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Three adaptive line enhancer (ALE) algorithms and architectures, namely conventional ALE, ALE with Double Filtering (ALEDF), and ALE with Coherent Accumulation (ALECA) are investigated for fast carrier acquisition in time-domain.
The calibration standards used in the Atmospheric Lifetime Experiment (ALE) for CFCl3, CF2Cl2, CH3CCl3, and CCl4 are described. This includes the preparation of the primary standards by static dilution and their propagation and stability for the period 1977-1982. Two independent assessments of the absolute concentrations of the primary standards used to initiate the ALE measurements in 1977-1978 are reported. For consistency in the ALE program the values assigned to the primary standards and subsequent working standards used in the field were not altered during the experiment when results of better estimates of the original concentration values were obtained. Rather, the appropriate factors by which the ALE mixing ratios for a given species should be multiplied to obtain the best estimate of the current concentration of a given species, are provided.
Three adaptive line enhancer (ALE) algorithms and architectures - namely, conventional ALE, ALE with double filtering, and ALE with coherent accumulation - are investigated for fast carrier acquisition in the time domain. The advantages of these algorithms are their simplicity, flexibility, robustness, and applicability to general situations including the Earth-to-space uplink carrier acquisition and tracking of the spacecraft. In the acquisition mode, these algorithms act as bandpass filters; hence, the carrier-to-noise ratio (CNR) is improved for fast acquisition. In the tracking mode, these algorithms simply act as lowpass filters to improve signal-to-noise ratio; hence, better tracking performance is obtained. It is not necessary to have a priori knowledge of the received signal parameters, such as CNR, Doppler, and carrier sweeping rate. The implementation of these algorithms is in the time domain (as opposed to the frequency domain, such as the fast Fourier transform (FFT)). The carrier frequency estimation can be updated in real time at each time sample (as opposed to the batch processing of the FFT). The carrier frequency to be acquired can be time varying, and the noise can be non-Gaussian, nonstationary, and colored.
This work complements our previous manuscript [J. Vac. Sci. Technol. A41, 062602 (2023)] where predictions from molecular dynamics (MD) simulations of silicon–chlorine–argon (Si–Cl 2 –Ar) atomic layer etching (ALE) are compared to experiments. When etch product distributions for atomic chlorine (Cl) and silicon chlorides were initially compared to optical emission spectroscopy (OES) signals, it appeared that there was a discrepancy between the MD predictions and experimental results at higher ion fluences. Experiments showed a relatively long period of nearly constant Cl-containing etch products released from the ion-bombarded surface (referred to as the “plateau”) but this effect was not observed in MD simulations. In this report, we demonstrate that the “plateau” observed in the OES signals is most likely due to the desorption of Cl-containing etch products from the walls of the reactor and subsequent adsorption on the Si substrate. Experiments varying the gas residence time in the chamber while keeping incoming gas concentrations and pressure constant support this interpretation. We also conducted experiments with an additional Ar-only flow in the chamber to reduce the concentration of Cl-containing species on the chamber walls. For both sets of flow modification experiments, we observe results consistent with the hypothesis that Cl-containing species desorbing from chamber walls are a significant cause of the observed discrepancy between MD predictions and experimental observations. If the measured OES signals are corrected for this “additional” source of Cl-containing species at the surface, the MD predictions and measured OES signals are in excellent agreement. This further supports the predictive capability of MD simulations to accurately capture the relevant physical and chemical processes in plasma-assisted ALE processes. We provide an order of magnitude estimate of the required density of Cl-containing species that would account for the additional etch products observed. Finally, we discuss the implications of this effect on ALE in plasma nanofabrication.
