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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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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

A Broad-Band X-Ray Study of the Geminga Pulsar

We present a comprehensive study of the Geminga pulsar at energies 0.1-10 keV using data from the ASCA, ROSAT, and EUVE satellites. The bulk of the soft X-ray flux can be parameterized as a blackbody of T = (5.6 +/- 0.6) x 10(exp 5) K, occupying a fraction 0. 10 - 0.64 of the surface area of the neutron star at the parallax distance of 160 pc. The ASCA detection of Geminga resolves the nature of the harder X-ray component previously discovered by ROSAT in favor of nonthermal emission, rather than thermal emission from a heated polar cap. The hard X-ray spectrum can be fitted by a power-law of energy index 1.0 +/- 0.5. The hard X-ray light curve has a strong main peak and a weak secondary peak; its total pulsed fraction is = 55%. Three ROSAT PSPC observations show significant variability of Geminga's light curve. In particular, a peculiar energy dependence of the modulation in the soft X-ray component, dubbed the "Geminga effect" in the original PSPC data, is not present in later observations. In addition, fine structure in the soft X-ray light curve, interpreted as eclipses due to cyclotron resonance scattering by a plasma screen on the closed magnetic field lines, almost disappeared in the most recent observations. All of the variable properties of Geminga can probably be associated with the nonthermal process that supplies e(sup +, sup -) pairs to its inner magnetosphere.

Halpern, Jules P.↗
NASA NTRSDate not supplied

Benefits of using Electronic Data Sheets (EDS) with coreFlight Systems (cFS) - A Project Example

Recently there has been interest in the incorporation of core Flight Systems (cFS) with Spacecraft Onboard Interface Services (SOIS) Electronic Data Sheets (EDS) in the spaceflight software community. The Regenerative Fuel Cell project at the Glenn Research Center is using cFS architecture with EDS support for its monitoring and control software. The presentation will outline the benefits to using cFS with EDS support: First, EDS establishes a single source of truth for the definitions of data structures used throughout an entire mission that may otherwise be programmed in different languages and designed with different processor architectures. Not only does this help with inter-application communication via the software bus, but it also greatly simplifies communication between systems. An EDS Application Programming Interface (API) library allows the conversion of EDS data structures to and from native data structures. Second, bindings for other programming languages (e.g. Lua, Python, JSON) have been written to allow the creation and manipulation of EDS data objects within those languages. The RFC project uses Lua scripts to automatically generate binary configuration files at build time to be loaded into our cFS programs. We also use Python bindings in a graphical user interface (GUI) to allow an operator to send commands and view telemetry messages sent from cFS instances. Finally, using Lua scripts we can set up specific simulation scenarios to perform automatic functional testing. During the development of the RFC software, the software team put together a generic python GUI called “cFS-EDS-GroundStation” that provides a basic interface to an instance of cFS with EDS support. The GUI includes a basic telecommand and telemetry system that reads directly from the generated EDS databases. In the telecommand system, dropdown menus are populated with all user commands that are defined in EDS. In the telemetry system, telemetry messages are automatically decoded, written to the screen, and saved to a binary file. Additional Python scripts have been written to convert the binary data files into a comma separated value (CSV) format for further processing. We will demonstrate the basic use of the cFS-EDS-GroundStation software including adding additional commands and telemetry payload values in EDS and see them appear automatically in the cFS-EDS-Groundstation software. About the RFC project: The Regenerative Fuel Cell project is tasked with developing and demonstrating a power system consisting of a fuel cell and electrolyzer to provide power during a lunar day/night cycle. During the night, the fuel cell takes Hydrogen and Oxygen gasses and converts them into electricity, water, and heat. During the day, the electrolyzer takes input power (e.g. from a photovoltaic array) and converts water back into Hydrogen and Oxygen gasses.

