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DOE OSTI2026

Characterizing Spatial and Temporal Variability of California’s Coastal Upwelling Using a Satellite-Derived CUTI Index

Ocean upwelling off California is a wind-driven coastal process with complex interactions between spatial wind patterns and sea surface temperatures. Large-scale spatial and temporal upwelling characterizations are traditionally based on numerical models, while quantifying upwelling with direct observations is not straightforward. To complement model-based upwelling characterizations, here we use satellite estimates of winds, sea surface temperatures, and ocean currents along the California coast to calculate the coastal upwelling transport index (CUTI), originally calculated from a numerical ocean model. The CUTI framework accounts for both Ekman and geostrophic offshore-directed ocean transport, the latter of which is often neglected in satellite-based upwelling assessments. The approach yields the CUTI over a 30-yr period at a daily temporal and 0.25° spatial resolution to analyze upwelling patterns, with focus on potential Morro Bay and Humboldt wind energy areas. Morro Bay (CUTI median of 0.63 m 2 s −1 ) exhibits stronger, more consistent upwelling throughout the year, whereas Humboldt (CUTI median of 0.45 m 2 s −1 ) exhibits a pronounced seasonal cycle with strongest upwelling in the summer and occasional downwelling in winter. The cluster analysis of 1993–2022 satellite maps of CUTI identifies four characteristic spatial upwelling patterns along the coast, corresponding to distinct spatial distributions of sea surface temperature anomalies, Ekman transport, and geostrophic ocean water transport. This dataset provides an observation-based foundation for characterizing natural coastal upwelling off the California coast, showing good agreement with the original model CUTI while being based on satellite data and offering increased spatial resolution.

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Fast Evaluation of Aircraft Icing Severity Using Machine Learning Based on XGBoost

Aircraft icing represents a serious hazard in aviation which has caused a number of fatal accidents over the years. In addition, it can lead to substantial increase in drag and weight, thus reducing the aerodynamics performance of the airplane. The process of ice accretion on a solid surface is a complex interaction of aerodynamic and environmental variables. The complex relationship makes machine learning-based methods an attractive alternative to traditional numerical simulation-based approaches. In this study, we introduce a purely data-driven approach to find the complex pattern between different flight conditions and aircraft icing severity prediction. The supervised learning algorithm Extreme Gradient Boosting (XGBoost) is applied to establish the prediction framework which makes prediction based on any set of observations. The input flight conditions for the proposed prediction framework are liquid water content, droplet diameter and exposure time. The proposed approach is demonstrated in three cases: maximum ice thickness prediction, icing area prediction and icing severity level evaluation. Performance comparison studies and error analysis are also conducted to verify the effectiveness and performance of the proposed method. Results show that the proposed method has reasonable capability in evaluating aircraft icing severity.

Li, Sibo (ORCID:000000021705844X)↗

DOE OSTI2021

ParFlow

ParFlow is an open-source, modular, parallel watershed flow model. It includes fully-integrated overland flow, the ability to simulate complex topography, geology and heterogeneity and coupled land-surface processes including the land-energy budget, biogeochemistry and snow (via CLM). It is multi-platform and runs with a common I/O structure from laptop to supercomputer. ParFlow is the result of a long, multi-institutional development history and is now a collaborative effort between CSM, LLNL, UniBonn and UCB. ParFlow has been coupled to the mesoscale, meteorological code ARPS and the NCAR code WRF.

Smith, Steven↗

DOE OSTI2021

ParFlow

Smith, Steven↗

DOE OSTI2024

High power pulse systems for surface processing

A high-power pulsed surface processing system includes insulated-gate bipolar transistors (IGBT) to replicate desirable pulse structures with high precision, at low cost, and with high reliability within a single system. The pulsed surface processing system includes a power supply, an anode and a cathode, a dual gate driver supplying power to one or more IGBT gates, and one or more capacitor banks. Pulse formation software controls the timing and duration of electrical pulses to the electrodes. A freewheeling diode protects the system from an abrupt reduction of current in the circuit. The high-power pulsed surface processing system may be used to control versatile and complex pulse structures while with precise control of instantaneous pulse powers, pulse timing, and process control. The inclusion of dual gate drivers also offers the ability for multiple pulsers to be created and “slaved” together for a wide variety of custom processes.