Applications of near-surface nitrogen-vacancy (NV) centers in diamond are often limited by surface defects created during processing. Understanding and controlling plasma-induced surface damage is important for preserving the optical and spin properties of diamond NV centers. We report molecular dynamics simulations of a novel form of plasma-aided atomic layer etching of diamond. In this proposed scheme, the initial surface modification step consists of Ar + ion bombardment. This creates an amorphous layer at the surface, the thickness of which is controlled by the ion energy. Amorphization is known to help smooth the surface, at least locally, and would also serve to sputter clean an initially contaminated surface. A second step impacts the amorphous carbon layer with O + between about 1 and 5 eV. Simulations show that this energy range will remove the amorphous carbon but will not etch the underlying diamond. Though this energy range is not trivial to achieve in a conventional low-temperature plasma, potential methods for creating these low-energy O + ions are discussed. In addition to a-C etching, the O + impacts are predicted to remove any isolated (100) diamond terraces by selectively attacking the edges of the terraces. The proposed atomic layer etching (ALE) approach reverses the conventional ALE sequence in which the surface modification step is usually chemical modification (oxidation in this case), followed by a removal step using Ar + impacts. This plasma ALE procedure is predicted to create a diamond surface that is atomically flat and defect free.
Biologists first documented elk on the Hanford Site in 1972; since then, the elk herd known as the Rattlesnake Hills Elk Herd (RHEH) has grown substantially (PNNL-13331, Population Characteristics and Seasonal Movement Patterns of the Rattlesnake Hills Elk Herd: Status Report 2000). Through the 1990s, the core range of the RHEH was focused on the portion of the Hanford Reach National Monument known as the Fitzner-Eberhardt Arid Lands Ecology (ALE) Reserve. More recently, larger numbers of elk have been occupying the U.S. Department of Energy (DOE), Hanford Field Office (HFO), formerly the DOE, Richland Operations Office managed portion- of the Hanford Site, known as central Hanford (Figure 1-1). This began with bachelor groups of bulls occupying the site intermittently, and the herd has grown to resident herds, including bulls, cows, and calves. The Washington Department of Fish and Wildlife (WDFW) established a target herd size for the RHEH of less than 350 animals to minimize damages on adjacent private agricultural lands (Washington State Elk Herd Plan–Yakima Elk Herd [WDFW 2002]). Attempts to control the RHEH population through hunting on private lands and a relocation effort during 2000 have failed to limit growth of the population toward the WDFW target, and the herd exceeds 1,600 according to recent counts (PNNL-13331; DOE/RL- 2023-20, Hanford Annual Site Environmental Report for Calendar Year 2022). Although elk are present on central Hanford year-round, they continue to move between areas offsite, the ALE Reserve, and central Hanford throughout the year, and it is these movements that result in many of the elk-vehicle collisions (EVC) that occur along Hanford Site roads and on the Washington State highways bordering the site. In addition, changes in management strategies on the ALE Reserve, including recent and potential future tribal elk hunts, may alter herd behavior and result in additional animals transiting onto and off central Hanford.
Explicit finite element techniques employing an Arbitrary Lagrangian-Eulerian (ALE) methodology, within the transient dynamic code LS-DYNA, are used to predict splashdown loads on a proposed replacement/upgrade of the hydrazine tanks on the thrust vector control system housed within the aft skirt of a Space Shuttle Solid Rocket Booster. Two preliminary studies are performed prior to the full aft skirt analysis: An analysis of the proposed tank impacting water without supporting aft skirt structure, and an analysis of space capsule water drop tests conducted at NASA's Langley Research Center. Results from the preliminary studies provide confidence that useful predictions can be made by applying the ALE methodology to a detailed analysis of a 26-degree section of the skirt with proposed tank attached. Results for all three studies are presented and compared to limited experimental data. The challenges of using the LS-DYNA ALE capability for this type of analysis are discussed.
Two dynamic inflation simulation methods are employed for modeling the deployment of folded thin-membrane tubes. The simulations are necessary because ground tests include gravity effects and may poorly represent deployment in space. The two simulation methods are referred to as the Control Volume (CV) method and the Arbitrary Lagrangian Eulerian (ALE) method. They are available in the LS-DYNA nonlinear dynamic finite element code. Both methods are suitable for modeling the interactions between the inflation gas and the thin-membrane tube structures. The CV method only considers the pressure induced by the inflation gas in the simulation, while the ALE method models the actual flow of the inflation gas. Thus, the transient fluid properties at any location within the tube can be predicted by the ALE method. Deployment simulations of three packaged tube models; namely coiled, Z-folded, and telescopically-folded configurations, are performed. Results predicted by both methods for the telescopically-folded configuration are correlated and computational efficiency issues are discussed.
