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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine learning framework for quantum sampling of highly constrained, continuous optimization problems

In recent years, there is growing interest in using quantum computers for solving combinatorial optimization problems. In this work, we developed a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate quadratic unconstrained binary optimization (QUBO) problems by employing a binary variational autoencoder and a factorization machine. The factorization machine is trained as a low-dimensional, binary surrogate model for the continuous design space and sampled using various QUBO samplers. Using the D-Wave Advantage hybrid sampler and simulated annealing, we demonstrate that by repeated resampling and retraining of the factorization machine, our framework finds designs that exhibit figures of merit exceeding those of its training set. We showcase the framework’s performance on two inverse design problems by optimizing (i) thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering. This technique can be further scaled to leverage future developments in quantum optimization to solve advanced inverse design problems for science and engineering applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Correlations of Platooning Track Test and Wind Tunnel Data

In this report, the National Renewable Energy Laboratory analyzed results from multiple, independent truck platooning projects to compare and contrast track test results with wind tunnel test results conducted by Lawrence Livermore National Laboratory (LLNL). Some highlights from the report include compiled data, and results from four independent SAE J1321 full-size track test campaigns that were compared to LLNL wind tunnel testing results. All platooning scenarios tested demonstrated significant fuel savings with good correlation relative to following distances, but there are still unanswered questions and clear opportunities for system optimization. NOx emissions showed improvements from NREL tests in 2014 to Auburn tests in 2015 with respect to J1321 platooning track testing of Peloton system. NREL evaluated data from Volpe's Naturalistic Study of Truck Following Behavior, which showed minimal impact of naturalistic background platooning. We found significant correlation between multiple track studies, wind tunnel tests, and computational fluid dynamics, but also showed that there is more to learn regarding close formation and longer-distance effects. We also identified potential areas for further research and development, including development of advanced aerodynamic designs optimized for platooning, measurement of platoon system performance in traffic conditions, impact of vehicle lateral offsets on platooning performance, and characterization of the national potential for platooning based on fleet operational characteristics.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

FY25 Electric Grid Security Annual Report

Sandia’s Electric Grid Security program advances a national vision of energy dominance and accessibility, while applying our national security -emphasis on ensuring of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY25 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • New open-source analytical tools for systems -level planning and optimization, including significant advances to the QuESt analytical environment; • Further advancement of artificial intelligence and machine learning to enhanced grid operations and planning as we rise to the challenge of new large loads; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by several offices within the U.S. Department of Energy (USDOE), including the Office of Electricity (OE); Cybersecurity, Energy Security, and Emergency Response (CESER); former offices such as the Office of Energy Efficiency and Renewable Energy (EERE), the Grid Deployment Office (GDO), the Office of Clean Energy Demonstrations (OCED), and other key programs at USDOE. As we continue to state in these annual reports, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Thermal Management and Reliability of Power Electronics and Electric Machines

Increasing the number of electric-drive vehicles (EDVs) on America's roads has been identified as a strategy with near-term potential for dramatically decreasing the nation's dependence on oil - by the U.S. Department of Energy, the federal cross-agency EV-Everywhere Challenge, and the automotive industry. Mass-market deployment will rely on meeting aggressive technical targets, including improved efficiency and reduced size, weight, and cost. Many of these advances will depend on optimization of thermal management. Effective thermal management is critical to improving the performance and ensuring the reliability of EDVs. Efficient heat removal makes higher power densities and lower operating temperatures possible, and in turn enables cost and size reductions. The National Renewable Energy Laboratory (NREL), along with DOE and industry partners is working to develop cost-effective thermal management solutions to increase device and component power densities. In this presentation, the activities in recent years related to thermal management and reliability of automotive power electronics and electric machines are presented.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2015

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated using a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Frozen Freedom: Unleashing Grocery Store Demand Flexibility: Preprint

