dynamics have led to uncertain assignments of the branching between singlet and triplet excited states. We derive a propensity rule that demonstrates the spin-selectivity of the shake-downs. This selectivity allows us to unequivocally assign contributions from the bright and dark singlet excited states, with populations tracked along the predissociation dynamic pathway.

Thompson, Henry J. [Univ. of Southampton (United K↗

DOE OSTI2026

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN↗

DOE OSTI2025

The Effect of Hydrothermal Alteration and Microcracks on Hydraulic Properties and Poroelastic Deformation: A Case Study of the Blue Mountain Geothermal Field

Abstract Geothermal energy plays a vital role in decarbonizing electricity and heat supply. Effective utilization of geothermal resources hinges on identifying or generating permeable reservoir zones and understanding how effective pressure variations affect fluid circulation and reservoir properties by poroelastic deformation. Hydrothermal alteration can modify the petrophysical properties of geothermal reservoir rocks, which may increase or decrease its productivity. Understanding these alteration effects is essential to predict and optimize long‐term sustainable geothermal operations. Here, we investigate the impact of hydrothermal alteration on poroelastic and hydraulic properties of diverse lithologies in a series of deformation tests performed at several confining (0–80 MPa) and pore pressure (10–30 MPa) levels. Experimental results of hydrothermally altered dikes and phyllites obtained from the Blue Mountain geothermal field (Nevada, USA) are compared to thermally cracked La Peyratte granite (France) and correlated with petrophysical properties, mineral composition, and microstructures. Argillic alteration of dikes increases porosity and storage capacity but lowers thermal conductivity and increases pore compressibility. Conversely, silicate precipitation in phyllites increases stiffness and thermal conductivity but also reduces porosity and permeability. Experimentally determined effective pressure coefficients range from 0.1 to 0.9, differ for permeability and volumetric strain and decrease with increasing effective pressure. The presence of compliant microcracks and crack‐like pores significantly increases the stress sensitivity of La Peyratte granite and silicified phyllites. This study demonstrates how thermal and chemical alteration impacts poromechanical and petrophysical characteristics of geothermal targets, which ultimately govern reservoir stability and subsidence, induced seismicity as well as fluid and heat extraction efficiency during geothermal operations.

Schuster, Valerian [Helmholtz Centre Potsdam GFZ G↗

DOE OSTI2024

New physical processes for extracting GPDs with a better sensitivity to partonic structure

We introduce a new type of exclusive processes for a better study of generalized parton distributions (GPDs), which we refer to as single-diffractive hard exclusive processes (SDHEPs). We advocate a two-stage framework for picturing SDHEPs based on the separation of scales, which gives a clear description both kinematically and dynamically. We examine the sensitivity of the SDHEP to the parton momentum fraction x -dependence of GPDs, and demonstrate it quantitatively with two specific processes that can be readily measured at J-PARC or AMBER using a pion beam and at JLab using a photon beam, respectively. Both processes are capable of providing enhanced sensitivity to the x -dependence, overcoming the problem of shadow GPDs, and disentangling different types of GPDs with various spin asymmetries.

Qiu, Jianwei↗

DOE OSTI2025

Nucleation and Antiphase Twin Control in Bi 2 Se 3 via Step‐Terminated Al 2 O 3 Substrates

The epitaxial synthesis of high-quality 2D layered materials is an essential driver of both fundamental physics studies and technological applications. Bi 2 Se 3 , a prototypical 2D layered topological insulator, is sensitive to defects imparted during the growth, either thermodynamically or due to the film-substrate interaction. Here, in this study, it is shown that step-terminated Al 2 O 3 substrates with a high miscut angle (3°) can effectively suppress a particular hard-to-mitigate defect, the antiphase twin. Systematic investigations across a range of growth temperatures and substrate miscut angles confirm that atomic step edges act as preferential nucleation sites, stabilizing a single twin domain. First-principles calculations suggest that there is a significant energy barrier for twin boundary formation at step edges, supporting the experimental observations. Detailed structural characterization indicates that this twin-selectivity is lost through the mechanism of the 2D layers overgrowing the step edges, leading to higher twin density as the thickness increases. These findings highlight the complex energy landscape unique to 2D materials that is driven by the interplay between substrate properties, nucleation dynamics, and defect formation, and overcoming and controlling these are critical to improve material quality for quantum and electronic applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Accelerated Carbon and Water Cycles in the Amazon and Congo Basins Revealed From TRENDY Models and Remote Sensing Products

Tropical forests play a vital role in the global carbon cycle and land–atmosphere interactions. Estimating tropical forest carbon–water dynamics is challenging due to observational and modeling uncertainties. This study leverages the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project models and satellite observations to assess changes (2003–2021) in vegetation carbon, gross primary production (GPP), evapotranspiration (ET), and net biosphere production (NBP) in the Amazon and Congo. Atmospheric CO 2 , climate variability, and land use and land cover changes constrain these variables between 1700 and 2021 with the overall increasing trends of carbon stock and fluxes. The models overestimate vegetation carbon and GPP, while ET and NBP are consistent with observations. Fire-activated models predict lower values for vegetation carbon and GPP, ET, and NBP, aligning more closely with observations. The higher ET from fire-activated models may result from enhanced soil evaporation due to increased canopy openings. Fire-inactivated models could well estimate the magnitudes of NBP. The high vegetation carbon in nitrogen-enabled models points to simulation uncertainties and imbalance in model numbers regarding the nitrogen cycle. Although the nitrogen cycle enhances water use efficiency in both the Amazon and Congo, the models show a higher sensitivity to the nitrogen cycle in the Congo. This study highlights the challenges in accurately representing tropical biogeochemical cycles and the values of satellite products in model evaluations, underscoring the need for standard modeling protocols that address biogeochemical components (e.g., nutrient cycles) to better resolve process-based representations.

