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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high throughputs. We demonstrate the performance of this approach by autonomously driving a 4-DOF robotic probe for 24 hours to characterize semiconductor photoconductivity at 3025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs of more than 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing defects. With this self-supervised neural network–driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

Science & Technology - Other Topics↗
DOE OSTI2024

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

36 MATERIALS SCIENCE↗
NASA NTRS2015

High-Reflectivity Multi-Layer Coatings for the CLASP Sounding Rocket Project

We are planning an international rocket experiment Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) is (2015 planned) that Lyman alpha line (Ly alpha line) polarization spectroscopic observations from the sun. The purpose of this experiment, detected with high accuracy of the linear polarization of the Ly alpha lines to 0.1% by using a Hanle effect is to measure the magnetic field of the chromosphere-transition layer directly. For polarization photometric accuracy achieved that approximately 0.1% required for CLASP, it is necessary to realize the monitoring device with a high throughput. On the other hand, Ly alpha line (vacuum ultraviolet rays) have a sensitive characteristics that is absorbed by the material. We therefore set the optical system of the reflection system (transmission only the wavelength plate), each of the mirrors, subjected to high efficiency of the multilayer coating in accordance with the role. Primary mirror diameter of CLASP is about 30 cm, the amount of heat about 30,000 J is about 5 minutes of observation time is coming mainly in the visible light to the telescope. In addition, total flux of the sun visible light overwhelmingly large and about 200 000 times the Ly alpha line wavelength region. Therefore, in terms of thermal management and 0.1% of the photometric measurement accuracy achieved telescope, elimination of the visible light is essential. We therefore, has a high reflectivity (greater than 50%) in Ly alpha line, visible light is a multilayer coating be kept to a low reflectance (less than 5%) (cold mirror coating) was applied to the primary mirror. On the other hand, the efficiency of the polarization analyzer required chromospheric magnetic field measurement (the amount of light) Conventional (magnesium fluoride has long been known as a material for vacuum ultraviolet (MgF2) manufactured ellipsometer; Rs = 22%) about increased to 2.5 times were high efficiency reflective polarizing element analysis. This device, Bridou et al. (2011) is proposed "that is coated with a thin film of the substrate MgF2 and SiO2 fused silica." As a result of the measurement, Rs = 54.5%, to achieve a Rp = 0.3%, high efficiency, of course, capable of taking out only about spolarized light. Other reflective optical elements (the secondary mirror, the diffraction grating-collector mirror), subjected to high-reflection coating of Al + MgF2 (reflectance of about 80%), less than 5% in the entire optical system by these (CCD Science was achieved a high throughput as a device for a vacuum ultraviolet ray of the entire system less than 5% (CCD of QE is not included).

sounding rocket↗
NASA NTRS2015

Solar Lyman-Alpha Polarization Observation of the Chromosphere and Transition Region by the Sounding Rocket Experiment CLASP

We are planning an international rocket experiment Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) is (2015 planned) that Lyman α line (Ly(alpha) line) polarization spectroscopic observations from the sun. The purpose of this experiment, detected with high accuracy of the linear polarization of the Ly(alpha) lines to 0.1% by using a Hanle effect is to measure the magnetic field of the chromosphere-transition layer directly. For polarization photometric accuracy achieved that approx. 0.1% required for CLASP, it is necessary to realize the monitoring device with a high throughput. On the other hand, Ly(alpha) line (vacuum ultraviolet rays) have a sensitive characteristics that is absorbed by the material. We therefore set the optical system of the reflection system (transmission only the wavelength plate), each of the mirrors, subjected to high efficiency of the multilayer coating in accordance with the role. Primary mirror diameter of CLASP is about 30 cm, the amount of heat about 30,000 J is about 5 minutes of observation time is coming mainly in the visible light to the telescope. In addition, total flux of the sun visible light overwhelmingly large and about 200 000 times the Ly(alpha) line wavelength region. Therefore, in terms of thermal management and 0.1% of the photometric measurement accuracy achieved telescope, elimination of the visible light is essential. We therefore, has a high reflectivity (> 50%) in Lyα line, visible light is a multilayer coating be kept to a low reflectance (<5%) (cold mirror coating) was applied to the primary mirror. On the other hand, the efficiency of the polarization analyzer required chromospheric magnetic field measurement (the amount of light) Conventional (magnesium fluoride has long been known as a material for vacuum ultraviolet (MgF2) manufactured ellipsometer; Rs = 22%) about increased to 2.5 times were high efficiency reflective polarizing element analysis. This device, Bridou et al. (2011) is proposed "that is coated with a thin film of the substrate MgF2 and SiO2 fused silica." As a result of the measurement, Rs = 54.5%, to achieve a Rp = 0.3%, high efficiency, of course, capable of taking out only about s-polarized light. Other reflective optical elements (the secondary mirror, the diffraction gratingcollector mirror), subjected to high-reflection coating of Al + MgF2 (reflectance of about 80%), less than 5% in the entire optical system by these (CCD Science was achieved a high throughput as a device for a vacuum ultraviolet ray of the entire system less than 5% (CCD of QE is not included).

