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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 1,063 records · Page 59

An Electrochemical Strategy to Synthesize Disilanes and Oligosilanes from Chlorosilanes**

Silanes are important compounds in industrial and synthetic chemistry. Here, we develop a general approach for the synthesis of disilanes as well as linear and cyclic oligosilanes via the reductive activation of readily available chlorosilanes. The efficient and selective generation of silyl anion intermediates, which are arduous to achieve by other means, allows for the synthesis of various novel oligosilanes by heterocoupling. In particular, this work presents a modular synthesis for a variety of functionalized cyclosilanes, which may give rise to materials with distinct properties from linear silanes but remain challenging synthetic targets. In comparison to the traditional Wurtz coupling, our method features milder conditions and improved chemoselectivity, broadening the functional groups that are compatible in oligosilane preparation. Furthermore, computational studies support a mechanism whereby differential activation of sterically and electronically distinct chlorosilanes are achieved in an electrochemically driven radical-polar crossover mechanism.

36 MATERIALS SCIENCE↗

“Off‐Label Use” of the Siderophore Enterobactin Enables Targeted Imaging of Cancer with Radioactive Ti (IV)

The development of inert, biocompatible chelation methods is required to harness the emerging positron emitting radionuclide 45 Ti for radiopharmaceutical applications. Herein, we evaluate the Ti (IV) -coordination chemistry of four catechol-based, hexacoordinate chelators using synthetic, structural, computational, and radiochemical approaches. The siderophore enterobactin (Ent) and its synthetic mimic TREN-CAM readily form mononuclear Ti (IV) species in aqueous solution at neutral pH. Radiolabeling studies reveal that Ent and TREN-CAM form mononuclear complexes with the short-lived, positron-emitting radionuclide 45 Ti (IV) , and do not transchelate to plasma proteins in vitro and exhibit rapid renal clearance in naïve mice. These features guide efforts to target the 45 Ti isotope to prostate cancer tissue through the design, synthesis, and evaluation of Ent-DUPA, a small molecule conjugate composed of a prostate specific membrane antigen (PSMA) targeting peptide and a monofunctionalized Ent scaffold. Additionally, the [ 45 Ti][Ti(Ent-DUPA)] 2− complex forms readily at room temperature. In a tumor xenograft model in mice, selective tumor tissue accumulation (8±5 %, n=5), and low off-target uptake in other organs is observed. Overall, this work demonstrates targeted imaging with 45 Ti (IV) , provides a foundation for advancing the application of 45 Ti in nuclear medicine, and reveals that Ent can be repurposed as a 45 Ti-complexing cargo for targeted nuclear imaging applications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Accurate prediction of terahertz spectra of molecular crystals of fentanyl and its analogs

Fentanyl is a potent synthetic opioid pain reliever with a high bioavailability that can be used as prescription anesthetic. Rapid identification via non-contact methods of both known and emerging opioid substances in the fentanyl family help identify the substances and enable rapid medical attention. We apply PBEh-3c method to identify vibrational normal modes from 0.01 to 3 THz in solid fentanyl and its selected analogs. The molecular structure of each fentanyl analog and unique arrangement of H-bonds and dispersion interactions significantly change crystal packing and is subsequently reflected in the THz spectrum. Further, the study of THz spectra of a series of stereoisomers shows that small changes in molecular structure results in distinct crystal packing and significantly alters THz spectra as well. We discuss spectral features of synthetic opioids with higher potency than conventional fentanyl such as ohmefentanyl and sufentanil and discover the pattern of THz spectra of fentanyl analogs.

42 ENGINEERING↗

Characterization of Mk-IV and EBR-II X441A Metallic Fuel Pins for the THOR-C-2 and THOR-M-TOP-1 Experiments: Results from Post-Transient Neutron Tomography of THOR-C-2 Capsule and Pre-transient Non-Destructive and Destructive Examination of DP 36 and DP 40 (Rev.1)