Shi, Mingjie [Pacific Northwest National Laborator↗

DOE OSTI2025

Defect-engineered GaN high electron mobility transistors for enhanced radiation tolerance

This study explores the radiation resilience of GaN high-electron-mobility transistors (HEMTs) under various preconditioning treatments—pristine, electro-pulsed, electrically overstressed, and 60 Co gamma-irradiated—with a focus on enhancing survivability in high-radiation environments. Key findings emphasize the gate region’s central role in radiation sensitivity, with increased defect concentrations leading to reduced sensitivity—as determined from reduced single-event transient intensities during pulsed-laser studies—and impacting charge-transport dynamics. Notably, the most sensitive region shifted as a function of bias state, with the drain side of the gate being most sensitive under negative gate bias and the source side under unbiased conditions due to two-dimensional electron gas disruptions. These insights clarify the mechanisms influencing radiation sensitivity in microelectronics and highlight the potential of defect and strain engineering to enhance the radiation hardness of GaN HEMTs across various operational states, providing valuable guidance for applications in space, defense, and other radiation-rich environments.

36 MATERIALS SCIENCE↗

DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗

DOE OSTI2025

Modeled sensitivity of multi-MA accelerator performance to electrode contaminant inventory

Significant particle-in-cell code development has enabled simulations of power flow in multi-MA accelerators to include the desorption of surface contaminants, their ionization into surface plasmas, and the impact of these plasmas on efficiency. The simulations base desorption on an Arrhenius equation, whose most significant unknown is the surface contaminant inventory. The sensitivity of power-flow simulations to this inventory is studied here using Sandia National Laboratories' Z accelerator with a 7-nH MagLIF load [Phys. Plasmas 17, 056303 (2010)]. Simulations are conducted in 3D cylindrical coordinates for the current-adder, or “convolute,” region of Z and in 2D for the final feed only. Simulated contaminant inventories are varied from 1 to 32 monolayers (MLs) in 2D, and 2 to 4 ML in 3D. The results reveal sensitivities to the local ratio of E/B⁠. The high B-field, low E-field region near the short-circuit load is insensitive to the contaminant inventory, where assumed values of 4–32 ML change the load current by ≤ 2%, and agree with experiment to within 2% at peak current. A 1-ML value is the outlier, increasing the load current by 5%, but still within measurement uncertainty. In contrast, the relatively higher E-field, lower B-field convolute region has slower contaminant desorption and higher-magnitude E-field penetration of the surface plasmas. The current loss in the convolute region does increase with contaminant inventory. The loss assuming 4 ML is 12% larger than for 2 ML, with 4 ML being the better match to experiment.

Arrhenius equation↗

DOE OSTI2025

In Situ Analysis of Electron-Induced Chemical Transformations in Vapor-Phase-Synthesized Al-Based Inorganic–Organic Hybrid Thin Films for EUV Resist Platform

The rapid advancement and stringent requirements of extreme ultraviolet (EUV) lithography technology necessitate the development of advanced photoresist systems for next-generation microelectronics. Recent studies have demonstrated that inorganic-based hybrid photoresists offer notable improvements in EUV sensitivity, etch resistance, and greater insusceptibility to pattern collapse compared to their purely organic counterparts. However, variations in the synthesis/coating approaches and chemistry of inorganic–organic photoresists can result in distinct exposure mechanisms. In this work, an Al-based hybrid thin film resist system synthesized via molecular (atomic) layer deposition (MLD or MALD) is explored, focusing on its electron-beam and EUV patterning mechanisms. The Al-based hybrid thin films are deposited using trimethylaluminum (TMA) and the organic precursor hydroquinone, exhibiting a saturated growth rate within the temperature range of 150–200°C. In diluted tetramethylammonium hydroxide (TMAH)-based developer solutions, the electron-irradiated Al-based hybrid thin film system behaves as a negative tone resist, achieving a sensitivity of 10.4 mC/cm 2 at 0.1 kV electron beam lithography (EBL). Chemical changes induced by electron exposure are also analyzed in this study using X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and a unique infrared spectroscopy setup, revealing the potential cross-linking pathways. To further correlate the electron-induced chemical transformations with those mediated by EUV irradiations, a combination of X-ray photoemission electron microscopy/low-energy electron microscopy (XPEEM/LEEM) system is also employed. In conclusion, this study provides critical insights into the mechanisms underlying solubility switching and contributes to the design of advanced resist materials for EUV lithography.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗

DOE OSTI2024

Dependence of tropical cyclone seeds and climate sensitivity on tropical cloud response

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth’s surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase.

Science & Technology - Other Topics↗