Narukage, Noriyuki↗
DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗
NASA NTRSDate not supplied

Overview and Performance Characterization of Northrop Grumman’s 1 kW Hall Thruster String

Northrop Grumman (NG) Tactical Space SystemsDivisionhas embarked on the development and qualification of a high throughput, low power Hall Thruster String (HTS) using hardware designed and built in-house.Following the success of Mission Extension Vehicles 1 and 2, NG is currently developing the next generation in its lineupof satellite servicing capabilities, the Mission Robotics Vehicle(MRV)and Mission Extension Pod (MEP).MEP’s mission profile imposes highly demanding requirements upon the electric propulsion system. When surveying the industry for available systems, NGwas unable to identify any mature Hall thrustersystems that could satisfy the performance and lifetime requirements for MEP. Eventually, it was decided to vertically integrate the EP development process, partneringwith NASA Glenn Research Center to leverage ongoing development at GRC of a high throughput, low power Hall thruster. The components of the HTS have successfully passed PDR and are currently in the engineering development and test phase. Based on performancecharacterization testingof the development hardware,NG’s low power Hall thruster, dubbed the NGHT-1X, promises to deliver state-of-the-art performanceand lifetime for a sub-kW Hall thruster, achieving total efficiencies of 50-55% over a wide range of throttle conditions. Results of a seriesof characterization tests including integrated systems testingwith the PPUare presented.Qualification methodology andthruster lifetime verification is also discussed. Environmental qualification of the HTS components is expected to completein mid-2023 with a first flight in mid-2024.

Propulsion↗
DOE OSTI2025

Synthetic Accessibility and Sodium Ion Conductivity of the Na8–x A x P2O9 (NAP) High-Temperature Sodium Superionic Conductor Framework

Advancement of solid-state electrolytes (SSEs) for all solid-state batteries typically focuses on modification of a known structural framework to improve conductivity, e.g., cation substitution for an immobile ion or varying the concentration of the mobile ions. Novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, thereby unlocking optimal conductors with different properties. Herein, we perform a high-throughput survey of a structural framework for sodium ion conduction, Na8–x A x P2O9 (NAP), to understand the family’s thermodynamic stability, synthesizability, and ionic conduction. We show that the parent phase Na4TiP2O9 (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons arising from pseudo-Jahn–Teller mode in the 1D titanium chains. Screening compounds in which Ti is substituted by other metals computationally reveal a number of candidates that are predicted to be low in formation energy and have high predicted ionic conductivities. High-throughput experimental and subsequent methodology optimization trials deliver one new compound, Na4SnP2O9 (NSP). X-ray diffraction (XRD), microscopy, and spectroscopy characterization indicate that the room-temperature structure of NSP is similar to the high-temperature, orthorhombic NTP phase but with some small unresolved structural differences. These uncharacterized structural details are speculated to limit the ion conductivity. Temperature-dependent XRD and electrochemical impedance spectroscopy indicate multiple coupled conductivity–structure transitions at a high temperature. We demonstrate the challenges with synthesis development and a priori identification of promising SSE phases as a major bottleneck in new (energy) materials development.