Current interest in sodium-cooled fast reactor designs, such as TerraPower’s Natrium Reactor, has highlighted the need for advanced-reactor fuel technology development. A Fuel Safety Research and Development (FSRD) program for metallic fast reactor fuels has been created to achieve comprehensive safety testing within the re-commissioned Transient Reactor Test (TREAT) facility at the Idaho National Laboratory. Despite over 60 years of metallic fuel irradiation, uncertainties exist in the performance of the fuel system, particularly under anticipated operational occurrences and severe accident scenarios. Throughout historical testing within the Experimental Breeder Reactor (EBR)-II and the Fast Flux Test Facility, fuel behavior has demonstrated benign response to transient reactor conditions; however, accurate predictions of failure thresholds rely heavily on fuel composition and irradiation history. In advancing the FSRD program, two planned transient heating experiments are at various stages of completion. The Temperature Heat sink Overpower Response (THOR)-C-2, fueled with an unirradiated Mk IV U-10Zr pin, has undergone transient irradiation in TREAT and post-transient three-dimensional neutron tomography. Additionally, pre-transient characterization of test and sibling U-19Pu-10Zr pins for THOR-M-TOP-1 was evaluated by both non-destructive and destructive methods. The test and sibling pins were selected from previously irradiated EBR-II experiment X441A. Both pins underwent visual examination, precise gamma spectrometry, two-dimensional neutron radiography, and element contact profilometry while the sibling pin was additionally subjected to sectioning and optical microscopy. THOR-C-2 radiography captured the fuel and cladding relocation during the intermediate transient at the top and bottom of the THOR capsule, allowing key features to be linked to the pin’s measured thermal response. For THOR M TOP 1, a solid baseline for steady-state behavior has been established. No anomalous features were identified in either the test or sibling pin. Defining characteristics and features were recorded for further comparisons.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Study of $\langle {p}_{\text{T}}\rangle$ and its higher moments, and extraction of the speed of sound in Pb-Pb collisions with ALICE

Ultrarelativistic heavy-ion collisions produce a state of hot and dense strongly interacting QCD matter called quark-gluon plasma (QGP). On an event-by-event basis, the volume of the QGP in ultracentral collisions is mostly constant, while its total entropy can vary significantly due to quantum fluctuations, leading to variations in the temperature of the system. Exploiting this unique feature of ultracentral collisions allows for the interpretation of the correlation of the mean transverse momentum ($\langle$p T $\rangle$) of produced charged hadrons and the number of charged hadrons as a measure for the speed of sound, c s . This speed is related to the rate at which compression waves travel in the QGP and is determined by fitting the relative increase in $\langle$p T $\rangle$ with respect to the relative change in the average charged-particle density ($\langle$dN ch /dη$\rangle$) measured at mid-rapidity. This study reports the event-average $\langle$p T $\rangle$ of charged particles as well as the variance, skewness, and kurtosis of the event-by-event transverse momentum per charged particle ([p T ]) distribution in ultracentral Pb-Pb collisions at a center-of-mass energy of 5.02 TeV per nucleon pair using the ALICE detector. Different centrality estimators based on charged-particle multiplicity or the transverse energy of the event are used to select ultracentral collisions. By ensuring a pseudorapidity gap between the region used to define the centrality and the region used to perform the measurement, the influence of biases and their potential effects on the rise of the mean transverse momentum is tested. The measured c$^{2}_{s}$ is found to strongly depend on the exploited centrality estimator and ranges between 0.1146±0.0028 (stat.)±0.0065 (syst.) and 0.4374±0.0006 (stat.)±0.0184 (syst.) in natural units. The self-normalized variance shows a steep decrease towards ultracentral collisions, while the self-normalized skewness variables show a maximum, followed by a fast decrease. These non-Gaussian features are understood in terms of the vanishing of the impact-parameter fluctuations contributing to the event-to-event [p T ] distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improving deep learning-based protein distance prediction in CASP14

Abstract Motivation Accurate prediction of residue–residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions [i.e. classifying distances between two residues into two categories: in contact (<8 Angstrom) and not in contact otherwise] and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. Availability and implementation The software package, source code and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