Grocery stores consumed approximately 3% of total electricity used by commercial buildings in the U.S. in 2018 (EIA 2018), representing a unique end-use load profile characterized by the critical use of refrigerated display cases. Exploring demand response (DR) scenarios in grocery stores presents an opportunity to enhance the efficiency and sustainability of surrounding communities. In addition, recent studies demonstrate that implementing control algorithms considering demand flexibility strategies can lead to load and peak reductions in standalone refrigerated display cases. Because small business grocery stores operate on thin margins, the energy bill cost savings DR might provide could make a positive difference toward continued operations. Still, uncertainty remains about the extent of demand flexibility potential controls could provide when coupling refrigeration with whole building operation. To enhance economic viability and grid stability, it is essential to quantify the load flexibility capability of grocery stores. Advanced controls can optimize energy consumption by responding to load shedding, shifting, and DR events, as well as daily Time-of-Use (TOU) rates without compromising food safety. Using both quantitative data and interviews with community-based organizations, we developed a full-size store model and two small store models with controlled refrigerated cases, HVAC, and lighting systems based on actual grocery store properties. Through simulations, we have assessed load flexibility strategies with varied DR events. The results highlight potential for energy and peak reduction with advanced or basic controls. However, interviews and data indicate that more support is needed to make DR strategies consistently accessible to small grocery stores.

demand flexibility↗
DOE OSTI2019

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science

Despite their wide and successful applications, deep learning (DL) models are prone to overfitting for small training datasets, produce a poor predictive performance for uncertain data, and provide point estimations without any indication of the accuracy and credibility. These limitations of the deterministic DL models hinder their effective application in Earth system science where the labelled data are sparse, noisy and incomplete with large uncertainty and where the predictive uncertainty quantification is needed for scientific understanding and policy decision making. Integration of Bayesian inference into DL models adds an estimate of uncertainty and regularization in the predictions. However, traditional Bayesian methods are computationally unaffordable and inflexible for high-dimensional problems, which limits their application to DL systems that typically have millions of model parameters. In this effort, we propose an efficient and general-purpose Bayesian inference method to advance DL model optimization and uncertainty quantification, so as to facilitate the adoption of DL in Earth sciences. In a demonstration, we integrate the proposed Bayesian method with a feedforward neural network (NN) to build a fast-to-evaluate surrogate of the complex Energy Exascale Earth System Land Model for efficient modeling. The formulated Bayesian NN, using a small number of training data, produces an accurate prediction with high credibility, whereas with the same small training size, the deterministic NN cannot yield a reasonable estimation and does not provide confidence information. The proposed Bayesian method is computationally efficient and flexible, capable of integration with diverse network variants such as convolutional NNs and recurrent NNs to advance the application of DL in Earth sciences.

Lu, Dan↗
DOE OSTI2025

Binder Effects on Processing, Mechanical Properties, and Performance of Thin Sulfide Solid‐State Electrolytes

All‐solid‐state batteries (ASSBs) offer enhanced safety and energy density compared to conventional lithium‐ion batteries by replacing flammable liquid electrolytes with solid‐state electrolytes (SSEs). Among SSEs, sulfide‐based electrolytes exhibit high ionic conductivity and mechanical deformability, making them promising candidates for next‐generation energy storage. However, their practical implementation is hindered by interfacial instability, mechanical brittleness, and challenges in fabricating ultrathin electrolyte membranes (<30 μm) with robust mechanical integrity. Here, this study systematically examines the influence of polymeric binders—polyisobutylene, hydrogenated nitrile butadiene rubber, and styrene–ethylene–butylene–styrene (SEBS)—on the structural, mechanical, and electrochemical performance of thin sulfide SSE membranes. Key findings reveal that SEBS enables the fabrication of ultrathin, uniform membranes, while binder elasticity significantly affects structural stability during cycling. Operando stack pressure measurements indicate that binder properties directly influence adhesion force for LPSCl particles, influencing their stabilizing cycling period. These results underscore the critical role of polymer binders beyond mechanical reinforcement, positioning them as essential design variables in sulfide SSE engineering. By linking binder chemistry to processability and electrochemical performance, this study provides insights into optimizing sulfide SSEs, advancing their commercial viability in ASSBs.

argyrodite sulfide electrolyte↗
DOE OSTI2022

Modeling of the effects of non-equilibrium excitation and electrode geometry on H 2 /air ignition in a nanosecond plasma discharge