Mathew Mccaskey↗
NASA NTRSDate not supplied

Extending ISS Life Beyond 2030

The United States On-orbit Segment (USOS) of the International Space Station (ISS) was designed to meet a 15-year on-orbit life. Since the first hardware was launched in late 1998, the ISS would have reached its end of life in 2013. With the realization that the ISS would be needed well into the next decade, and beyond, a multi-disciplinary effort was undertaken to extend the ISS’ life through 2028 and to show that further extension to 2040 and beyond is not only feasible but achievable. Currently, NASA and the ISS international partners have agreed to extend its operations through 2030. This collaborative effort ensures that the ISS will continue to serve as a hub for scientific research, international cooperation, and educational endeavors for the next decade. Maintaining a continuous human presence in Low Earth Orbit (LEO) is desirable for testing new LEO, lunar, and deep-space technologies; conducting scientific research in micro-gravity for the benefit of life on Earth; and enabling a seamless transition of capabilities to one or more commercially owned and operated destinations. This paper provides an overview of the ISS life extension project with a particular focus on the analytical approach used to assess the primary structure. This analytical approach includes future operations planning, critical location screening, on-orbit dynamic load simulation, on-orbit optical property degradation studies, on-orbit thermal analyses, spectra generation, crack model idealization, fracture analyses, and post processing. Structural life results and identification of the most critical on-orbit events are presented. Also addressed are life extension approaches for other affected sub-systems, including: 1. Secondary Structure. 2. Materials. Evaluations consider environmental exposure to atomic oxygen, ionizing and gamma radiation, fluids, etc. Life limited materials, wear, and usage effects are also considered. 3. Environmental Control and Life Support Systems, including oxygen supply and generation, water recovery and management, and regenerative hardware. Evaluations are performed to determine which hardware can be run to failure and which are assessed for life extension. 4. Electrical power system. ISS is powered by eight channels of solar arrays and an electrical energy storage system providing 357 kWh power. Power generation and balance analyses are performed considering hardware degradation and increasing power demand. 5. Logistics and maintenance. Analyses are performed to determine critical spares required to maintain functionality. Consideration is given to supply chain health, obsolescence issues, onboard stowage availability, and up-mass capability. The successful life extension results have built confidence to safely operate, maintain and enhance the ISS well beyond the current decade. Extending the operational life of the ISS maintains an international presence in LEO and serves to avoid a gap in capability necessary to fulfill the exploration and research needs of NASA, international partners, and industry without interruption until a commercial space station is operational.

design life↗
DOE OSTI2022

Deep Learning‐Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard‐to‐Transfect Cells

Abstract Genome engineering of cells using CRISPR/Cas systems has opened new avenues for pharmacological screening and investigating the molecular mechanisms of disease. A critical step in many such studies is the intracellular delivery of the gene editing machinery and the subsequent manipulation of cells. However, these workflows often involve processes such as bulk electroporation for intracellular delivery and fluorescence activated cell sorting for cell isolation that can be harsh to sensitive cell types such as human‐induced pluripotent stem cells (hiPSCs). This often leads to poor viability and low overall efficacy, requiring the use of large starting samples. In this work, a fully automated version of the nanofountain probe electroporation (NFP‐E) system, a nanopipette‐based single‐cell electroporation method is presented that provides superior cell viability and efficiency compared to traditional methods. The automated system utilizes a deep convolutional network to identify cell locations and a cell‐nanopipette contact algorithm to position the nanopipette over each cell for the application of electroporation pulses. The automated NFP‐E is combined with microconfinement arrays for cell isolation to demonstrate a workflow that can be used for CRISPR/Cas9 gene editing and cell tracking with potential applications in screening studies and isogenic cell line generation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Cost Efficient and Highly Weather-Resistant Solar Panel Backsheet Produced through Continuous Co-Extrusion Processing

