Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “DEPTH”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

WE-EF-207-11: Energy and Depth Response of Thermoluminescent Dosimeters and Ionization Chambers in Water for Kilovoltage X-Ray Beams

Purpose: To assess the effects of changes in beam quality on detector response in the kilovoltage energy range by modulating the x-ray tube voltage and the measurement depth in water. Methods: Measurements were performed with TLD-100 and TLD-100H thermoluminescent dosimeters and an A12 farmer-type ionization chamber. To assess the energy response of the detectors, irradiations were performed at a depth of 3 cm in a custom-built thin-window water phantom using the moderately filtered x-ray beams at the UWADCL (20 kVp-250 kVp) and a Co-60 beam.The x-ray beams and detectors were modeled using the EGSnrc Monte Carlo code. The model was validated by simulating dose to the collecting volume of an A12 farmer chamber and comparing it with measured A12 signal as a function of depth. Dose was tallied to each detector and to water for comparison with measurements. Simulations were used to calculate the predicted energy response, which was compared to the measured response of each detector. Dose to each detector and dose to water as a function of depth were also simulated. Results: Detector output per dose to water was found to deviate by up to 15%, 20% and 30% as a function of energy relative to Co-60 for the A12, TLD-100H and TLD-100, respectively. The EGSnrc simulations produced results similar to the measurements for ionization chambers, but discrepancies of up to 30% were observed for TLD-100H. Simulated detector response as a function of depth was found to vary by up to 3%. Conclusion: These results suggest that changes in beam quality in kilovoltage x-ray beams can have a significant impact on detector response. In-water detector response was found to differ from the previously investigated in-air response. Deviations in detector response as a function of depth were less significant, but could potentially cause dosimetric errors if ignored.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico

Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collected at the Teller 27 Watershed and Kougarok 64 Hillslope during the 2021 - 2022 water year on the Seward Peninsula, Alaska using distributed temperature profiling (DTP) systems. We then applied this model to other sites where ground surface or shallow soil temperature data was available for at least one water year (see Related Datasets). Many of these temperature measurements were collocated with snow depth observations. Ground surface temperature (i.e. snow-ground interface temperature) is easy to measure using small, cheap and easy-to-deploy temperature sensors such as iButtons and TinyTags, and such measurements have previously been used to calculate a variety of snow metrics (e.g. snow onset date). However, this is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sites with snow depth predictions. No training data predictions are included in the *.csv file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

Time series mapping of snow volume in the mountains at global scales and at resolutions needed for water resource management is an unsolved challenge to date. Snow depth mapping by differencing surface elevations from airborne lidar is a mature measurement approach filling the observation gap operationally in a few regions, primarily in mountain headwaters in the Western United States. The same concept for snow depth retrieval from stereo- or multi-view photogrammetry has been demonstrated, but these previous studies had limited ability to determine the uncertainties of photogrammetric snow depth at the basin scale. For example, assessments used non-coincident or discrete points for reference, masked out vegetation, or compared a subset of the fully snow-covered study domain. Here, using a unique data set with simultaneously collected airborne data, we compare snow depth mapped from multi-view Structure from Motion photogrammetry to that mapped by lidar at multiple resolutions over an entire mountain basin (300 km 2 ). After excluding reconstruction errors (negative depths), SfM had lower snow-covered area (~27%) and snow volume (~16%) compared to lidar. The reconstruction errors were primarily in areas with vegetation, shallow snow (< 1 m), and steep slopes (> 60°C). Across the overlapping snow extent, snow depths compared well to lidar with similar mean values (< 0.03 m difference) and snow volume (± 5%) for output resolutions of 3 m and 50 m, and with a normalized median absolute deviation of 0.19 m. Our results indicate that photogrammetry from aerial images can be applied in the mountains but would perform best for deeper snowpacks above tree line.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

The Role of Thermodynamic Phase Shifts in Cloud Optical Depth Variations with Temperature