In this work, we present the results of two-dimensional modeling of the effects of non-equilibrium excitation and electrode geometry on H 2 /air ignition in a nanosecond plasma discharge. A multiscale adaptive reduced chemistry solver for plasma assisted combustion (MARCS-PAC) based on PASSKEy discharge modeling package and compressible multi-component reactive flow solver ASURF+ is developed and validated. This model is applied to simulate the impact of non-equilibrium plasma excitation and electrode geometry and heat loss on the dynamics of the discharge from streamer to spark and ignition kernel development in a H 2 /air mixture with a pair of cylindrical electrodes. The results show that the plasmagenerated species (N 2 (A), N 2 (B), N 2 (a'), N 2 (C), O( 1 D), O and H) in the spark and afterglow significantly accelerate the ignition kernel development. The increase of discharge voltage at the same total discharge energy promotes the non-equilibrium active species production. It is found that the production of electronically excited species at higher reduced electric field strength is more efficient in enhancing ignition in comparison to the vibrational excitation and heating. Moreover, the 2D simulation clearly reveals that the electric field and active species distribution are highly non-uniform. The streamers are initiated at the sharp outer edges of the negative and positive electrodes by a strong electric field while the electric field is much weaker at the centerline of the electrodes. Furthermore, the simulations reveal that the ignition enhancement is sensitive to the variation of electrode shape, diameter, and gap size due to the changes of electric field distribution and location of streamer formation. A cylindrical electrode produces a larger discharge volume and ignition kernel than the parabolic and spherical electrodes, when the discharge is localized near the axis of the gap. It is found that there is a non-monotonic dependence of ignition kernel size on the electrode diameter and inter-electrode distance. The increase of electrode diameter and gap size above the optimal conditions leads to the reduction of ignition kernel volume, due to the decrease of active species concentration and gas temperature. At a larger electrode surface area and electrode diameter as well as smaller electrode gap size, the heat loss to electrode plays a greater role in reducing the ignition kernel size and slowing ignition kernel development. This work provides insights and guidance to understand the kinetic enhancement of non-equilibrium plasma and the effects of electrode geometries on ignition for the optimization ignitors in advanced engines.

42 ENGINEERING↗
DOE OSTI2026

Optimizing district energy systems by integrating Borehole Thermal Energy Storage Using a Mixed-Integer Linear Programming g-function framework with a Multi-Timescale Rolling Horizon method

Shallow geothermal has gained increasing attention in recent years; however, a reliable framework for its accurate incorporation into large-scale energy system optimization remains lacking. This study proposes a Mixed-Integer Linear Programming (MILP) framework combined with the g-function approach to integrate Borehole Thermal Energy Storage (BTES) technology into energy system optimization. Validation against a Modelica-based reservoir network simulation demonstrates that the proposed framework effectively captures the ground thermal response under varying energy loads and accurately estimates the borefield energy supply. To enhance scalability, a Rolling Horizon with Multi-Timescale (RH-MTS) method is further introduced, reducing computational time by 73 % for the 1-year optimization model with only minor loss of optimality. The framework is demonstrated through the case study of the UC Berkeley campus. Results indicate that BTES is a cost-effective and low-carbon solution: two borefields comprising 382 boreholes can meet 8.0 % and 6.6 % of the total campus heating and cooling demand, respectively, at an average energy rate of 0.70–0.77 USD/kWh and carbon intensity of 0.54 kg-CO2/kWh. Short-term analysis reveals a 35%–65% decline in BTES energy flow after 3–6 months of continuous heating/cooling operation, while long-term simulation shows that annual energy production of BTES can vary by up to 12.0 % after four years before stabilizing. Overall, this study develops a novel optimization framework that couples physics-based g-function method with MILP optimization framework, thereby advancing methodological development for shallow-geothermal integration and providing actionable guidance for BTES deployment in district-energy systems.

Yang, Jiahui↗
DOE OSTI2025

Comparative life cycle assessment of a modular cross-laminated timber residential building designed for disassembly and reuse versus traditional wood frame construction