Chemical reactions↗
DOE OSTI2025

Strategies to search for two-dimensional materials with long spin qubit coherence time

Two-dimensional (2D) materials that can host qubits with long spin coherence time (T 2 ) have the distinct advantage of integrating easily with existing microelectronic and photonic platforms, making them attractive for designing novel quantum devices with enhanced performance. However, the relative lack of 2D materials as spin qubit hosts, as well as appropriate substrates that can help maintain long T 2 , necessitates a strategy to search for candidates with robust spin coherence. Here, we develop a high-throughput computational workflow to predict the nuclear spin bath-driven qubit decoherence and T 2 in 2D materials and heterostructures. We initially screen 1172 2D materials and find 189 monolayers with T 2 > 1 ms, higher than that of naturally-abundant diamond. We then construct 1554 lattice-commensurate heterostructures between high-T 2 2D materials and select 3D substrates, and we find that T 2 is generally lower in a heterostructure than in the bare 2D host material; however, low-noise substrates (such as CeO 2 and CaO) can help maintain high T 2 . To further accelerate the material screening effort, we derive analytical models that enable rapid predictions of T 2 for 2D materials and heterostructures. The models offer a simple, yet quantitative, way to determine the relative contributions to decoherence from the nuclear spin baths of the 2D host and substrate in a heterostructural system. By developing a high-throughput workflow and analytical models, we expand the genome of 2D materials and their spin coherence times for the development of spin qubit platforms.

Toriyama, Michael Y. [Argonne National Laboratory ↗
NASA NTRS1988

Design of the Extreme Ultraviolet Explorer long-wavelength grazing incidence telescope optics

Designing optics for photometry in the long-wavelength portion of the EUV spectrum (400-900) A) poses different problems from those arising for optics, operating shortward of 400 A. The available filter materials which transmit radiation longward of 400 A are also highly transparent at wavelengths shortward of 100 A. Conventional EUV optics, with grazing engles of less than about 10 deg, have very high throughput in the EUV, which persists to wavelengths shortward of 100 A. Use of such optics with the longer-wavelength EUV filters thus results in an unacceptably large soft X-ray leak. This problem is overcome by developing a mirror design with larger graze angles of not less than 20 deg, which has high throughput at wavelengths longer than 400 A but at the same time very little throughput shortward of 100 A.

Finley, David S.↗
DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Self-Driving Microscopy for AI/ML-Enabled Physics Discovery and Materials Optimization

Materials are the bedrock of economy and foundation for all real-world technologies. The viability of space travel, grid energy storage, solar to fuels conversion, methane removal, and photovoltaic energy solutions hinge on the discovery and optimization of novel materials and rapid scaling toward manufacturing. The last 20 years have seen an exponential growth in the theoretical predictive capability for crystalline materials and small molecules. However, it is only in the last five years that we have seen the rapid expansion of high-throughput synthesis enabled by laboratory robotics and microfluidics, as well as a resurgence of combinatorial synthesis (Abolhasani and Kumacheva 2023; Epps and Abolhasani 2021; Jiang et al. 2022; Rajan 2008; Soldatov et al. 2021; Szymanski et al. 2023). Combinatorial synthesis, microfluidics, and ultimately dip-pen megalibraries have demonstrated the ability to “write” multicomponent nanomaterials at high throughput scale, generating millions of material examples in the 3D, 4D, and 5D composition spaces (Chen et al. 2016, 2019; Jibril et al. 2022).

36 MATERIALS SCIENCE↗
NASA NTRS2013

Evaluation of the Controller-Managed Spacing Tools, Flight-Deck Interval Management and Terminal Area Metering Capabilities for the ATM Technology Demonstration #1

NASA has developed a suite of advanced arrival management technologies combining time-based scheduling with controller- and flight deck-based precision spacing capabilities that allow fuel-efficient arrival operations during periods of high throughput. An operational demonstration of these integrated technologies, i.e., the ATM Technology Demonstration #1 (ATD-1), is slated for 2016. Human-in-the-loop simulations were conducted to evaluate the performance of the ATD-1 system and validate operational feasibility. The ATD-1 system was found to be robust to scenarios with saturated demand levels and high levels of system delay. High throughput, 10 above baseline demand levels, and schedule conformance less than 20 seconds at the 75th percentile were achievable. The flight-deck interval management capabilities also improved the median schedule conformance at the final approach fix from 5 to 3 seconds with less variance.

terminal metering↗
DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗
DOE OSTI2025

Spectral interferometry-based microwave-frequency vibrometry for integrated acoustic wave devices

