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At least 865 records · Page 48
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Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗
DOE OSTI2020

Panel Session 98B: Treatment of Low-Activity Tank Waste at Hanford: Considering the Alternatives

This panel focused on alternative treatment of the Hanford Low-Activity Tank Waste. Currently, Tank Waste at the Hanford Site is to be separated into low-activity (LAW) and high-level waste (HLW) fractions, and then vitrified to produce immobilized LAW glass for onsite disposal, and HLW glass for ultimate geologic disposal. This panel discussed several commissioned studies by the National Laboratories, National Academies, and General Accountability Office to evaluate the risks, costs, and benefits associated with considering options for immobilizing portions of the Hanford LAW waste forms. Panelists with presentations: Analysis of Supplemental Treatment Approaches for Low-Activity Waste at the Hanford Nuclear Reservation Overview and Conclusions (Michael Stone); Analysis of Supplemental Treatment Approaches for Low-Activity Waste at the Hanford Nuclear Reservation: Disposal Performance Evaluation (Tom Brouns); National Academies Review of the Study on Supplemental Treatment Approaches of Low-Activity Waste at the Hanford Nuclear Reservation (Charles Ferguson); GAO Observations on Immobilizing Low-Activity Waste at DoE's Hanford Site (David Trimble)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Development of a Regulatory Strategy for Post Operational Clean Out Activities at Sellafield Limited Nuclear Licensed Site - 20273

The Thermal Oxide Reprocessing Plant (THORP) at the Sellafield nuclear licensed site carried out its final commercial shear of spent nuclear fuel in November 2018, since when the facility has undergone rundown prior to cessation of operations. The Magnox Reprocessing Facility (MRF) is also due to cease operating, around the end of 2020. Following the cessation of spent fuel reprocessing, the facilities and supporting infrastructure will transition into Post Operational Clean Out (POCO), which will become an increasingly significant portion of Sellafield Limited's (SL) activities. POCO is defined as the set of activities undertaken directly after a nuclear facility comes to the end of its operational life, in order to place the plant in a suitable state for the subsequent decommissioning steps. The decommissioning strategy at SL is for deferred dismantling of these facilities, which will be subject to a period of Surveillance and Maintenance (S and M) following completion of POCO. This paper outlines the development and implementation of a regulatory strategy for POCO activities at the Sellafield site by the Office for Nuclear Regulation (ONR), the independent nuclear safety, transport and security regulator in the United Kingdom. The paper also describes how this regulatory strategy is applied to nuclear installations undergoing transition into POCO and subsequent decommissioning. ONR works jointly with the Environment Agency, as the environmental regulators in England, to ensure that matters relevant to them have been duly considered. In developing a regulatory strategy, ONR has taken a site-wide approach, moving away from regulation on a facility-by-facility basis, to promote consistency, as well as maximising efficiency and effectiveness. The aim of this integrated approach was to prevent complications during future decommissioning and dismantling that have been experienced previously at similar facilities in France. The strategy recognised that once reprocessing operations are concluded, the hazard present will be reduced significantly, which provides the opportunity for ONR to consider how best to deliver a proportionate approach to regulatory interventions. Once POCO has been completed and the facility has transitioned into a period of S and M, the scale and frequency of ONR's interventions will reduce, commensurate with the safety case covering the remaining hazard and activities associated with maintaining the facility prior to final decommissioning. This approach allows ONR to consider risks holistically, considering the full life cycle of the facility and by taking due account of the interdependencies between facilities across the site. The strategy also aligns with existing regulation of high hazard and risk reduction activities, which remain ONR's top priority. As SL progresses with POCO activities in THORP and across the rest of the site, ONR will review its regulatory strategy to ensure that the intended safety benefits are realised. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Hazard-Based Model of Activity Generation Using Vehicle Trajectory Data

