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DOE OSTI2018

Chemical Modification Mechanisms in Hybrid Hafnium Oxo-methacrylate Nanocluster Photoresists for Extreme Ultraviolet Patterning

The potential implementation of extreme ultraviolet (EUV) lithography into next generation device processing is bringing urgency to identify resist materials that optimize EUV lithographic performance. Inorganic/organic hybrid nanoparticles or clusters constitute a promising new class of materials, with high EUV sensitivity from the core and tunable chemistry through the coordinating ligands. Development of a thorough mechanistic understanding of the solubility switching reactions in these materials is an essential first step toward their implementation in patterning applications but remains challenging due to the complexity of their structures, limitations in EUV sources, and lack of rigorous in situ characterization. Here, we report a mechanistic investigation of the solubility switching reactions in hybrid clusters comprising a small HfOx core capped with a methacrylic acid ligand shell (HfMAA). We show that EUV-induced reactions can be studied by performing in situ infrared (IR) spectroscopy of electron-irradiated films using a variable energy electron gun. Combining additional ex situ metrology, we track the chemical evolution of the material at each stage of a typical resist processing sequence. For instance, we find that a cross-linking reaction initiated by decarboxylation of the methacrylate ligands under electron irradiation constitutes the main solubility switching mechanism, although there are also chemical changes imparted by a typical post application bake (PAB) step alone. Lastly, synchrotron-based IR microspectroscopy measurements of EUV-irradiated HfMAA films enable a comparison of reactions induced by EUV vs electron beam irradiation of the same resist material, yielding important insight into the use of electron beam irradiation as an experimental model for EUV exposure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Manipulating the Lateral Diffusion of Surface-Anchored EGF Demonstrates that Receptor Clustering Modulates its Phosphorylation Levels

Upon activation, the epidermal growth factor (EGF) receptor becomes phosphorylated and triggers a vast signaling network that has profound effects on cell growth. The EGF receptor is observed to assemble into clusters after ligand binding and tyrosine kinase autophosphorylation, but the role of these assemblies in the receptor signaling pathway remains unclear. To address this question, we measured the phosphorylation of EGFR when the EGF ligand was anchored onto laterally mobile and immobile surfaces. We found that cells generated clusters of ligand-receptor complex on mobile EGF surfaces, and generated a lower ratio of phosphorylated EGFR to EGF than when compared to immobilized EGF that is unable to cluster. This result was verified by tuning the lateral assembly of ligand-receptor complexes on the surface of living cells using patterned supported lipid bilayers. Nanoscale metal lines fabricated into the supported membrane constrained lipid diffusion and EGF receptor assembly into micron and sub-micron scale corrals. Single cell analysis indicated that clustering impacts EGF receptor activation, and larger clusters (> 1 m2) of ligand-receptor complex generated lower EGF receptor phosphorylation per ligand than smaller assemblies (< 1 m2) in HCC1143 cells that were engaged to ligand-functionalized surfaces. We investigated EGFR clustering by treating cells with compounds that disrupt the cytoskeleton (Latrunculin-B), clathrin-mediated endocytosis (Pitstop2), and inhibit EGFR activation (Gefitinib). These results help elucidate the nature of large-scale EGFR clustering, thus underscoring the general significance of receptor spatial organization in tuning function.

Stabley, Daniel↗

DOE OSTI2015

Cellulose synthase gene expression profiling of Physcomitrella patens

Abstract The cellulose synthase ( CESA ) gene family of seed plants comprises six clades that encode isoforms with conserved expression patterns and distinct functions in cellulose synthesis complex ( CSC ) formation and primary and secondary cell wall synthesis. In mosses, which have rosette CSC s like those of seed plants but lack lignified secondary cell walls, the CESA gene family diversified independently and includes no members of the six functionally distinct seed plant clades. There are seven CESA isoforms encoded in the genome of the moss Physcomitrella patens . However, only Pp CESA 5 has been characterised functionally, and little information is available on the expression of other Pp CESA family members. We have profiled Pp CESA expression through quantitative RT ‐ PCR , analysis of promoter‐reporter lines, and cluster analysis of public microarray data in an effort to identify expression and co‐expression patterns that could help reveal the functions of Pp CESA isoforms in protein complex formation and development of specific tissues. In contrast to the tissue‐specific expression observed for seed plant CESA s, each of the Pp CESA s was broadly expressed throughout most developing tissues. Although a few statistically significant differences in expression of Pp CESA s were noted when some tissues and hormone treatments were compared, no strong co‐expression patterns were observed. Along with CESA phylogenies and lack of single Pp CESA mutant phenotypes reported elsewhere, broad overlapping expression of the Pp CESA s indicates a high degree of inter‐changeability and is consistent with a different pattern of functional specialisation in the evolution of the seed plant and moss CESA families.