Vibrational spectroscopy analysis of silica and silicate glass networks

Vibrational spectroscopy has been widely used to investigate various structural aspects of the glass network, and there are a plethora of papers reporting subtle but consistent changes in infrared and Raman spectral features of glass upon alterations of glass compositions, thermal histories, mechanical stresses, or surface treatments. However, interpretations of such spectral features are still obscured due to the lack of well-established physical principles accurately describing vibrational modes of the non-crystalline glass network. Due to the non-equilibrium nature of the glass network, three-dimensionally connected without any long-range orders, vibrational spectral features of glass cannot be interpreted using the analogy with those of isolated molecular moieties or crystalline counterparts. Here, this feature article explains why such comparisons are outdated and describes the recent advances made from theoretical calculations of vibrational spectral features of amorphous networks or comparisons of computational results with experimental data. For the interpretation of vibrational spectral features of silica and silicate glasses, the following empirical relationships are suggested: (i) the intensity-weighted peak position of the Si-O-Si stretch mode negatively correlates with the weighted average of the Si-O bond length distribution, and (ii) the broad band of the Si-O-Si bending mode negatively correlates with the Si-O-Si bond angle distribution. Selected examples of vibrational spectroscopic imaging of surface defects are discussed to deliberate the implication of these findings in the structure-property relationship of silica and silicate glass materials. Unanswered questions and continuing research challenges are identified.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Effect of pattern transfer process on roughness of block copolymer patterns from directed self-assembly

Block copolymer-directed self-assembly (DSA) remains promising for improving pattern quality and reducing the stochastic variations that challenge high numerical aperture extreme ultraviolet lithography. Equally critical is refining pattern transfer methods for accurately transferring the rectified DSA features to the underlying substrate. We compare two atomic layer deposition (ALD)-based techniques: sequential infiltration synthesis (SIS) and dry liftoff, applied to polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) DSA patterns. Both methods utilize aluminum oxide hard masks, with one synthesized through infiltration into the PMMA domains and the other through conformal ALD coating. High-resolution scanning electron micrographs were analyzed to measure line edge, width, and placement roughness for both the line (PMMA) and space (PS) features. Although both methods yielded similar overall 3σ rms roughness, they differed significantly in the frequency-dependent power spectral density (PSD) profiles. SIS reduced line placement roughness at length scales associated with the polymer pitch, but increased space width roughness at low frequencies, whereas dry liftoff mimicked the frequency content of the original guiding pattern. This study underscores the importance of PSD evaluation in selecting optimal pattern transfer strategies for specific applications.

Block copolymers↗

Development of Multimodal Few-Shot Analytics for Electron Micrographs

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Attempts at creating a multimodal approach can provide further improvements to current models and help extract more salient features from data. In this vein, raw spectrum data was taken to provide an additional modality to our current pyCHIP classifier. Modifications in segmentation also show potential in improving the accuracy of the pyCHIP classifier. Classifier output was analyzed using network graphs and unsupervised clustering algorithms such as spectral clustering to detect better segmentation methods than the current “chipping” approach. We suggest that the chip selection process can be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗

Photocatalytic Hydrogen Evolution by a De Novo Designed Metalloprotein that Undergoes Ni–Mediated Oligomerization Shift

De novo metalloprotein design involves the construction of proteins guided by specific repeat patterns of polar and apolar residues, which, upon self-assembly, provide a suitable environment to bind metals and produce artificial metalloenzymes. While a wide range of functionalities have been realized in de novo designed metalloproteins, the functional repertoire of such constructs towards alternative energy-relevant catalysis is currently limited. Here we show the application of de novo approach to design a functional H 2 evolving protein. The design involved the assembly of an amphiphilic peptide featuring cysteines at tandem a/d sites of each helix. Intriguingly, upon Ni II addition, the oligomers shift from a major trimeric assembly to a mix of dimers and trimers. The metalloprotein produced H 2 photocatalytically with a bell-shape pH dependence, having a maximum activity at pH 5.5. Transient absorption spectroscopy is used to determine the timescales of electron transfer as a function of pH. Selective outer sphere mutations are made to probe how the local environment tunes activity. Finally, a preferential enhancement of activity is observed via steric modulation above the Ni II site, towards the N-termini, compared to below the Ni II site towards the C-termini.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Plutonium migration and phase evolution in irradiated U-Pu-Zr metallic fuels: An integrated EPMA-SEM-TEM study