Travel behavior modeling suffers from lack of longitudinal data due to the high cost of procurement and difficulty with ensuring data quality. In the recent years ubiquitous data intercepted from social media, cell detail record, GPS traces are increasingly being explored to analyze human mobility. However, these data lack key demographic information which limits their use for developing causal models of traveler behavior. The primary contribution of the paper lies in using vehicle trajectory data from Ann Arbor along with multiple other data sources for developing a hazard-based activity generation model. The different category of data used in the analysis include (i) vehicle trajectory data (VTD), (ii) American Census Survey (ACS), (iii) land use data, (iv) household travel survey (HTS) data, and (v) smart location data from Environmental Protection Agency (EPA). Home and work are identified as the two most frequently visited locations from the VTD and validated against land use categories of the region. Next a random utility-based model is developed using HTS data to identify other activity purposes as a function of the polynomials of distance from home, activity duration and departure time. The developed model is then applied to identify the non-home activity purposes in the VTD. Demographic and accessibility information are appended to the VTD from ACS and smart location databases respectively. The fmal database is then used to develop a hazard-based activity generation model. (C) 2020 The Authors. Published by Elsevier B.V.

activity generation↗
DOE OSTI2023

Design, Optimization, and Validation of GaN-Based DAB Converter for Active Cell Balancing in BTMS Applications

This paper focuses on the design of a bidirectional dual active bridge (DAB) DC/DC converter that utilizes Gallium Nitride (GaN) switches as active components. In the existing literature, MOSFET-based DAB for active cell balancing is available, but GaN-based DAB converter for active cell balancing is still new. The proposed modular isolated GaN-based DAB converter is designed as an individual module of active cell balancing for behind-the-meter storage (BTMS) applications, targeting high-power charging stations. Modular isolated converters are connected to each cell (low voltage bus), and each cell is connected in series to build up a battery module. According to the reference current command of supervisory control, each DAB converter can transfer power back and forth through the high voltage (HV) bus to balance the State of Charge (SoC) between the cells. Each module DAB converter is designed at a 50 W power rating. Switch power and transformer losses are analyzed for different switching frequencies, showing the optimum switching frequency for minimum losses. Furthermore, the procedure to select the required gate driver and the PCB layout optimization are discussed. Finally, the DAB performance analysis of GaN-based DAB and Si-based DAB is provided for a battery module operating with a LiFeMnPO4 prismatic cell with 3.2V 20Ah rated values.

active cell balancing↗
DOE OSTI2023

Design, Optimization, and Validation of GaN-Based DAB Converter for Active Cell Balancing in BTMS Applications: Preprint

This paper focuses on the design of a bidirectional dual active bridge (DAB) DC/DC converter that utilizes Gallium Nitride (GaN) switches as active components. In the existing literature, MOSFET-based DAB for active cell balancing is available, but GaN-based DAB converter for active cell balancing is still new. The proposed modular isolated GaN-based DAB converter is designed as an individual module of active cell balancing for behind-the-meter storage (BTMS) applications, targeting high-power charging stations. Modular isolated converters are connected to each cell (low voltage bus), and each cell is connected in series to build up a battery module. According to the reference current command of supervisory control, each DAB converter can transfer power back and forth through the high voltage (HV) bus to balance the State of Charge (SoC) between the cells. Each module DAB converter is designed at a 50W power rating. Switch power and transformer losses are analyzed for different switching frequencies, showing the optimum switching frequency for minimum losses. Furthermore, the procedure to select the required gate driver and the PCB layout optimization are discussed. Finally, the DAB performance analysis of GaNbased DAB and Si-based DAB is provided for a battery module operating with a LiFeMnPO4 prismatic cell with 3.2V 20Ah rated values.

active cell balancing↗
DOE OSTI2021

Modification of the Coordination Environment of Active Sites on MoC for High-Efficiency CH 4 Production