The feasibility of using Control Volume (CV) method and the Arbitrary Lagrangian Eulerian (ALE) method in LSDYNA to simulate the dynamic deployment of inflatable space structures is investigated. The CV and ALE methods were used to predict the inflation deployments of three folded tube configurations. The CV method was found to be a simple and computationally efficient method that may be adequate for modeling slow inflation deployment sine the inertia of the inflation gas can be neglected. The ALE method was found to be very computationally intensive since it involves the solving of three conservative equations of fluid as well as dealing with complex fluid structure interactions.
Atomic layer etching (ALE) of metals offers a path to greater control of selectivity, etch rate, and etch profile. However, tailoring a process to meet these demands requires an understanding of the volatile etch product, which is challenging to detect due to low product concentrations and the paramagnetism of many metalorganic products. Here, in this work, several complementary analysis techniques were used to determine the volatile etch product and residual surface species of a Cu ALE process based on plasma oxidation and formic acid vapor etching of the oxidized layer. It was shown that the volatile product was copper formate with a Cu 2+ paramagnetic center, in concordance with prior density functional theory (DFT) calculations. While copper formate tetrahydrate was formed, it was determined that the tetrahydrate product was confined to the surface, with anhydrous copper formate being the volatile etch product.
The growth of the information era economy is driving the pursuit of advanced materials for microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” paradigms. Atomically thin 2D materials, such as transition metal dichalcogenides (TMDCs), show great potential for next-generation microelectronics due to their properties and defect engineering capabilities. This perspective delves into atomic precision processing (APP) techniques like atomic layer deposition (ALD), epitaxy, atomic layer etching (ALE), and atomic precision advanced manufacturing (APAM) for the fabrication and modification of 2D materials, essential for future semiconductor devices. Additive APP methods like ALD and epitaxy provide precise control over composition, crystallinity, and thickness at the atomic scale, facilitating high-performance device integration. Subtractive APP techniques, such as ALE, focus on atomic-scale etching control for 2D material functionality and manufacturing. In APAM, modification techniques aim at atomic-scale defect control, offering tailored device functions and improved performance. Achieving optimal performance and energy efficiency in 2D material-based microelectronics requires a comprehensive approach encompassing fundamental understanding, process modeling, and high-throughput metrology. Finally, the outlook for APP in 2D materials is promising, with ongoing developments poised to impact manufacturing and fundamental materials science. Integration with advanced metrology and codesign frameworks will accelerate the realization of next-generation microelectronics enabled by 2D materials.
In this work, we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching (ALE), surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma–surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to ALE and more complex structures.
Yarrowia lipolytica , an oleaginous yeast, shows promise for industrial fermentation due to its robust acetyl-CoA flux and well-developed genetic engineering tools. However, its lack of an active xylose metabolism restricts the conversion of cellulosic sugars to valuable products. To address this, metabolic engineering, and adaptive laboratory evolution (ALE) were applied to the Y. lipolytica PO1f strain, resulting in an efficient xylose-assimilating strain (XEV). Whole-genome sequencing (WGS) of the XEV followed by reverse engineering revealed that the amplification of the heterologous oxidoreductase pathway and a mutation in the GTPase-activating protein gene (YALI0B12100g) might be the primary reasons for improved xylose assimilation in the XEV strain. When a sorghum hydrolysate was used, the XEV strain showed superior xylose consumption and lipid production compared to its parental strain (X123). This study advances our understanding of xylose metabolism in Y. lipolytica and proposes effective metabolic engineering strategies for optimizing lignocellulosic hydrolysates.