There is a need for affordable housing across the U.S., with high-performance modular and prefabricated buildings providing a logical avenue for meeting some of this demand. However, there is a need to balance high performance construction – including low emissions – with affordability. To provide a proof-of-concept in meeting these goals, the Circular Home is a cross-laminated timber (CLT)-based deconstructible and reconfigurable single-family residence that meets high performance targets in moisture, energy, design, economics, and life cycle assessment (LCA). This study focuses on the LCA, presenting a cradle-to-cradle whole-building life cycle assessment (WBLCA) for the Circular Home and a functionally equivalent Baseline Home constructed with traditional materials and methods. The functional unit is 1 m 2 of gross floor area across 60 years. Revit building information models (BIM) provided material quantities and Tally LCA was utilized for impact data (inclusive of biogenic carbon sequestration), supplemented with manufacturer environmental product declarations (EPDs). The Circular Home outperforms the baseline residence in most measured impact categories, including global warming potential (GWP), producing −2.73 kgCO 2 eq/m 2 in embodied emissions, whereas the modeled baseline has an embodied GWP of 428 kgCO 2 eq/m 2 . The careful material selection and advanced building design optimizes performance, with the Circular Home containing only −0.006 times the embodied emissions and −0.02 times the operational emissions of its traditional counterpart. Finally, the unique contribution of this work is in the environmental impact comparison of a high-performance modular CLT structure that can be affordably scaled and mass produced in a U.S. market, compared to typical single family home construction.

Circularity↗
DOE OSTI2025

Conditional guide RNA deactivation by mRNA and small molecule triggers in Saccharomyces cerevisiae

CRISPR interference (CRISPRi) technologies have revolutionized bioengineering by providing precise tools for gene expression modulation, enabling targeted gene perturbation and metabolic pathway optimization. Despite these advances, achieving dynamic control over gene expression by CRISPR-based regulation remains a challenge due to its inherently static nature. Utilizing toehold-mediated strand displacement and ligand-responsive ribozymes (aptazymes), this study introduces switchable guide RNAs (gRNAs) that facilitate tunable gene expression mediated by mRNA or small molecule signals. We demonstrate complete silencing of gRNA via strategically designed 5’ or 3’ extensions that impede the gRNA spacer or the dCas9 handle, with subsequent restoration of function through sequestration or cleavage of the obstructive sequence. The resulting toehold-embedded or aptazyme-embedded gRNAs can be deactivated by specific signals, including two full-length translatable mRNAs and two small molecule triggers, thereby lifting CRISPRi repression on targeted genes. This modular approach allows for gRNA-based biocomputing through multi-layer or multi-input genetic logic gates in Saccharomyces cerevisiae . Offering a versatile strategy for post-CRISPR regulation in response to environmental signals or cellular states, this methodology expands the toolkit in eukaryotic systems for reversible control of gene expression.

Aptazyme↗
DOE OSTI2023

Theoretical Perspective of Promoting Direct Methane-to-Methanol Conversion at Complex Metal Oxide–Metal Interfaces

Direct methane conversion to methanol has been considered as an effective and economic way to release greenhouse effects and current high demand on methanol in industry. However, the process has long been challenging due to lack of viable catalysts to compromise the activation of methane that typically occurs high temperatures and retaining of produced methanol that requires mild conditions. Here, this perspective demonstrates an effective strategy to promote direct methane to methanol conversion by engineering the active sites and chemical environments at complex metal oxide - copper oxide - copper interfaces. Such effort strongly depends on the extensive theoretical studies by combining density functional theory (DFT) calculations and kinetic Monte Carlo (KMC) simulations to provide in-depth understanding of reaction mechanism and active sites, which built the strong basis to enable the identification of design principles and advance the catalyst optimization for selective CH 4 -to-CH 3 OH conversion.

03 NATURAL GAS↗
DOE OSTI2019

Resolving the Ultrafast Changes of Chemically Inequivalent Metal–Ligand Bonds in Photoexcited Molecular Complexes with Transient X-ray Absorption Spectroscopy

Photoactive transition-metal complexes that incorporate heteroleptic ligands present a fast coordination shell, which is asymmetric. While it is generally expected that the metal-ligand bond lengths respond differently to photoexcitation, resolving these fine structural changes remains experimentally challenging, especially for flexible multidentate ligands. Here, ultrafast X-ray absorption spectroscopy is employed to capture directly the asymmetric elongations of chemically inequivalent metal-ligand bonds in the photoexcited spin-switching Fe-II complex [Fe-II(tpen)](2+) solvated in acetonitrile, where tpen denotes N,N,N',N'-tetrakis(2-pyridylmethyl)-1,2-ethylenediamine. The possibility to correlate precisely the nature of the donor/acceptor coordinating atoms to specific photoinduced structural changes within a binding motif will provide advanced diagnostics for optimizing numerous photoactive chemical and biological building blocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A biochemical framework for anaerobic oxidation of methane driven by Fe(III)-dependent respiration