We present a novel method that identifies the contributions of thermodynamic phase shifts and processes governing supercooled liquid and ice clouds to cloud optical depth variations with temperature using MODIS observations. Our findings suggest that thermodynamic phase shifts outweigh the net influence of processes governing supercooled liquid and ice clouds in causing increases in mid-latitudinal cold cloud optical depth with temperature. Cloud regime analysis suggests that dynamical conditions appear to have less influence on the contribution of thermodynamic phase shifts to cloud optical depth variations with temperature. Thermodynamic phase shifts also contribute more to increases in cloud optical depth during colder seasons due to the enhanced optical thickness contrast between liquid and ice clouds. The results of this study highlight the importance of thermodynamic phase shifts in explaining cold cloud optical depth increases with temperature in the current climate and may elucidate their role in the cloud optical depth feedback.

cloud optical depth↗
DOE OSTI2021

Direct Observation of the Depth of Active Groundwater Circulation in an Alpine Watershed

Abstract The depth of active groundwater circulation is a fundamental control on stream flows and chemistry in mountain watersheds, yet it remains challenging to characterize and is rarely well constrained. We collected hydraulic conductivity, hydraulic head, temperature, chemical, noble gas, and 3 H/ 3 He groundwater age data from discrete levels in two boreholes 46 and 81 m deep in an alpine watershed, in combination with chemical and age data from shallow groundwater discharge, to discern groundwater flow rates at different depths and directly observe active and inactive groundwater. Vertical head gradients are steep (average of 0.4) and thermal profiles are consistent with typical linear conductive continental geotherms. Groundwater deeper than ∼20 m is distinct from shallow groundwater and creek water in its chemistry, noble gas signature, and age (dominantly >65 years compared to <9 years). Together these results suggest low vertical groundwater flow velocities and a relatively shallow active circulation depth of ∼20 m. This hypothesis is tested with a simple 2‐D numerical fluid flow and heat transport model representing a hillslope transect through the two boreholes. The modeling indicates that the subhorizontally bedded sedimentary rocks underlying the basin are highly anisotropic with low vertical hydraulic conductivity, and at most ∼10% of bedrock recharge (equivalent to <2% of stream baseflow) flows below a depth of 20 m. The study demonstrates the considerable value of discrete‐depth hydrogeologic, chemical, and age data for determining active circulation depth, and illustrates an approach for maximizing the utility of individual boreholes drilled for mountain bedrock aquifer characterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Updraft Width Modulates Ambient Atmospheric Controls on Convective Cloud Depth

Abstract The depth of convective clouds affects vertical transport of atmospheric constituents, influencing downstream weather and climate. Atmospheric controls on the maximum depth reached by moist convection are investigated with radar‐tracked convective cells tagged with sounding‐derived atmospheric parameters from a field campaign in central Argentina. Regression analyses show that narrow (<12‐km diameter) and wide (>16‐km diameter) cell depths respond to disparate factors, where cell areas are defined using composite reflectivity signatures. Undiluted lifted parcel indices including convective available potential energy (CAPE) and level of neutral buoyancy (LNB) are top predictors of wide cell maximum depth while mid‐tropospheric relative humidity is the top predictor of narrow cell maximum depth. Because narrow cells are more numerous than wide cells, the overall outcome of the full cell population does not strongly correlate with CAPE and LNB conditions. Tracked cells and atmospheric conditions in a simulation with 3‐km grid spacing covering the field campaign produce similar results to those observed. Narrow cells that are relatively deep have a cooler and moister mid‐troposphere with weaker free tropospheric subsidence, while relatively deep wide cells have much warmer and moister lower tropospheric conditions. These atmospheric differences are present 1 hr before cell initiation at both a fixed observing site and variable cell initiation locations. Simulated narrow cell maximum equivalent potential temperature decreases with height at a rate similar to the ambient vertical gradient, causing these cells to fall short of their LNB and supporting the view that entrainment‐driven dilution is a dominant control on their depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Nanoscale solid-state nuclear quadrupole resonance spectroscopy using depth-optimized nitrogen-vacancy ensembles in diamond

Nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR) spectroscopy of bulk quantum materials have provided insight into phenomena, such as quantum phase criticality, magnetism, and superconductivity. With the emergence of nanoscale 2D materials with magnetic phenomena, inductively detected NMR and NQR spectroscopy are not sensitive enough to detect the smaller number of spins in nanomaterials. The nitrogen-vacancy (NV) center in diamond has shown promise in bringing the analytic power of NMR and NQR spectroscopy to the nanoscale. However, due to depth-dependent formation efficiency of the defect centers, noise from surface spins, band bending effects, and the depth dependence of the nuclear magnetic field, there is ambiguity regarding the ideal NV depth for surface NMR of statistically polarized spins. In this work, we prepared a range of shallow NV ensemble layer depths and determined the ideal NV depth by performing NMR spectroscopy on statistically polarized 19 F in Fomblin oil on the diamond surface. We found that the measurement time needed to achieve a signal-to-noise ratio of 3 using XY8-N noise spectroscopy has a minimum at an NV ensemble depth of 5.5 ± 1.5 nm for ensembles activated from 100 ppm nitrogen concentration. To demonstrate the sensing capabilities of NV ensembles, we perform NQR spectroscopy on the 11 B of hexagonal boron nitride flakes. We compare our best diamond to previous work with a single NV and find that this ensemble provides a shorter measurement time with excitation diameters as small as 4 μm. Furthermore, this analysis provides ideal conditions for further experiments involving NMR/NQR spectroscopy of 2D materials with magnetic properties.

2D materials↗
DOE OSTI2018

Establishing Outcrop Time Series at Various Depths in a Nonlinear Soil Column: An Iterative Approach

Seismic soil-structure interaction (SSI) analysis of nuclear facilities is an important consideration during design and retrofit. SSI tools used in the nuclear industry are currently based on an equivalent linear approach. Procedures for developing input ground motion for equivalent linear approaches are well established. However, the procedures for establishing input ground motion for Nonlinear Soil-Structure Interaction (NLSSI) analysis of nuclear facilities is not well established. A collaborative research group at Idaho National Laboratory (INL) has recently developed analytical methods and numerical tools for using NLSSI analysis for nuclear facility seismic calculations. NLSSI analysis for a nuclear facility allows for calculation of seismic wave motion through a near field soil domain using either, (1) vertically propagating shear and compressive waves, which is the current industry practice, or (2) a three-dimensional non-vertical wave field. This paper presents an iterative procedure for establishing outcrop motion at a depth in the soil column for NLSSI analysis that uses vertically propagating shear waves. The approach presented in this paper starts with a known ground motion at the surface, deconvolves to a depth, and then convolves the obtained motion up to a different desired location of input for the NLSSI model. To demonstrate the validity of the approach a finite element soil column, that is representative of a nuclear facility site in the US, is used to produce compatible outcrop seismic time series for reduced nonlinear soil mesh depths. The developed approach for reducing the nonlinear soil column model depth is a two-step iterative method; 1) the first step is establishing an outcrop time series at the lowest depth considered that produces the top-of-soil response spectrum of an actual recorded ground motion, and 2) the second step is providing compatible outcrop time series at a shallower depth based on the information from the first step. The comparison of the 5% damped response spectrum from the resulting acceleration time series based on the iterated outcrop motions and the original acceleration time series are conducted. The study showed that the proposed iterative approach produced comparable results within 1% range of the original recorded time series results when sufficient iterations were performed.