Tian, Hui↗

DOE OSTI2021

Intelligent Agents for the Optimization of Atomic Layer Deposition

Atomic layer deposition (ALD) is a highly controllable thin film synthesis approach with applications in computing, energy, and separations. The flexibility of ALD means that it can access a massive chemical catalogue; however, this chemical and process diversity results in significant challenges in determining processing parameters that result in stable and uniform film growth with minimal precursor consumption. In situ measurements of the ALD growth per cycle (GPC) can accelerate process development but it still requires expert intuition and time-consuming trial and error to identify acceptable processing parameters. This procedure is made more difficult by the presence of experimental noise in the GPC values and the complexity of ALD surface chemistries. Here, a need exists for efficient optimization approaches capable of autonomously determining processing conditions resulting in optimal ALD film growth. In this work, we present the development of three optimization strategies and compare their performance in optimizing four simulated ALD processes. Furthermore, the effect of noise in the GPC measurements on optimization convergence is studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Surface Reordering During Layer‐by‐Layer Growth on SrTiO 3

With the development of layer‐by‐layer growth techniques such as molecular beam epitaxy (MBE), it is now possible to construct materials with atomic‐level precision, with the layer sequence primarily determined by the growth recipe. However, materials can still restructure in the high‐temperature growth environment with surface thermodynamics strongly influencing growth behavior. Here, we demonstrate that growth on ${\rm SrTiO}_{3}$ (001), the principal platform for oxide electronics, does not occur in a layer‐by‐layer fashion but follows a more complex process in which a ${\rm TiO}_{2}$ plane continually diffuses toward the growth surface. Employing in situ synchrotron X‐ray scattering combined with ab initio thermodynamic calculations, we discover the existence of a stable ${\rm TiO}_{2}$ double‐layer structure on the pristine substrate and the occurrence of dynamic layer rearrangement during homoepitaxial growth by oxide MBE. Our findings suggest that the current methods used to precisely control ${\rm SrTiO}_{3}$ surfaces are limited, as well as our ability to dictate the composition of ultrathin films, resulting in important ramifications regarding the surface reactivity of perovskite materials grown on ${\rm SrTiO}_{3}$.

Coherent Bragg Rod Analysis↗

DOE OSTI2025

The Role of Surface Hydroxyls in Dehydration and Dehydrogenation of Formic Acid on Fe 3 O 4 (001)

Understanding the role of surface structure and hydroxylation in catalytic reactions on metal oxide surfaces is important for developing a mechanistic insight into the complex interface processes. Here, we investigate the reactivity of formic acid on reconstructed Fe 3 O 4 (001) using a combination of X-ray photoelectron spectroscopy, infrared reflection absorption spectroscopy, temperature-programmed reaction spectroscopy, low energy electron diffraction, and electronic structure calculations. We find that formic acid initially dissociates at low temperatures (< 80 K) into bidentate formate and a hydroxyl up to an initial dosed coverage of two HCOOH per Fe 3 O 4 (001) unit cell. At higher temperatures (> 450 K), formate largely decomposes along the dehydration pathway, producing CO and H 2 O, with dehydrogenation to CO 2 being a minority side reaction. As a first step, water formation leads to surface oxygen extraction via the Mars-van Krevelen mechanism. Computational studies reveal formate embedded in oxygen vacancies as a key intermediate in the CO formation mechanism. CO formation proceeds via two reaction pathways with desorption that peaks at 530 K on the hydroxyl-rich surface and 560 K on the hydroxyl-deficient surface. Atomic hydrogen coadsorption experiments and ab initio calculations reveal that the presence of surface hydroxyls reduces the CO formation barrier. Furthermore, these results highlight the complex interactions between substrate and intermediate species occurring during reactions on metal oxide surfaces.