Consumption of methane by aerobic and anaerobic microbes governs the atmospheric level of this powerful greenhouse gas. Whereas a biochemical understanding of aerobic methanotrophy is well developed, a mechanistic understanding of anaerobic methanotrophy has been prevented by the unavailability of pure cultures. Here we report a biochemical investigation of Methanosarcina acetivorans, a methane-producing species capable of anaerobic methanotrophic growth dependent on reduction of Fe(III). Our findings support a pathway anchored by Fe(III)-dependent mechanisms for energy conservation driving endergonic reactions that are key to methanotrophic growth. The pathway is remarkably similar to pathways hypothesized for uncultured anaerobic methanotrophic archaea. The results contribute to an improved understanding of the methane cycle that is paramount to understanding human interventions influencing Earth’s climate. Finally, the pathway enables advanced development and optimization of biotechnologies converting methane to value-added products through metabolic engineering of M. acetivorans.

09 BIOMASS FUELS↗
DOE OSTI2025

Non-fluorinated electrolyte for high-voltage anode-free sodium metal battery

Abundant sodium (Na) batteries are a sustainable alternative to resource-constrained lithium-ion batteries, offering huge cost advantages. However, developing high-voltage anode-free sodium metal batteries (SMBs) to narrow the energy density gap with lithium-ion batteries is hindered by a critical challenge: existing electrolytes cannot simultaneously achieve ultra-high Na coulombic efficiency and anodic stability. Here, in this study, we present a rationally designed non-fluorinated electrolyte (1.0 M NaPF 6 in 1,2-diethoxyethane/1,2-di-tert-butoxyethane) to address this key limitation, achieving Na coulombic efficiency of >99.95% and anodic stability of >4.8 V. For coin cells (2.0 mAh cm −2 , N/P = 1.7), our electrolyte design enables 4.0 V Na | |Na 3 V 2 (PO 4 ) 3 (NVP) at 5 C and 4.3 V Na | |NaNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) at 0.3 C for 5,000 and 500 cycles with a capacity retention >80%. Remarkably, the 50 mAh anode-free pouch cells 4.0 V Al | |NVP and 4.3 V Al | |NMC622 also achieve 500 and 300 cycles (retention >75%) with a specific energy of >360 Wh kg (electrode) −1 . This work focuses on electrolyte optimization and conceptual advances, whereas critical aspects such as safety, large-scale manufacturability and practical feasibility of SMBs require further investigation. The electrolyte design using non-fluorinated solvents enhances the anodic stability without sacrificing Na efficiency, laying groundwork for advancing low-cost, high-energy SMBs and supporting the transition to sustainable battery technologies.

25 ENERGY STORAGE↗
DOE OSTI2022

A Unified Protocol for CRISPR/Cas9-Mediated Gene Knockout in Tephritid Fruit Flies Led to the Recreation of White Eye and White Puparium Phenotypes in the Melon Fly

Abstract Tephritid fruit flies are among the most invasive and destructive agricultural pests worldwide. Over recent years, many studies have implemented the CRISPR/Cas9 genome-editing technology to dissect gene functions in tephritids and create new strains to facilitate their genetics, management, and control. This growing literature allows us to compare diverse strategies for delivering CRISPR/Cas9 components into tephritid embryos, optimize procedures, and advance the technology to systems outside the most thoroughly studied species within the family. Here, we revisit five years of CRISPR research in Tephritidae and propose a unified protocol for candidate gene knockout in fruit flies using CRISPR/Cas9. We demonstrated the efficiency of our protocol by disrupting the eye pigmentation gene white eye (we) in the melon fly, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae). High rates of somatic and germline mutagenesis were induced by microinjecting pre-assembled Cas9-sgRNA complexes through the chorion of embryos at early embryogenesis, leading to the rapid development of new mutant lines. We achieved comparable results when targeting the we orthologue in the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), illustrating the reliability of our methods when transferred to other related species. Finally, we functionally validated the recently discovered white pupae (wp) loci in the melon fly, successfully recreating the white puparium phenotype used in suppression programs of this and other major economically important tephritids. This is the first demonstration of CRISPR-based genome-editing in the genus Zeugodacus, and we anticipate that the procedures described here will contribute to advancing genome-editing in other non-model tephritid fruit flies.

Entomology↗