The efficient generation and delivery of ‘clean energy” through photovoltaic (PV) technology relies heavily on the cost, performance and reliability of solar panels, along with the individual components used in assembly of those panels. In order to continue towards the Department of Energy’s (DOE) goals of driving costs down, the lifetime of high-output power generation equipment (i.e. solar modules) must be extended to reduce costs associated with power output degradation and replacement of failed or degraded panels. One of the major sources of module failures in the past 30 years has been the failure of the solar panel backsheet. Failure of this component causes severe output losses due to oxidation and yellowing of the module while creating safety concerns as the major electrical insulation of the module is compromised. Tomark-Worthen LLC was formed in 2012 with the goal of designing and manufacturing novel PV backsheets and encapsulants that would be produced in the United States of America and exceed expectations of domestic and international module producers. These backsheets would improve upon known backsheet design failures (i.e. Isovoltaic’s AAA backsheet) and create domestic manufacturing jobs in the PV sector. This goal became a reality with a first-generation product in 2018 and now, with the support of the DOE, Tomark-Worthen has launched a second-generation product known as PhotoMark® Reflections™ 205-3 and 360-3. This 2+ year effort began in late 2017 with a screening of potential polymeric materials and backsheet structural designs. Performance evaluations of each material provided the necessary data needed to select the proper material combinations and move forward into the design of the manufacturing process. These decisions were made by teaming with a local university, well-respected government laboratories, and private research organizations who provided the tools necessary to predict long-term performance of these materials in much shorter periods of time. Once the go/no-go criteria were met at the mid-point of the project, the effort transitioned to predicting 30+ year performance, achieving certifications, and improving manufacturing efficiencies designed to lower costs in order to be competitive in a cost-driven market. Major project accomplishments include recognition of the new backsheet by Underwriters Laboratories (UL) and international certification for 1000V and 1500V modules by TUV Sud. Both 1000V and 1500V products have been proven on several module manufacturing lines and initial customer orders have been received by both domestic and international module producers. Accelerated exposure testing has shown that the performance of this backsheet exceeds the prior polyamide-based backsheet known to fail in the field. Cost-models and initial customer orders have shown the ability to produce this backsheet at a cost acceptable to many domestic manufacturers, while International costs for standard backsheet remains extremely low. Tomark-Worthen remains one of the only U.S. companies manufacturing backsheet domestically and this has drawn interest from many domestic module manufacturers looking to improve their material supply logistics by avoiding long lead times and custom’s headaches while avoiding current tariffs on Chinese-made components. Along with the launch of a new PV backsheet, this project has provided a critical deliverable to the PV R&D community. This deliverable is the knowledge that not all polyamide-containing backsheets are doomed to failure as the earlier versions did. There has been a fear amongst the PV community to consider these materials due to the failure of one design. This effort has brought a new level of interest to the scientific community as it has been shown that polyamides can be used successfully in a well-designed backsheet. As we move forward from this project, we are excited to see how backsheet technology and manufacturing in the U.S. will continue to progress and grow.

14 SOLAR ENERGY↗
DOE OSTI2022

A parametric approach to identify synergistic domains of process intensification for reactive separation

Process intensification aims to combine multiple tasks within multi-functional units to drastically improve economic, energy or sustainability metrics of a chemical process. Limited work exists to systematically identify the synergistic domains where intensification outperforms its nonintensified counterpart. In this work, we computationally derive the synergistic domains of a reactive separation system. Specifically, we first postulate general models for both intensified and nonintensified systems. We use these models to generate data to train a ReLU-type artifical neural network (ANN). Further, the trained ReLU-NN model is formulated as a multi-parametric mixed-integer linear program (mp-MILP), and the critical regions of this mp-MILP define the synergistic feasible domains of intensification. We have derived these synergistic domains of vapor–liquid equilibrium (VLE)-based reactive separation for several industrial applications. These synergistic domains enable quick screening of properties that favor intensification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The effects of process parameters on the properties of manganese-rich carbonate precursors: A study of co-precipitation synthesis using semi-batch reactors

The synthesis of precursors for lithium- and manganese-rich xLi 2 MnO 3 ∙ (1-x)LiMO 2 (M = Ni, Mn, Co) (LMR-NMC) materials is generally carried out via co-precipitation using a continuously stirred tank reactor (CSTR) under steady-state conditions. But during the early stages of research (e.g., when screening compositional spaces of interest), using a CSTR in steady-state mode can be time consuming and wasteful. An alternative is to operate the reactor in semi-batch mode, which shortens reaction times and reduces the amount of waste. However, the effect of this mode on the product is not well documented. The present work investigates how several process variables affect the physical properties of Mn-rich NMC carbonate precursors prepared under semi-batch operation of a CSTR. The process variables examined are pH, ammonia concentration of feed solution, process temperature, stirring speed, and reaction time. Overall, the results can help guide research-scale production to support efficient development of Mn-rich carbonates for cathode oxides.

25 ENERGY STORAGE↗
DOE OSTI2021

Tissue scale agent-based simulation of premalignant progressions in Barrett’s esophagus