Adsorption↗

DOE OSTI2024

Intermittent Criticality Multi‐Scale Processes Leading to Large Slip Events on Rough Laboratory Faults

Abstract We discuss data of three laboratory stick‐slip experiments on Westerly Granite samples performed at elevated confining pressure and constant displacement rate on rough fracture surfaces. The experiments produced complex slip patterns including fast and slow ruptures with large and small fault slips, as well as failure events on the fault surface producing acoustic emission bursts without externally‐detectable stress drop. Preparatory processes leading to large slips were tracked with an ensemble of ten seismo‐mechanical and statistical parameters characterizing local and global damage and stress evolution, localization and clustering processes, as well as event interactions. We decompose complex spatio‐temporal trends in the lab‐quake characteristics and identify persistent effects of evolving fault roughness and damage at different length scales, and local stress evolution approaching large events. The observed trends highlight labquake localization processes on different spatial and temporal scales. The preparatory process of large slip events includes smaller events marked by confined bursts of acoustic emission activity that collectively prepare the fault surface for a system‐wide failure by conditioning the large‐scale stress field. Our results are consistent overall with an evolving process of intermittent criticality leading to large failure events, and may contribute to improved forecasting of large natural earthquakes.

Geochemistry & Geophysics↗

DOE OSTI2024

Variations of organic functional chemistry in carbonaceous matter from the asteroid 162173 Ryugu

Abstract Primordial carbon delivered to the early earth by asteroids and meteorites provided a diverse source of extraterrestrial organics from pre-existing simple organic compounds, complex solar-irradiated macromolecules, and macromolecules from extended hydrothermal processing. Surface regolith collected by the Hayabusa2 spacecraft from the carbon-rich asteroid 162173 Ryugu present a unique opportunity to untangle the sources and processing history of carbonaceous matter. Here we show carbonaceous grains in Ryugu can be classified into three main populations defined by spectral shape: Highly aromatic (HA), Alkyl-Aromatic (AA), and IOM-like (IL). These carbon populations may be related to primordial chemistry, since C and N isotopic compositions vary between the three groups. Diffuse carbon is occasionally dominated by molecular carbonate preferentially associated with coarse-grained phyllosilicate minerals. Compared to related carbonaceous meteorites, the greater diversity of organic functional chemistry in Ryugu indicate the pristine condition of these asteroid samples.

Science & Technology - Other Topics↗

DOE OSTI2020

Water adsorption on vanadium oxide thin films in ambient relative humidity

Here in this work, ambient pressure x-ray photoelectron spectroscopy (APXPS) is used to study the initial stages of water adsorption on vanadium oxide surfaces. V 2p, O 1s, C 1s, and valence band XPS spectra were collected as a function of relative humidity in a series of isotherm and isobar experiments. Experiments were carried out on two VO 2 thin films on TiO 2 (100) substrates, prepared with different surface cleaning procedures. Hydroxyl and molecular water surface species were identified, with up to 0.5 ML hydroxide present at the minimum relative humidity, and a consistent molecular water adsorption onset occurring around 0.01% relative humidity. The work function was found to increase with increasing relative humidity, suggesting that surface water and hydroxyl species are oriented with the hydrogen atoms directed away from the surface. Changes in the valence band were also observed as a function of relative humidity. The results were similar to those observed in APXPS experiments on other transition metal oxide surfaces, suggesting that H 2 O-OH and H 2 O-H 2 O surface complex formation plays an important role in the oxide wetting process and water dissociation. Compared to polycrystalline vanadium metal, these vanadium oxide films generate less hydroxide and appear to be more favorable for molecular water adsorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Artificial Intelligence for imaging Cherenkov detectors at the EIC

Abstract Imaging Cherenkov detectors form the backbone of particle identification (PID) at the future Electron Ion Collider (EIC). Currently all the designs for the first EIC detector proposal use a dual Ring Imaging CHerenkov (dRICH) detector in the hadron endcap, a Detector for Internally Reflected Cherenkov (DIRC) light in the barrel, and a modular RICH (mRICH) in the electron endcap. These detectors involve optical processes with many photons that need to be tracked through complex surfaces at the simulation level, while for reconstruction they rely on pattern recognition of ring images. This proceeding summarizes ongoing efforts and possible applications of AI for imaging Cherenkov detectors at EIC. In particular we will provide the example of the dRICH for the AI-assisted design and of the DIRC for simulation and particle identification from complex patterns and discuss possible advantages of using AI.