A major goal of this project is to significantly increase our understanding of isochoric heating of matter using laser produced proton beams, and the associated high energy density (HED) and warm dense matter (WDM) regimes generated. This will benefit research fields such as planetary science, fusion energy, plasma physics, and material science. For example, it will enhance our understanding of WDM properties of iron and silica under conditions encountered in planetary interiors and diagnostic components in fusion devices exposed to high fluxes of energetic plasma ions. The project is motivated by recent experiments that irradiated Si targets with proton beams generated by the 20 TW-laser at the SLAC MEC end-station. The HED/WDM states are probed using the 50 fs hard X-rays available in the 3rd harmonic of the LCLS. As part of this project, results from the phase contrast X-ray imaging, which shows the generation of compression waves that produces rear surface spallation, are compared with results from the 3D multi-physics multi- material code, PISALE, that combines Arbitrary Lagrangian-Eulerian (ALE) hydrodynamics with Adaptive Mesh Refinement (AMR). This comparison required modifications to several physics models in the PISALE (Pacific Island Structured-AMR with ALE) code. An important aspect of this project is the continued training of graduate students in HED physics and in conducting complex multiphysics simulations.
Lagrangian and Arbitrary Lagrangian Eulerian (ALE) hydrodynamics codes such as FLAG form the backbone of many mission-critical multi physics simulations at Los Alamos National Laboratory. Critical to pre forming high fidelity simulations with these codes are Lagrangian and ALE contact algorithms, which allow materials to collide, slide, and sep arate throughout a simulation.
This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.
A global three-dimensional chemical tracer model of the distribution and seasonal cycles of the surface concentration of CH3CCl3 is compared with surface observations from the Atmospheric Lifetime Experiment (ALE) for the years 1980-1985. Two-dimensional OH distributions calculated by a photochemical model are empirically adjusted from observed trends in the global average and the interhemispheric ratio of methyl chloroform. The effects of the recently discovered ocean sink for methyl chloroform were investigated. The model simulates the 5-year record of observations made at the five ALE sampling sites to generally within +/- 5 percent of the observed mean. The calculated average global lifetime of methyl chloroform is 5.7 +/- 0.3 years. The estimated global mean OH concentration is 6.5 +/- 0.4 x 10 exp 5/cu cm. However, the inclusion of the ocean sink does not significantly improve the simulation of the observed interhemispheric gradient of methyl chloroform. Atmospheric transport dominates the simulated CH3CCl3 seasonal cycle throughout the Northern Hemisphere but is less important in the Southern Hemisphere.
The threat of global warming, driven by rising carbon emissions, highlights the need to decarbonize our economy. This requires innovative solutions for managing carbon waste and its effective utilization. One promising method for CO2 capture and sequestration is the electrochemical reduction of CO2 to formic acid, a soluble C1 molecule that can be used to store carbon and energy, and as a feedstock for biological conversion. Cupriavidus necator H16, a soil bacterium capable of consuming and growing on formic acid as its sole carbon and energy source, is well positioned to upgrade CO2-derived formic acid into platform chemicals and fuel precursors. To improve the performance of C. necator on formic acid, adaptive laboratory evolution (ALE), a proven tool for improving microbial fitness, has been conducted using continuous pH-stat bioreactors. The system works on the basis that consumption of formic acid raises the pH and triggers the addition of more formic acid to maintain the pH (in this case 6.7), such that formic acid is provided at the same rate as it is consumed. This system has been coupled with level control to achieve continuous fermentation where cells acquiring mutations that improve growth on formic acid become more abundant in the population, from which they can be isolated and characterized. During developmental experiments it was discovered that formic acid accumulated to inhibitory levels. It was determined that the nitrogen source, ammonium hydroxide, must be tailored to the carbon consumption to avoid formic acid accumulation. The ALE ran in three lineages for approximately 3000 hours and more than 500 generations. Evolved isolates obtained from each lineage demonstrated an increase in growth rate in conjunction with improve formate utilization compared to the parental strain when evaluated in pH-stat bioreactors. The isolates with improved performance were then subjected to whole genome sequencing to identify potentially causative mutations. Mutations in several key genes across different lineages have been found and will be evaluated individually and in combination to identify those that improve growth on formic acid. Incorporating these mutations into production strains has the potential to greatly improve formic acid conversion and further industrial decarbonization.