Constituent redistribution is a defining feature of irradiated U-Pu-Zr metallic fuels, yet its mechanisms and effects on fuel performance are not sufficiently resolved to guide model development. Although decades of irradiation testing have established broad trends, a true mechanistic understanding of constituent redistribution has not been achieved. Here, in this study, we use electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and transmission electron microscopy-based (TEM) selective area electron diffraction (SAED) on a EBR-II irradiated U-19 wt.% Pu-6 wt.% Zr fuel pin cross-section to correlate the composition, porosity, and crystallographic phases formed after irradiation. Constituent redistribution is thought to consist of three distinct zones, in which uranium and zirconium migrate while plutonium remains relatively unchanged. Our EPMA results resolve eight distinct compositional regions, and more importantly, show that plutonium redistributes alongside zirconium, contrary to historical assumptions. The distribution of fission products was highly asymmetric with a few large lanthanide precipitates observed at isolated sites on the pin periphery instead of a uniform distribution of smaller precipitates around the periphery. Using thermodynamic data from TAF-ID and the measured EPMA compositions, matrix phase fractions were predicted across the fuel radius. Phase predictions based on composition did not match TEM/SAED results, which revealed a much higher fraction of α−U phase than would be expected if phases were retained from reactor temperatures. These findings highlight the need for expanded SAED phase identification to capture post-irradiation and storage effects, as well as rigorous uncertainty quantification in fuel performance and phase diagram modeling to better constrain predictions from compositional data.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

A Microporous Metal–Organic Framework Incorporating Both Primary and Secondary Building Units for Splitting Alkane Isomers

We demonstrate the assembly of a mononuclear metal center, a hexanuclear cluster, and a V-shaped, trapezoidal tetracarboxylate linker into a microporous MOF featuring an unprecedented 3-nodal (4,4,8)-c lyu topology. Here, the compound, HIAM-302, represents the first example that incorporates both primary building unit (PBU) and hexanuclear secondary building unit (SBU) in one structure, which should be attributed to the desymmetrized geometry of the organic linker. HIAM-302 possesses optimal pore dimensions and can separate mono-branched and di-branched alkanes through selective molecular sieving, which is of significant value in petrochemical industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Infinitely rugged intra-cage potential energy landscape in metallic glasses caused by many-body interaction

The absence of translational symmetry in glassy materials poses a significant challenge in establishing effective structure-property relationships in real space. Consequently, the potential energy landscape (PEL) in phase space is widely utilized to comprehend the complex phenomena in glasses. The classical PEL features a two-scale profile comprising mega-basins and sub-basins, corresponding to α-relaxations (e.g. glass transition) and β-relaxations (e.g. local cage-breaking atomic rearrangements), respectively. Recent studies, however, reveal that sub-basins are not smooth and contain finer structures, the origins of which remain elusive. Here we probe the smoothness of sub-basin bottoms in glasses' PEL by introducing small intra-cage cyclic loading and then measuring the net changes in atomic-level stresses. Compared to glasses with pair interaction, glasses with many-body interaction exhibit orders-of-magnitude larger and loading-dependent stress changes even before the first cage-breaking event takes place, which reflect much more feature-rich sub-basins. We further demonstrate this stark contrast stems from the spatial distribution of individual atom's constraining force field. Specifically, at vanishing perturbations, many-body interactions disrupt the positive-definite synchrony in energy variations of the perturbed atom and the whole system, causing inherently less confined atomic responses and infinitely rugged sub-basins. The implications of these findings for the selective addition or removal of fine structures in the PEL and the subsequent tuning of glassy materials' responses to external stimuli are also explored.

36 MATERIALS SCIENCE↗

Population Count on Intel® CPU, GPU, and FPGA

Population count is a primitive used in many applications. Commodity processors have dedicated instructions for achieving high-performance population count. Motivated by the productivity of high-level synthesis and the importance of population count, in this paper we investigated the OpenCL implementations of population count algorithms, and evaluated their performance and resource utilizations on an FPGA. Based on the results, we select the most efficient implementation. Then we derived a reduction pattern from a representative application of population count. We parallelized the reduction with atomic functions, and optimized it with vectorized memory accesses, tree reduction, and compute-unit duplication. We evaluated the performance of the reduction kernel on an Intel (R) Xeon (R) CPU and an Intel (R) IrisT Pro integrated GPU, and an FPGA card that features an Intel (R) Arria (R) 10 FPGA. When DRAM memory bandwidth is comparable on the three computing platforms, the FPGA can achieve the highest kernel performance for large workload. On the other hand, we described performance bottlenecks on the FPGA. To make FPGAs more competitive in raw performance compared to high-performant CPU and GPU platforms, it is important to increase external memory bandwidth, minimize data movement between a host and a device, and reduce OpenCL runtime overhead on an FPGA.