Instruments & Instrumentation↗

DOE OSTI2020

Chemisorption-Driven Roughening of Hydrothermally Grown KTa1–xNbxO3 Nanoparticles

The chemical and physical properties of nanoparticle surfaces have significant effects on their growth processes and the resulting morphology. Hydrothermally grown KNbO3, KTaO3, and KTa1–xNbxO3 were studied to examine the complex relationships between surface composition, phase, chemistry, and energetics and how these may be used to model and thereby control nanoparticle growth mechanisms. Two different composition-dependent growth modes were identified, where one type formed smooth surface facets, while the other resulted in roughened nanoparticle morphologies. Electron microscopy characterization, density functional theory calculations, and mathematical growth models were used to illuminate the role of surface properties and chemisorption on the nanoparticle growth morphology. Surface energy reduction by chemisorption can increase the rate of terrace nucleation, driving the roughening of these lower energy nanoparticle surfaces.

Ly, Tiffany↗

DOE OSTI2020

Characterizing Physical Properties of Streambed Interface Sediments Using In Situ Complex Electrical Conductivity Measurements

Streambed sediment physical properties such as surface area, are difficult to quantify in situ but exert a high-level control on a wide range of biogeochemical processes and sorption of contaminants. We introduce the use of complex electrical conductivity (CC) methods (also known as spectral-induced polarization (SIP)) that measure both real and imaginary conductivity to noninvasively and efficiently characterize shallow streambed sediments. We explore the method through synthetic modeling, laboratory, and field measurements to demonstrate the sensitivity of imaginary conductivity to sediment surface area, controlled in part by fine-grained iron oxides produced by anoxic groundwater discharge. Laboratory measurements verify expected relationships between CC parameters and sediment properties. Synthetic modeling using a 1D analytical model illustrates the influence of water layer depth and conductivity on the field CC measurements made at the streambed-stream water interface. Specifically, the inverted sediment imaginary conductivity is less impacted by uncertainty in the water layer depth and conductivity relative to the real conductivity and phase shift. Field CC measurements along a landfill-impacted river reveal discrete streambed zones with enhanced bulk surface area generally corresponding to anoxic groundwater discharge zones with high concentrations of fine-grained iron oxide precipitates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Reaction Optimization for Enzymatic Deconstruction of Industrially Relevant Nylon Composites

Plastics such as polyamides (PAs) possess unique physicochemical properties that make them indispensable in modern society. However, their energy‐intensive production and challenging end‐of‐life management highlight the urgent need for efficient recycling or remanufacturing solutions. Enzymatic depolymerization offers a promising route toward circular recycling, yet remains constrained by limited enzyme characterization, lack of validation under industrially relevant conditions and substrates, and overall performance. Here, we optimized the reaction conditions for three recently discovered nylon‐degrading enzymes. One of them, Nyl12, achieved product titers with PA6 and PA66 that exceed previously reported values, without enzyme engineering or substrate pretreatment. We further demonstrated the scalability of the process and its application to complex PA‐based materials used in microelectronic components. Analysis of substrate features, including surface area and particle size, revealed key parameters governing enzymatic activity and provided a framework for future pretreatment and process optimization efforts. In combination, these efforts provide a new benchmark for enzymatic nylon recycling.

nylon↗

DOE OSTI2026

Low-energy ion beam sputtering of polycrystalline diamond

Ion beam milling is a highly versatile and precise technique for the microfabrication of diamond films, enabling controlled material removal at both micrometer and sub-micrometer scales while accommodating complex geometries and surface features. Comprehensive sputtering yield data are essential for process optimization. In this work, we systematically measure the sputter yield of polycrystalline diamond bombarded with $\textrm{N}_2^+$, Ne + , Ar + , Kr + , and Xe + ions over an energy range of 300–900 eV and beam incidence angles of 0°−80°, providing data critical for precision machining applications. Results show that the sputter yield increases with both ion energy and the incidence angle and decreases with increasing ion mass. For $\textrm{N}_2^+$ ions, the sputter yield is largely insensitive to ion energy and exceeds values expected for pure physical sputtering. The concurrent formation of volatile C 2 N 2 molecules during $\textrm{N}_2^+$ ion bombardment suggests that the process is dominated by chemical sputtering. The absence of a peak in the dependence of the sputter yield on the beam incidence angle expected at oblique incidence angles is attributed to the effects of surface roughness.

Materials science↗