Microwave phononics is a promising platform for sensing, computing, and quantum information science; thus, sensitive and high-throughput characterization tools are needed not only for device verification and optimization but also for revealing transient and nonlinear dynamics. Existing interferometric optical vibrometers for 2D mapping are challenged by operating point stabilization, surface reflectivity contrast, and long acquisition time. Here, we use spectral interferometry, which is insensitive to these factors and utilizes a continuous raster scanning scheme for vibration mapping with high throughput. We intensity-modulate our broadband light source with an electro-optic modulator to resolve vibrations at microwave frequencies. Our system requires no fast photodetector or digitizer operating in the microwave frequency range. We image the 1 GHz vibration field of a 300 × 150 µm 2 area of an entire surface acoustic wave device in 10 min with simultaneous surface profilometry. Our system has a vibration sensitivity of 120 fm/sqrt(Hz) and a linear throughput of 0.77 mm/s on the chip surface. The technique offers capabilities for characterizing a wide range of acoustic wave and micromechanical devices to better understand their behavior and performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Neurovascular Outcomes of Ionizing Radiation in Human Blood-Brain Barrier Models

One of the main health risks in human deep space exploration is central nervous system (CNS) damage by ionizing radiation due to exposure to galactic cosmic rays (GCRs). In animal models, irradiation with simulated GCRs or their components has been shown to cause neurodegeneration and neuroinflammation associated with cognitive and behavioral dysfunction. The extent of CNS damage is partially mediated by the blood-brain barrier (BBB), which regulates the interaction between CNS and systemic responses to stressors in the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal death, neuroinflammation and oxidative stress. However, studies on BBB and astrocyte functions in regulating CNS responses to ionizing radiation have been limited, especially in human tissue/organ analogs. Therefore, we developed a high throughput 3D human neurovascular system model, based on commercially available Mimetas OrganoPlates seeded by primary human cells, to investigate the neurovascular responses to simulated deep space radiation. Using this system, we have demonstrated that 600MeV/n 56Fe irradiation leads to cellular damage and increased blood-brain barrier permeability via dysfunction of brain cells called astrocytes, which appear to be the weakest link and therefore a highly suitable countermeasure target to reduce the impact of space radiation on the blood-brain barrier. We anticipate that our results form merely the first step in ongoing development of organ models, including their adaptation for personalized risk assessment, high throughput approaches to countermeasure screening and validation, and eventual payload adaptation.

organ-on-a-chip↗
NASA NTRS2010

Fabrication of Single, Vertically Aligned Carbon Nanotubes in 3D Nanoscale Architectures

Plasma-enhanced chemical vapor deposition (PECVD) and high-throughput manufacturing techniques for integrating single, aligned carbon nanotubes (CNTs) into novel 3D nanoscale architectures have been developed. First, the PECVD growth technique ensures excellent alignment of the tubes, since the tubes align in the direction of the electric field in the plasma as they are growing. Second, the tubes generated with this technique are all metallic, so their chirality is predetermined, which is important for electronic applications. Third, a wafer-scale manufacturing process was developed that is high-throughput and low-cost, and yet enables the integration of just single, aligned tubes with nanoscale 3D architectures with unprecedented placement accuracy and does not rely on e-beam lithography. Such techniques should lend themselves to the integration of PECVD grown tubes for applications ranging from interconnects, nanoelectromechanical systems (NEMS), sensors, bioprobes, or other 3D electronic devices. Chemically amplified polyhydroxystyrene-resin-based deep UV resists were used in conjunction with excimer laser-based (lambda = 248 nm) step-and-repeat lithography to form Ni catalyst dots = 300 nm in diameter that nucleated single, vertically aligned tubes with high yield using dc PECVD growth. This is the first time such chemically amplified resists have been used, resulting in the nucleation of single, vertically aligned tubes. In addition, novel 3D nanoscale architectures have been created using topdown techniques that integrate single, vertically aligned tubes. These were enabled by implementing techniques that use deep-UV chemically amplified resists for small-feature-size resolution; optical lithography units that allow unprecedented control over layer-to-layer registration; and ICP (inductively coupled plasma) etching techniques that result in near-vertical, high-aspect-ratio, 3D nanoscale architectures, in conjunction with the use of materials that are structurally and chemically compatible with the high-temperature synthesis of the PECVD-grown tubes. The techniques offer a wafer-scale process solution for integrating single PECVD-grown nanotubes into novel architectures that should accelerate their integration in 3D electronics in general. NASA can directly benefit from this technology for its extreme-environment planetary missions. Current Si transistors are inherently more susceptible to high radiation, and do not tolerate extremes in temperature. These novel 3D nanoscale architectures can form the basis for NEMS switches that are inherently less susceptible to radiation or to thermal extremes.

Kaul, Anupama B.↗
DOE OSTI2024

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Plant Sciences↗