Barrett’s esophagus (BE) is a benign condition of the distal esophagus that initiates a multistage pathway to esophageal adenocarcinoma (EAC). Short of frequent intrusive (and costly) surveillance, effective screening for neoplasia in BE populations is yet to be established since progressors are rare and virtually undetectable without routine biopsies, which often sample only a small portion of the BE tissue. As a result, reliable estimation of the true prevalence of dysplasia in a BE population and evidence-based optimization of screening for at-risk individuals is challenging. Data-driven microsimulations, i.e., model-generated instances of disease history in a predefined virtual population, have found utility in the EAC screening literature as low-overhead alternatives to real-world hypothesis testing of optimal interventions for dysplasia. Despite the successes, computational limitations, paucity of knowledge and data on Barrett’s dysplasia, and the complexities of disease progression as a multiscale multiphysics process have hindered the treatment of disease progression in BE as a spatial process. Agent-based modeling of nucleation and proliferation processes in dysplasia warrants exploration in this context as an approximation that operates at a trade-off between computational tractability and precise representation of the composition and physics of the substrate (tissue). In this study, we describe spatially resolved simulations of premalignant progression toward EAC in a coarse-grained model of Barrett’s tissue that resolves the metaplastic tissue at a length scale of 0.42 mm (~3300 crypts/mm 2 ). Finally, the model is calibrated to reproduce historical high-grade dysplasia prevalence when model-generated patients are screened using the Seattle protocol.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Optimized Incorporation of Furan into Diketopyrrolopyrrole-Based Conjugated Polymers for Organic Field-Effect Transistors

Flexible electronics have received considerable attention in the past decades due to their promising application in rollable display screens, wearable devices, implantable devices, and other electronic applications. Here, in particular, conjugated polymers are favored for flexible electronics due to their mechanical flexibility and potential for solution-processed fabrication techniques, such as blade-coating, roll-to-roll printing, and high-throughput printing allowing for high-performance transistor devices. Thiophene is the prevailing conjugated unit to construct these conjugated polymers due to its favorable electronic properties. On the other hand, furans are among the few conjugated moieties that are easily derived from bio renewable resources. To promote sustainability, we selectively introduced furan into the conjugated backbone of a high-mobility polymer scaffold and systematically studied the effect on the microstructure and charge transport. We show that partially and selectively replacing thiophene units with furan can yield nearly comparable performance compared to the all-thiophene polymer. This strategy offers an improvement in the sustainability of the polymer by incorporating bio-sourced furan without sacrificing the high-performance characteristics. Meanwhile, polymers with incorrect or complete furan incorporation show reduced mobilities. This work serves to develop coherent structure–morphology–performance relationships; such knowledge will establish guidelines for the future development of sustainable, furan-based conjugated materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Accurate Prediction of pKb in Amines: Validation of the CAM-B3LYP/6-311+G(d,p)/SMD Model

Amines play several key roles in chemistry and biology and are involved in numerous industrial processes, often with significant economic impacts. Recently, amines are also garnering interest as catalysts for polymer synthesis and for CO 2 fixation, incentivizing the need to rapidly design and screen new amino compounds. Hence, developing reliable methods to predict their physicochemical properties, e.g., the base dissociation constant (pKb), is pivotal. Here, a density functional theory (DFT)-based approach was employed to compute the pKb of substituted amines, exploring the impact of several key parameters, including (i) the number of explicit water molecules at the reaction center, (ii) the van der Waals (vdW) surface, and (iii) solvent polarizability. In previous work, it was determined that including two explicit water molecules at the reaction center resulted in highly accurate pKb estimates for primary amines. Here, we find that including a third water molecule at the reaction center is essential for accurate pKb for secondary and tertiary amines. The revised methodology was then applied to a wider selection of amines, obtaining a minimum average error (MAE) < 0.4. In conclusion, this result represents an extension of our “easy-to-use method,” a simple and direct DFT approach exploiting CAM-B3LYP/SMD/6-311G+(d,p) to compute pKb without post facto modifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Relationship between the Nature of Monovalent Cations and Charge Recombination in Metal Halide Perovskites

We report charge recombination rates in lead iodide perovskites with alloys of formamidinium (FA) and methylammonium (MA) cations, FA x MA 1–x PbI 3 , 0 ≤ x ≤ 0.9, prepared through ion exchange with minimal differences in morphology and charge trap density. Our results show that the trap-mediated recombination rate increases over 2 orders of magnitude with decreasing MA content. These results are consistent with a proposed mechanism that MA molecules are more effective in screening charged defects. In contrast, band-to-band charge recombination rates are minimized at an intermediate alloy composition. Here, these findings reveal that the monovalent cations impact recombination processes through different mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

CRAGE-mediated insertion of fluorescent chromosomal markers for accurate and scalable measurement of co-culture dynamics in Escherichia coli