Modulating the coordination environment of active sites on catalyst surfaces is crucial to developing effective catalysts and controlling catalysis, but it is highly challenging. Guided by our first-principles calculations, we experimentally accomplish the modification of the coordination environment of active sites on MoC nanoparticle surfaces by anchoring pyridinic N atom rings of holey graphene on Mo atoms. The rings produce electrostatic forces that enable the tuning of the Mo sites' affinity to reaction intermediates, which passivates Mo hollow sites, activates Mo top sites and reduces the over-adsorption of OH on the Mo active sites, as predicted by the calculations. The atomic-level modification is well confirmed by atomic-resolution imaging, high-resolution electron tomography, synchrotron soft X-ray spectroscopy and operando electrochemical infrared spectroscopy. Consequently, the Faradaic efficiency for CO 2 reduction to CH 4 is enhanced from 16% to 89%, a record high efficiency so far, in aqueous electrolyte. Meantime, it also exhibits a negligible activity loss over 50 h.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p‐tau, synaptic peptides, and physical activity

INTRODUCTION: In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies. METHODS: As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity. RESULTS: Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology. DISCUSSION: Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment. Highlights: Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

active reactance↗
DOE OSTI2020

Structure and activity of supported bimetallic NiPd nanoparticles: influence of preparation method on CO 2 reduction

Here, bimetallic Ni-Pd and monometallic reference catalysts were prepared by decomposing organometallic precursors, Ni(cod) 2 and Pd 2 (dba) 3 , leading to nanoparticles with sizes ranging from 3 to 6 nm. Two different synthesis procedures were followed: i) solution synthesis using capping ligand (hexadecylamine) followed by impregnation of pre-formed nanoparticles on SiO 2 , called Sol-immobilization (SI); and 2) direct precursor decomposition onto SiO 2 , without stabilizer, called Direct Decomposition (DD). Samples prepared by SI procedure are alloyed bimetallic nanoparticles, whereas samples obtained by DD one show phase segregation. Interestingly, DD samples show better activity for CO 2 hydrogenation into CO (reverse water-gas shift reaction - RWGS) than SI ones. The best compromise between activity for CO 2 activation (at lower temperature) and CO selectivity was achieved with Ni DD and NiPd DD catalysts. Moreover, the addition of palladium increased the concentration of surface undercoordinated sites, which chemisorb CO weakly, thus improving activity and selectivity, in opposition to other samples that chemisorb CO strongly, in multiband configuration. In the presence of Pd, different decomposition rates drive the formation of smaller and more active Ni clusters. The knowledge acquired here on the influence of synthesis conditions on the catalytic properties of Ni-Pd catalysts should guide us to better catalysts for CO 2 transformations into valuable products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Abstract Graph learning, when used as a semi-supervised learning (SSL) method, performs well for classification tasks with a low label rate. We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi- or hyperspectral image segmentation. Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification. This work builds on recent advances in the design of novel active learning acquisition functions (e.g., the Model Change approach in arXiv:2110.07739) while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods. In addition to improvements in the accuracy, our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enhanced visible-light-assisted peroxymonosulfate activation on cobalt-doped mesoporous iron oxide for orange II degradation

Sulfate radical-based advanced oxidation processes (SR-AOP) attracted a lot of attention due to their capability and adaptability in wastewater treatment. Although heterogeneous cobalt-based catalysts have been extensively studied and have emerged superior for peroxymonosulfate (PMS) activation, leaching of cobalt is still a major concern. Herein, we report a photo-assisted PMS activation approach using cobalt doped mesoporous iron oxide with exceptional activity and stability up to 7 cycles with very low cobalt leaching (0.5 ppm). The highest activity was obtained by 1 mol % cobalt doped mesoporous iron oxide. Orange II dye was completely degraded by the reactive oxygen species (ROS) generated under visible light irradiation within 1 min. ROS were evaluated using two distinct techniques (Fluorescence probes and Electron Paramagnetic Resonance (EPR). ROS quenching experiments were also used to highlight the dominant pathway. This study correlates the structural and electronic changes induced with cobalt doping to the exceptional activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Recent advances in biological activities of lignin and emerging biomedical applications: A short review