Tran, M. L.↗

DOE OSTI2018

Ambient-Pressure X-ray Photoelectron Spectroscopy Characterization of Radiation-Induced Chemistries of Organotin Clusters

Advances in extreme ultraviolet (EUV) photolithography require the development of next-generation resists that allow high-volume nanomanufacturing with a single nanometer patterning resolution. Organotin-based photoresists have demonstrated nanopatterning with high resolution, high sensitivity, and low-line edge roughness. However, very little is known regarding the detailed reaction mechanisms that lead to radiation-induced solubility transitions. In this study, we investigate the interaction of soft X-ray radiation with organotin clusters to better understand radiation-induced chemistries associated with EUV lithography. Butyltin Keggin clusters (β-NaSn13) were used as a model organotin photoresist, and characterization was performed using ambient-pressure X-ray photoelectron spectroscopy. The changes in relative atomic concentrations and associated chemical states in β-NaSn 13 resists were evaluated after exposure to radiation for a range of ambient conditions and photon energies. A significant reduction in the C 1s signal versus exposure time was observed, which corresponds to the radiation-induced homolytic cleavage of the butyltin bond in the β-NaSn 13 clusters. To improve the resist sensitivity, we evaluated the effect of oxygen partial pressure during radiation exposures. Here, we found that both photon energy and oxygen partial pressure had a strong influence on the butyl group desorption rate. These studies advance the understanding of radiation-induced processes in β-NaSn 13 photoresists and provide mechanistic insights for EUV photolithography.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2010

Mimas: Preliminary Evidence For Amorphous Water Ice from VIMS

We have conducted a statistical clustering analysis (1,2) on a mosaic of VIMS data cubes obtained on February 13, 2010, for Saturn s satellite Mimas. Seven VIMS cubes were geometrically projected and re-sampled to a common spatial resolution. The clustering technique consists of a partitioning algorithm coupled to a criterion that prevents sub-optimal solutions and tests for the influence of random noise in the measurements. The clustering technique is agnostic about the meaning of the clusters, and scientific interpretation requires their a posteriori evaluation. The preliminary results yielded five clusters, demonstrating that spectral variability across Mimas surface is statistically significant. The ratios of the means calculated for each of the clusters show structure within the 1.6- micron water ice band, as well as the shape and the central wavelength of the strong ice band at 2 micron, that map spatially in patterns apparently related to the topography of Mimas, in particular certain regions in and around Herschel crater. The mean spectra of the five clusters, show similarities with laboratory spectra of amorphous and crystalline H2O ice (3) that are suggestive of the presence of an amorphous ice component in certain regions of Mimas, notably on the central peak of Herschel, on the crater floor, and in faults surrounding the crater. This may represent a mixture of both ice phases, or perhaps a layer of amorphous ice on a base of crystalline ice. Another possible occurrence of amorphous ice appears southwest of Herschel, close to the south pole.

Cruikshank, Dale P.↗

DOE OSTI2026

Optimal invariant sets for atomistic machine learning

The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Unsupervised texture image segmentation by improved neural network ART2

We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

Wang, Zhiling↗

DOE OSTI2026

Differences in cluster and internal wake effects from mesoscale and large-eddy simulations off the US East Coast