58 GEOSCIENCES↗
DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

The microlensing event rate and optical depth toward the galactic bulge from MOA-II

We present measurements of the microlensing optical depth and event rate toward the Galactic Bulge (GB) based on two years of the MOA-II survey. This sample contains ∼1000 microlensing events, with an Einstein radius crossing time of t {sub E} ≤ 200 days in 22 bulge fields covering ∼42 deg{sup 2} between –5° < l < 10° and –7° < b < –1°. Our event rate and optical depth analysis uses 474 events with well-defined microlensing parameters. In the central fields with |l| < 5°, we find an event rate of Γ = [2.39 ± 1.1]e {sup [0.60±0.05](3–|b|)} × 10{sup –5} star{sup –1} yr{sup –1} and an optical depth (for events with t {sub E} ≤ 200 days) of τ{sub 200} = [2.35 ± 0.18]e {sup [0.51±0.07](3–|b|)} × 10{sup –6} for the 427 events, using all sources brighter than I{sub s} ≤ 20 mag. The distribution of observed fields is centered at (l, b) = (0.°38, –3.°72). We find that the event rate is maximized at low latitudes and a longitude of l ≈ 1°. For the 111 events in 3.2 deg{sup 2} of the central GB at |b| ≤ 3.°0 and 0.°0 ≤ l ≤ 2.°0, centered at (l, b) = (0.°97, –2.°26), we find Γ=4.57{sub −0.46}{sup +0.51}×10{sup −5} star{sup –1} yr{sup –1} and τ{sub 200}=3.64{sub −0.45}{sup +0.51}×10{sup −6}. We also consider a red clump giant (RCG) star sample with I{sub s} < 17.5, and we find that the event rate for the RCG sample is slightly lower than but consistent with the all-source event rate. The main difference is the lack of long duration events in the RCG sample due to a known selection effect. Our results are consistent with previous optical depth measurements, but they are somewhat lower than previous all-source measurements, and slightly higher than previous RCG optical depth measurements. This suggests that the previously observed difference in optical depth measurements between all-source and RCG samples may largely be due to statistical fluctuations. These event rate measurements toward the central GB are necessary to predict the microlensing event rate and to optimize the survey fields in future space missions such as Wide Field Infrared Space Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Accelerated Depth Computation for Surface Boxplots with Deep Learning

Functional depth is a well-known technique used to derive descriptive statistics (e.g., median, quartiles, and outliers) for 1D data. Surface boxplots extend this concept to ensembles of images, helping scientists and users identify representative and outlier images. However, the computational time for surface boxplots increases cubically with the number of ensemble members, making it impractical for integration into visualization tools. In this paper, we propose a deep-learning solution for efficient depth prediction and computation of surface boxplots for time-varying ensemble data. Our deep learning framework accurately predicts member depths in a surface boxplot, achieving average speedups of 6X on a CPU and 15X on a GPU for the 2D Red Sea dataset with 50 ensemble members compared to the traditional depth computation algorithm. Our approach achieves at least a 99% level of rank preservation, with order flipping occurring only at pairs with extremely similar depth values that pose no statistical differences. This local flipping does not significantly impact the overall depth order of the ensemble members.

Han, Mengjiao↗
DOE OSTI2013

Transcriptomic analysis of metabolic function in the giant kelp, Macrocystis pyrifera , across depth and season

To increase knowledge of transcript diversity for the giant kelp, Macrocystis pyrifera, and assess gene expression across naturally occurring depth gradients in light, temperature and nutrients, we sequenced four cDNA libraries created from blades collected at the sea surface and at 18 m depth during the winter and summer. Comparative genomics cluster analyses revealed novel gene families (clusters) in existing brown alga expressed sequence tag data compared with other related algal groups, a pattern also seen with the addition of M. pyrifera sequences. Assembly of 228 Mbp of sequence generated c. 9000 isotigs and c. 12 000 open reading frames. Annotations were assigned using families of hidden Markov models for c. 11% of open reading frames; M. pyrifera had highest similarity to other members of the Phaeophyceae, namely Ectocarpus siliculosus and Laminaria digitata. Quantitative polymerase chain reaction of transcript targets verified depth-related differences in gene expression; stress response and light-harvesting transcripts, especially members of the LI818 (also known as LHCSR) family, showed high expression in the surface compared with 18 m depth, while some nitrogen acquisition transcripts (e.g. nitrite reductase) were upregulated at depth compared with the surface, supporting a conceptual biological model of depth-dependent physiology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Effects of reflector and crystal surface on the performance of a depth-encoding PET detector with dual-ended readout