Monitoring population dynamics in co-culture is necessary in engineering microbial consortia involved in distributed metabolic processes or biosensing applications. However, it remains difficult to measure strain-specific growth dynamics in high-throughput formats. This is especially vexing in plate-based functional screens leveraging whole-cell biosensors to detect specific metabolic signals. Here, we develop an experimental high-throughput co-culture system to measure and model the relationship between fluorescence and cell abundance, combining chassis-independent recombinase-assisted genome engineering (CRAGE) and whole-cell biosensing with a PemrR-green fluorescent protein (GFP) monoaromatic reporter used in plate-based functional screening. CRAGE was used to construct Escherichia coli EPI300 strains constitutively expressing red fluorescent protein (RFP) and the relationship between RFP expression and optical density (OD600) was determined throughout the EPI300 growth cycle. A linear equation describing the increase of normalized RFP fluorescence during deceleration phase was derived and used to predict biosensor strain dynamics in co-culture. Measured and predicted values were compared using flow cytometric detection methods. Induction of the biosensor lead to increased GFP fluorescence normalized to biosensor cell abundance, as expected, but a significant decrease in relative abundance of the biosensor strain in co-culture and a decrease in bulk GFP fluorescence. Taken together, these results highlight sensitivity of population dynamics to variations in metabolic activity in co-culture and the potential effect of these dynamics on the performance of functional screens in plate-based formats. The engineered strains and model used to evaluate these dynamics provide a framework for optimizing growth of synthetic co-cultures used in screening, testing and pathway engineering applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Construction and characterization of a genome-scale ordered mutant collection of Bacteroides thetaiotaomicron

Ordered transposon-insertion collections, in which specific transposon-insertion mutants are stored as monocultures in a genome-scale collection, represent a promising tool for genetic dissection of human gut microbiota members. However, publicly available collections are scarce and the construction methodology remains in early stages of development. Here, we describe the assembly of a genome-scale ordered collection of transposon-insertion mutants in the model gut anaerobe Bacteroides thetaiotaomicron VPI-5482 that we created as a resource for the research community. We used flow cytometry to sort single cells from a pooled library, located mutants within this initial progenitor collection by applying a pooling strategy with barcode sequencing, and re-arrayed specific mutants to create a condensed collection with single-insertion strains covering >2500 genes. To demonstrate the potential of the condensed collection for phenotypic screening, we analyzed growth dynamics and cell morphology. We identified both growth defects and altered cell shape in mutants disrupting sphingolipid synthesis and thiamine scavenging. Finally, we analyzed the process of assembling the B. theta condensed collection to identify inefficiencies that limited coverage. We demonstrate as part of this analysis that the process of assembling an ordered collection can be accurately modeled using barcode sequencing data. We expect that utilization of this ordered collection will accelerate research into B. theta physiology and that lessons learned while assembling the collection will inform future efforts to assemble ordered mutant collections for an increasing number of gut microbiota members.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Strategies for using membrane-based separations to extract critical metals from waste streams

Critical metals are currently extracted by mining followed by their purification. These processes are costly and not environmentally very desirable. In this perspective paper we discuss the potential of extracting these critical metals from a range waste-streams available in abundance globally. These waste streams include brine from desalination plants, effluents from oil drilling and hydraulic fracturing, as well as discharges from various industrial processes such as metal finishing, electroplating, mining, and chemical manufacturing. We show that with a range of new separation processes being developed their separation is showing potential of being both technologically and economically feasible. We also show how high performance computing can be combined with computational models to screen and accelerate the development of new technologies for extracting critical metals from waste streams.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis

Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Status on Genetic Resistance to Rice Blast Disease in the Post-Genomic Era

Rice blast, caused by Magnaporthe oryzae, is a major threat to global rice production, necessitating the development of resistant cultivars through genetic improvement. Breakthroughs in rice genomics, including the complete genome sequencing of japonica and indica subspecies and the availability of various sequence-based molecular markers, have greatly advanced the genetic analysis of blast resistance. To date, approximately 122 blast-resistance genes have been identified, with 39 of these genes cloned and molecularly characterized. The application of these findings in marker-assisted selection (MAS) has significantly improved rice breeding, allowing for the efficient integration of multiple resistance genes into elite cultivars, enhancing both the durability and spectrum of resistance. Pangenomic studies, along with AI-driven tools like AlphaFold2, RoseTTAFold, and AlphaFold3, have further accelerated the identification and functional characterization of resistance genes, expediting the breeding process. Future rice blast disease management will depend on leveraging these advanced genomic and computational technologies. Emphasis should be placed on enhancing computational tools for the large-scale screening of resistance genes and utilizing gene editing technologies such as CRISPR-Cas9 for functional validation and targeted resistance enhancement and deployment. These approaches will be crucial for advancing rice blast resistance, ensuring food security, and promoting agricultural sustainability.

Pedrozo, Rodrigo↗