Mesoscale simulations are increasingly used to estimate wake effects within and between large wind farms, despite limited validation for large-scale wake effects. This study evaluates the capabilities and limitations of mesoscale simulations in capturing wake-induced impacts on wind turbine power production through a direct comparison with large-domain large-eddy simulations (LESs) for three planned offshore wind farms under realistic atmospheric conditions and a range of atmospheric stabilities. We assess mesoscale performance in replicating wake characteristics behind single and multiple turbine clusters and quantify the resulting variability in mean turbine power. Results show that mesoscale Weather Research and Forecasting simulations with the Fitch wind farm parameterization capture key features of the velocity deficit downstream of both single and multiple wind farms, with mean root-mean-square errors near 5 % and good agreement with stability-driven wake behavior. However, in these simulations, the mesoscale Fitch parameterization underestimates power losses from internal wake effects, particularly when turbines align with the prevailing wind direction or under stable stratification. In these conditions, individual wakes persist and dominate downstream power deficits. The coarse resolution of the mesoscale simulations limits their ability to resolve individual wind turbine wakes that drive power fluctuations within wind farms. Nonetheless, mesoscale simulations can yield accurate estimates of combined wake losses from internal and cluster effects across some wind direction sectors, where errors in wake representation may cancel each other out. These findings underscore the strengths of mesoscale simulations for capturing broader wake patterns while highlighting their limitations for modeling turbine-level power losses. Future work should explore hybrid modeling approaches to capture both long-range cluster wake propagation and localized internal wake dynamics.

17 WIND ENERGY↗

NASA NTRS1990

Down-to-earth studies of carbon clusters

Recent advances in supersonic beam experiments with laser-vaporization sources of clusters have provided some interesting new insights into the nature of the small clusters of carbon, and the processes through which carbon condenses. One cluster in particular, C(sub 60), appears to play a central role. It is argued that this cluster takes the shape of a soccerball: a hollow sphere composed of a shell of 60 carbon atoms connected by a lattice of hexagonal and pentagonal rings, in a pattern of overall icosahedral symmetry. Although C(sub 60) appears to be uniquely stable due to its perfect symmetry, all other even-numbered carbon clusters in the 32 to 100+ atom size range seem to favor similar closed spheroidal forms. These species are interpreted as relatively unreactive side products in condensation reactions of carbon vapor involving spiraling graphitic sheets. The prevalence of C(sub 60) in laser-vaporized carbon vapors and sooting flames suggests that it may be formed readily whenever carbon condenses. Such ready formation and extraordinary stability may have substantial astrophysical implications.

Smalley, R. E.↗

DOE OSTI2025

Root size and soil physicochemical properties drive microscale spatial patterns of Fe and As retention in the rice rhizosphere

Background and Aims: Radial oxygen loss from rice roots in flooded soils oxidizes and precipitates dissolved Fe(II), Mn(II), and As(III) into mixed Fe(III), Mn(III/IV), and As(V) as root plaque and in the rhizosphere soil. It is unknown how different soils and root sizes impact the spatial extent of Fe and As retention outside the root. Methods: We imaged cross-sections of 90 roots from 6 different soils using synchrotron μXRF imaging followed by k-means clustering and elliptical averaging to distinguish bulk soil, rhizosphere, plaque, and roots based on As and Fe patterns. Results: We found preferential As retention in the plaque and rhizospheres of most roots except small (< 0.45 mm) roots in silty soils with low P or high As. In contrast, clayey soils had similar As-Fe correlations across plaque, rhizosphere, and bulk soil. Large (> 0.45 mm) roots often had no oxidized rhizosphere region. We obtained an extensive dataset of 256 As and 155 Mn synchrotron μXANES measurements, which revealed that rhizosphere and plaque As was mainly inorganic As(V) and As(III), and Mn oxidation state varied between soils but not between belowground locations. Conclusion: Small roots in coarse-textured soils were less likely to have As retention in the plaque or rhizosphere compared to large roots and fine-textured soils. Furthermore, the unique and extensive data in this study provides new insight into soil and root size impacts on As retention in the rhizosphere. It is essential to investigate a representative number of samples to draw conclusions from XRF imaging.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Shifting temporal dynamics of human mobility in the United States