Purpose: Depth encoding detectors are required to improve the spatial resolution and spatial resolution uniformity of small animal positron emission tomography (PET) scanners, as well as dedicated breast and brain scanners. Depth of interaction (DOI) can be measured by using dual-ended readout of lutetium oxyorthosilicate (LSO) scintillator arrays with position-sensitive avalanche photodiodes. Inter-crystal reflectors and crystal surface treatments play important roles in determining the performance of dual-ended detectors. In this paper, the authors evaluated five LSO arrays made with three different intercrystal reflectors and with either polished or unpolished crystal surfaces. Methods: The crystal size in all arrays was 1.5 mm, which is typical of the detector size used in small animal and dedicated breast scanners. The LSO arrays were measured with dual-ended readout and were compared in terms of flood histogram, energy resolution, and DOI resolution performance. Results: The four arrays using enhanced specular reflector (ESR) and Toray reflector provided similar quality flood histograms and the array using Crystal Wrap reflector gave the worst flood histogram. The two arrays using ESR reflector provided the best energy resolution and the array using Crystal Wrap reflector yielded the worst energy resolution. All arrays except the polished ESR array provided good DOI resolution ranging from 1.9 mm to 2.9 mm. DOI resolution improved as the gradient in light collection efficiency with depth (GLCED) increased. The geometric mean energies were also calculated for these dual-ended readout detectors as an alternative to the conventional summed total energy. It was shown that the geometric mean energy is advantageous in that it provides more uniform photopeak amplitude at different depths for arrays with high GLCED, and is beneficial in event selection by allowing a fixed energy window independent of depth. A new method of DOI calculation that improved the linearity of DOI ratio vs depth and simplifies the DOI calibration procedure also was developed and tested. Conclusions: The results of these studies provide useful guidance in selecting the proper reflectors and crystal surface treatments when LSO arrays are used for high-resolution PET applications in small animal scanners or dedicated breast and brain scanners.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-I-11: Cascaded Linear System Model for Columnar CsI Flat Panel Imagers with Depth Dependent Gain and Blur

Purpose: To implement a depth dependent gain and blur cascaded linear system model (CLSM) for optimizing columnar structured CsI indirect conversion flat panel imager (FPI) for advanced imaging applications. Methods: For experimental validation, depth dependent escape efficiency, e(z), was extracted from PHS measurement of different CsI scintillators (thickness, substrate and light output). The inherent MTF and DQE of CsI was measured using high resolution CMOS sensor. For CLSM, e(z) and the depth dependent MTF(f,z), were estimated using Monte Carlo simulation (Geant4) of optical photon transport through columnar CsI. Previous work showed that Monte Carlo simulation for CsI was hindered by the non-ideality of its columnar structure. In the present work we allowed variation in columnar width with depth, and assumed diffusive reflective backing and columns. Monte Carlo simulation was performed using an optical point source placed at different depth of the CsI layer, from which MTF(z,f) and e(z) were computed. The resulting e(z) with excellent matching with experimental measurements were then applied to the CLSM, Monte Carlo simulation was repeated until the modeled MTF, DQE(f) also match experimental measurement. Results: For a 150 micron FOS HL type CsI, e(z) varies between 0.56 to 0.45, and the MTF at 14 cycles/mm varies between 62.1% to 3.9%, from the front to the back of the scintillator. The overall MTF and DQE(f) at all frequencies are in excellent agreement with experimental measurements at all frequencies. Conclusion: We have developed a CLSM for columnar CsI scintillators with depth dependent gain and MTF, which were estimated from Monte Carlo simulation with novel optical simulation settings. Preliminary results showed excellent agreement between simulation results and experimental measurements. Future work is aimed at extending this approach to optimize CsI screen optic design and sensor structure for achieving higher DQE(f) in cone-beam CT, which uses high kVp.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Causal Pathways for Temperature Predictability from Snow Depth