As an abundant biopolymer, lignin gains interest owing to its renewable nature and polyphenolic structure. It possesses many biological activities such as antioxidant activity, antimicrobial activity, and biocompatibility. Studies are being carried out to relate the biological activities to the polyphenolic structures. These traits present lignin as a natural compound being used in biomedical field. Lignin nanoparticles (LNPs) are being investigated for safe use in drug and gene delivery, and lignin-based hydrogels are being explored as wound dressing materials, in tissue engineering and 3D printing. In addition, lignin and its derivatives have shown the potential to treat diabetic disease. This review summarizes latest research results on the biological activities of lignin and highlights potential applications exampled by selective studies. It helps to transform lignin from a waste material into valuable materials and products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Selective activation of methane C–H bond in the presence of methanol

Direct methane to methanol (MTM) conversion over heterogeneous catalysts is a promising route for valorization of methane. The methane C-H bond activation is considered as the key step of the MTM and is the focus of considerable research activity. However, the formed methanol typically suffers from overoxidation largely due to the cleavage of a methanol C-H bond, whose bond dissociation energy is ca. 0.5 eV lower than that of the methane C-H bond, which usually translates to a transition state energy of the methanol C-H bond cleavage that is ca. 0.55 eV lower than that of methane whenever the reactions proceed through a radical mechanism. Furthermore, we propose a general approach for decreasing the transition state energy difference between the CH 4 and CH 3 OH C-H bond dissociation. When a metal-oxide supported cationic transition metal atom and a neighboring oxygen on the oxide surface serve as the active site, the transition state energy difference through a surface-stabilized pathway can be noticeably narrowed as compared with that of a radical pathway. For Ir, Pt and Rh-doped anatase TiO 2 (101), the CH 4 C-H bond activation can be preferred over that of CH 3 OH at significant methanol mole fraction. Also, for PdAu alloys containing adsorbed oxygen and positively charged Pd, calculations suggest in agreement with recent experiments (Science 367 (6474), 193-197) that the CH 4 C-H bond can be selectively activated in the presence of CH 3 OH.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Insights into the electronic origin of enhancing the catalytic activity of Co 3 O 4 for oxygen evolution by single atom ruthenium

The surface electronic structure of transition-metal oxide catalysts plays a decisive role in binding the intermediates of the oxygen evolution reaction (OER) to the oxide surface, in turn influencing the catalytic activity of these materials. However, the approaches to modulating the electronic structure of surface metal ions are rare and far behind the demands. Here, we report a surface single atom decoration for adjusting the surface electronic structure of Co 3 O 4 , leading to enhanced electrocatalytic activity for OER, in which the isolated Ru single atoms were uniformly deposited on the surface of Co 3 O 4 by an atomic layer deposition technology. As the OER catalyst, the as-made catalysts have exhibited a significantly enhanced catalytic activity (with increasing to 95.5 times) and a dramatically decreased overpotential. The density functional theory calculations reveal that the single-atom Ru acts as a promotor to adjust the 3d electronic structure of adjacent Co atoms and to tune the binding energy between intermediates and activity sites, finally leading to enhanced catalytic activity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Distinguishing fissile uranium isotopes using an active well neutron coincidence counter

Proposed thorium-based nuclear fuel cycles are likely to require quantification and verification of 233 U within nuclear material. Because of their similar fission cross sections, active neutron nondestructive assay (NDA) systems may respond similarly to 233 U and 235 U. Traditional safeguards equipment has been optimized for 235 U and 238 U quantification associated with conventional uranium/plutonium fuel cycles and may not be directly applicable to 233 U quantification when mixed with other actinides. This work used models of the large volume active well coincidence counter (LV-AWCC) at Oak Ridge National Laboratory to evaluate the performance of this neutron NDA system to differentiate fissile uranium isotopes. The models were developed to simulate NDA system performance in response to a number of triangular radiation signature training device sources within the central cavity or well. This work predicted that the LV-AWCC can effectively differentiate 233 U from 235 U in certain modes of operation. In active mode, the LV-AWCC with the cadmium liner results in different doubles count rates between the fissile isotopes for a given fissile uranium mass. Without the cadmium liner, the uranium isotopes provide a statistically indistinguishable doubles count rate response for the fissile masses considered in this work (up to approximately 150 g). The cadmium liner serves to harden the neutron interrogation spectrum, which better exploits the notable difference in the 233 U and 235 U fission cross sections at approximately 1 eV. In passive mode, the two fissile isotopes exhibit different doubles and singles count rates regardless of liner presence because the passive source strength of 233 U is approximately 2 orders of magnitude stronger than that of 235 U due to the shorter half-life and correspondingly higher (α, n) yield. We conclude that using neutron interrogation in the LV-AWCC, two measurements are needed to quantify 233 U content in mixed uranium items. The first measurement is used to determine the total fissile uranium mass using a mode that cannot distinguish fissile isotopes (i.e., where a similar response is observed for both fissile uranium isotopes such as active doubles without cadmium or using a thermal neutron interrogation source). In conclusion, the second measurement is used to determine the 233 U content by using a differentiating technique (e.g., passive doubles, passive doubles to singles ratio, active doubles with cadmium).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A data analytical approach for assessing the efficacy of Operational Technology active defenses against insider threats