In this paper we analyze the average hourly temporal dynamics of human mobility in the United States from 2019 to 2020. We discuss how large decreases in human mobility nonuniformly effect the daily temporal dynamics of aggregate human behavior. The data used are weekly activity patterns for POIs from 2019 to 2020 in the United States, provided by SafeGraph and made openly available to academic and research institutions. We use clustering methods to create metrics describing how human activity changes throughout the day/week at the county and national levels. In response to significant mobility reductions starting March 2020, daily temporal patterns of human activity changed nonuniformly. Morning activity started later, and evening activity started earlier in 2020 compared to 2019, and temporal behavioral patterns on weekdays began to look more similar to weekends. The changes in daily temporal behavior persisted throughout the year even as total mobility levels recovered. The results provide insights on the changes in human behavior in response covid-19 policies and illustrate influences on social systems, health, and transportation networks.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

Abstract Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peak-shifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Photoelectron Spectroscopy and Computational Study on Microsolvated [B 10 H 10 ] 2– Clusters and Comparisons to Their [B 12 H 12 ] 2– Analogues

Microhydrated closo-Boranes have attracted great interests due to their superchaotropic activity related to well-known Hofmeister effect and important applications in biomedical and battery fields. In this work, we report a combined negative ion photoelectron spectroscopy and quantum chemical investigation on hydrated closo-decaborate clusters [B 10 H 10 ] 2- ·nH 2 O (n = 1 – 7) with a direct comparison to their analogues [B 12 H 12 ] 2- ·nH 2 O and free water clusters. A single H 2 O molecule is found sufficient to stabilize the intrinsically unstable [B 10 H 10 ] 2- dianion. The first two water molecules strongly interact with the solute forming B-H···H-O dihydrogen bonds while additional water molecules show substantially reduced binding energies. Unlike [B 12 H 12 ] 2- ·nH 2 O possessing highly structured water network with the attached H 2 O molecules arranged in a unified pattern by maximizing B-H···H-O dihydrogen bonding, distinct structural arrangements of the water clusters within [B 10 H 10 ] 2– ·nH 2 O are achieved with the water cluster networks from trimer to heptamer resembling free water clusters. Such a distinct difference arises from the variations in size, symmetry, and charge distributions between these two dianions. Finally, the present finding again confirms the structural diversity of hydrogen-bonding networks in microhydrated closo-boranes and enrich our understanding of aqueous borate chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Decreasing Aerosol Loading in the North American Monsoon Region

Here, we examine the spatio-temporal variability of aerosol loading in the recent decade (2005-2014) over the North American Monsoon (NAM) region. Emerging patterns are characterized using aerosol optical depth (AOD) retrievals from the NASA Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) instrument along with a suite of satellite retrievals of atmospheric and land-surface properties. We selected 20 aerosol hotspots and classified them into fire, anthropogenic, dust, and NAM alley clusters based on the dominant driver influencing aerosol variability. We then analyzed multivariate statistics of associated anomalies during pre-, monsoon, and post-monsoon periods. Our results show a decrease in aerosol loading for the entire NAM region, confirming previous reports of a declining AOD trend over the continental United States. This is evident during pre-monsoon and monsoon for fire and anthropogenic clusters, which are associated with a decrease in the lower and upper quartile of fire counts and carbon monoxide, respectively. The overall pattern is obfuscated in the NAM alley, especially during monsoon and post-monsoon seasons. While the NAM alley is mostly affected by monsoon precipitation, the frequent occurrence of dust storms in the area modulates this trend. We find that aerosol loading in the dust cluster is associated with observed vegetation index and has only slightly decreased in the recent decade.

58 GEOSCIENCES↗

DOE OSTI2022

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Searching for metastable particles using graph computing

The reconstruction of charged particle trajectories at the Large Hadron Collider and future colliders relies on energy depositions in sensors placed at distances ranging from a centimeter to a meter from the colliding beams. We propose a method of detecting charged particles that decay invisibly after traversing a short distance of about 25 cm inside the experimental apparatus. One of the decay products may constitute the dark matter known to be 84% of all matter at galactic and cosmological distance scales. Our method uses graph computing to cluster spacepoints recorded by two-dimensional silicon pixel sensors into mathematically-defined patterns. The algorithm may be implemented on silicon-based integrated circuits using field-programmable gate array technology to augment or replace traditional computing platforms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Background rejection in NEXT using deep neural networks

Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at $\sqrt{s}$ = 13TeV, corresponding to an integrated luminosity of 35.9 fb -1 . Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