Dynamical subseasonal-to-seasonal (S2S) weather forecasting has made strides in recent years, thanks partly to better initialization and representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced temperature predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is examined via two causal pathways. Through the first pathway, snow depth anomalies in month 1 persist into month 2 and are then linked to temperature anomalies through snow–temperature feedback mechanisms. The first pathway is active from fall to summer, and its effect peaks before the melting season: in winter in the low latitudes, in spring in the midlatitudes, and in early summer in the high latitudes. The second pathway, where snow depth anomalies in month 1 lead to soil moisture anomalies in month 2 (through melting), which are then linked to temperature anomalies in month 2 through soil moisture–temperature feedbacks, is most active in spring and summer. The effect of the second pathway peaks during the melting season, namely, later in the year than the first pathway. The latitudes of the highest mediated effect through both pathways follow a seasonal cycle, shifting northward along with the seasonal insolation cycle. Finally, in keeping with this seasonal cycle, the highest snow depth mediation occurs to the north and the highest soil moisture mediation to the south of the latitudes with the highest overall temperature predictability from snow depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Any Clifford+T circuit can be controlled with constant T-depth overhead

Since an n-qubit circuit consisting of CNOT gates can have up to $Ω(n^2/\log{n})$ CNOT gates, it is natural to expect that $Ω(n^2/\log{n})$ Toffoli gates are needed to apply a controlled version of such a circuit. We show that the Toffoli count can be reduced to at most n. The Toffoli depth can also be reduced to O(1), at the cost of 2n Toffoli gates, even without using any ancilla or measurement. In fact, using a measurement-based uncomputation, the Toffoli depth can be further reduced to 1. From this, we give two corollaries: any controlled Clifford circuit can be implemented with O(1) T-depth, and any Clifford+T circuit with T-depth D can be controlled with T-depth O(D), even without ancillas. As an application, we show how to catalyze a rotation by any angle up to precision $ε$ in T-depth exactly 1 using a universal $\lceil\log_2(8/ε)\rceil$-qubit catalyst state.

FOS: Physical sciences↗
DOE OSTI2025

From the top: surface-derived carbon fuels greenhouse gas production at depth in a peatland

Tropical peatlands play an important role in global carbon (C) cycling, but little is known about factors driving carbon dioxide (CO 2 ) and methane (CH 4 ) emissions from these ecosystems, especially production in deeper soils. This study aimed to identify source material and processes regulating C emissions originating deep in three sites in a peatland on the Caribbean coast of Panama. We hypothesized that (1) surface-derived organic matter transported down the soil profile is the primary C source for respiration products at depth and that (2) high lignin content results in hydrogenotrophic methanogenesis as the dominant CH 4 production pathway throughout the profile. We used radiocarbon isotopic values to determine whether CO 2 and CH 4 at depth are produced from modern substrates or ancient deep peat, and we used stable C isotopes to identify the dominant CH 4 production pathway. Peat organic chemistry was characterized using 13 C solid-state nuclear magnetic resonance spectroscopy ( 13 C-NMR). We found that deep peat respiration products had radiocarbon signatures that were more similar to surface dissolved organic C (DOC) than deep solid peat. These results indicate that surface-derived organic matter was the dominant source for gas production at depth in this peatland, likely because of vertical transport of DOC from the surface to depth. Lignin, which was the most abundant compound (55 %–70 % of C), increased with depth across these sites, whereas other C compounds like carbohydrates did not vary with depth. These results suggest that there is no preferential decomposition of carbohydrates but instead preferential retention of lignin. Stable isotope signatures of respiration products indicated that hydrogenotrophic rather than acetoclastic methanogenesis was the dominant production pathway of CH 4 throughout the peat profile. These results show that deep C in tropical peatlands does not contribute greatly to surface fluxes of carbon dioxide, with compounds like lignin preferentially retained. This protection of deep C helps explain how peatland C is retained over thousands of years and points to the vulnerability of this C should anaerobic conditions in these wet ecosystems change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets

This repository contains simulated outputs from superwell – a hydro-economic tool for long-term assessment of groundwater cost and supply – providing globally gridded groundwater extractable volumes and associated unit costs ($/km³) for accessible groundwater production, based on a variety of user-defined depletion and ponded depth scenarios. Key model documentation: Niazi, H., Ferencz, S. B., Graham, N. T., Yoon, J., Wild, T. B., Hejazi, M., Watson, D. J., & Vernon, C. R. (2025). Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1. Geoscientific Model Development, 18(5), 1737-1767. https://doi.org/10.5194/gmd-18-1737-2025 Find the source code of the superwell model on GitHub: https://github.com/JGCRI/superwell Repository Overview Main output: superwell_outputs.7z contains 6 files (4.5 GB) named as superwell_py_deep_all_0.*PD_0.*DL.csv. These files present superwell outputs of global groundwater extraction volumes and cost estimates on a 0.5° scale for six scenarios with different Ponded Depth (PD; 0.3 and 0.6 m) and Depletion Limit (DL; 5%, 25%, and 40% of available volume) targets over the entire pumping lifetime of a grid cell superwell_py_deep_all_0.3PD_0.25DL_sample_100.csv contains superwell outputs for 100 data points sampled to match the global inputs' distribution superwell_py_deep_all_0.3PD_0.25DL_Grid_72548.csv contains superwell output for a single grid cell concept_v5.png provides an overview of the superwell workflow Outputs Description year_number: year of pumping depletion_limit: set depletion limit (DL) as a volume fraction of total available groundwater Mappings: continent, country, gcam_basin_id, Basin_long_name, grid_id: geographic identifiers and basin information Inputs: grid_area (km²): area of the grid cell whyclass: hydrogeological classification of the aquifer permeability (m/day), porosity (%), total_thickness (m), depth_to_water (m): aquifer properties. The geo-processed input data has been published separately: https://doi.org/10.57931/2307831 Model outputs: orig_aqfr_sat_thickness (m), aqfr_sat_thickness (m): original and remaining/instantaneous saturated thickness of the aquifer hydraulic_conductivity (m/day), transmissivity (m²/day): hydraulic properties of the aquifer radius_of_influence (m), areal_extent (km²): well radius and area of influence from the center of the well number_of_wells (-): number of wells in a grid cell determined by a ratio of well area and grid area max_drawdown (m), drawdown (m), drawdown_interference (m): well and aquifer drawdown during extraction total_head (m): total lift for the groundwater (depth to water plus drawdown) total_well_length (m): total depth of wells drilled well_yield (m³/day): pumping rate or well yield power (kW), energy (kWh): power and energy required for pumping groundwater Volume Outputs: volume_produced_perwell (m³), cumulative_vol_produced_perwell (m³): production volume metrics per well volume_produced_allwells (m³), cumulative_vol_produced_allwells (m³): aggregate extraction volumes for all wells in a grid cell available_volume (m³): available groundwater in storage for the grid cell as determined by aquifer properties depleted_vol_fraction: fraction of total volume pumped over available volumes in a grid cell (same as depletion limit) Cost Outputs: well_installation_cost ($): well installation cost based on the hydrogeological complexity of the aquifer annual_capital_cost, maintenance_cost, nonenergy_cost ($): nonenergy costs energy_cost_rate ($/kWh): electricity rate energy_cost ($): energy cost of pumping groundwater total_cost_perwell ($), total_cost_allwells ($): total annual energy and non-energy cost for each and all wells in a grid cell a unit_cost ($/m³), unit_cost_per_km3 ($/km³), unit_cost_per_acreft ($/acre-ft): total cost of pumping a unit of groundwater, indicated for different spatial units Key Resources Model documentation: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. Input data: Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data repository. https://doi.org/10.57931/2307831 superwell source code: https://github.com/JGCRI/superwell Cite as Niazi, H., Ferencz, S., Yoon, J., Graham, N., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024). Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets. MSD-LIVE Data repository. https://doi.org/10.57931/2307832 Contact Reach out to Hassan Niazi or Stephen Ferencz or open an issue in the superwell repository for questions or suggestions.

Earth Systems↗