heterogeneous computing↗

Modeling the Effects of Artificial Drainage on Agriculture-dominated Watersheds using a Fully Distributed Integrated Hydrology Model: Datasets, scripts, model files

This model-data archive supports the research paper that demonstrates the integration of agricultural drainage features—specifically, narrow engineered ditches and tile drains—into a fully distributed, basin-scale integrated surface-subsurface hydrology model (ISSHM), Amanzi-ATS. The model employs innovative computational meshes aligned with agricultural ditches and incorporates the physically based Hooghoudt's drainage equation to simulate tile drainage, offering a novel strategy that enhances the accuracy of hydrological simulations.The archived dataset includes input parameters, model configurations, and select simulation outputs for the Amanzi-ATS model that successfully captured the streamflow patterns in the Portage River Watershed as validated by USGS gauge readings. Jupyter notebook for the preparation of model inputs and post-processing of outputs are also included. The model's predictive performance achieved a normalized Kling-Gupta Efficiency (KGE) of 0.81, surpassing SWAT without the necessity for site-specific calibration.The Amanzi-ATS model presented in this modeL-data archive allows for numerical experiments to explore the shifts in the flow structure under different drainage scenarios. As a tool for advancing the understanding of distributed hydrological responses and nutrient cycling, this archived model provides valuable insights for researchers, modelers, and decision-makers involved in watershed management and environmental modeling.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include CSV and HDF5 files, which can be read through Python scripts. The input files for the ATS model, open-source integrated hydrology, and transport model, are in XML format and can be edited in any commonly used text editors.

54 ENVIRONMENTAL SCIENCES↗

Scaling from Flux Towers to Ecosystem Models: Regional Constraints on Carbon Cycle Processes from Atmospheric Carbonyl Sulfide (Final Report)

DOE supported research suggests that gross primary productivity (GPP) is largely underestimated by global earth system models [Welp et al., 2011], reflecting the persistent challenge in extrapolating from local-scale GPP observations to global-scale earth system models. This poor understanding of GPP at large spatial scales is of particular concern in tropical forests. In tropical forests, some earth systems models forecast a powerful feedback between a warming climate and a decline in GPP resulting in forest dieback. While this simulated feedback is intensely debated, we lack robust large-scale constraints on GPP that are needed to resolve this debate. In particular, carbon dioxide measurements provide valuable information on net carbon flux, but not on the gross flux associated with GPP. Here we conducted a study of regional-to-global scale GPP using atmospheric carbonyl sulfide to provide a new constraint on GPP mechanisms in earth system models. Our project activities integrated modeling, in situ measurement, and remote sensing techniques to resolve GPP for the Amazon as well as global scale trends. The results of this work included initiating airborne carbonyl sulfide monitoring in the Amazon, training for postdocs and graduate students at a Hispanic Serving Institution, fundamental advances in carbonyl sulfide budgets [e.g. Hilton et al., Nature Climate Change, 2017], and high-profile publications that focused on GPP trends for the Amazon [Stinecipher et al., GRL, 2022] and global historical GPP trends [Campbell, et al., Nature, 2017]. Based on the suggestion of our DOE program manager, we published a state-of-the-science commentary to the scientific community on GPP monitoring with COS [Campbell et al., EOS, 2017] which was selected as the cover story. DOE support was acknowledged in all reports. The importance of this research to understanding climate change was communicated to the general public through community seminars (Rotary, Public Libraries, State Parks), an op-ed (SF Chronicle), and interviews in the mass media including two stories in the New York Times (4/5/17; 7/30/18), one of which was especially widely read after it was featured in the New York Time’s Quote of the Day.

54 ENVIRONMENTAL SCIENCES↗

Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

54 ENVIRONMENTAL SCIENCES↗