In recent years, the need for Operational Technology (OT) defenses has been recognized, serving as an additional line of defense when Information Technology (IT) defenses are bypassed. This is no longer considered an uncommon possibility when dealing with advanced persistent threat (APT) actors expected to be state-sponsored and receiving insider assistance. In these extreme adversarial situations, OT defenses aim to provide another layer of defense for the system, introduced directly at the physical process level, as described by the sensors data, the system model, and control actions. Just like IT defenses, two schools of thought, i.e., passive and active defenses, have emerged to address this challenge. In active defenses, representing the focus of this paper, known signatures, synthesized based on the system's unique characteristics, are inserted into the system. In contradistinction, passive methods rely solely on observing system behavior in search of patterns of normal behavior with deviations thereof representing abnormal behavior. In their most sophisticated implementations, both passive and active defenses rely on the use of data analytics to identify the patterns and synthesize the observed and/or inserted signatures. Past research has shown that passive defenses may be bypassed by APT actors relying on data analytics and their intimate knowledge of the system to evade detection by respecting the patterns identified by the defenders. Here this manuscript explores the use of active defenses under the assumption that the attacker has privileged access to the system, including access to the system's model and sensors data. Specifically, this manuscript assesses the ability of active defenses to remain invisible to the attackers, and discusses the associated challenges that must be addressed to ensure their resiliency against APT actors.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Active microbial biomass decreases, but microbial growth potential remains similar across soil depth profiles under deeply-vs. shallow-rooted plants

Climate-smart land management practices that replace shallow-rooted annual crop systems with deeply-rooted perennial plants can contribute to soil carbon sequestration. However, deep soil carbon accrual may be influenced by active microbial biomass and their capacity to assimilate fresh carbon at depth. Incorporating active microbial biomass, dormancy, and growth in microbially-explicit models can improve our ability to predict soil's capacity to store carbon. But, so far, the microbial parameters that are needed for such modeling are poorly constrained, especially in deep soil layers. Here, we used a lab incubation experiment and growth kinetics model to estimate how microbial parameters vary along 240 cm of soil depth in profiles under shallow- (soy) and deeply-rooted (switchgrass) plants 11 years after plant cover conversion. We also assessed resource origin and availability (total organic carbon, 14 C, extractable organic carbon, specific UV absorbance of K 2 SO 4 extractable organic C, total nitrogen, total dissolved nitrogen) along the soil profiles to examine associations between soil chemical and biological parameters. Even though root biomass was greater and rooting depth was deeper under switchgrass than soy, resource availability and microbial growth parameters were generally similar between vegetation types. Instead, depth significantly influenced soil chemical and biological parameters. For example, resource availability and total and relative active microbial biomass decreased with soil depth. Decreases in the relative active microbial biomass coincided with increased lag time (response time to external carbon inputs) along the soil profiles. Even at a depth of 210–240 cm, microbial communities were activated to grow by added resources within a day. Maximum specific growth rate decreased to a depth of 90 cm and then remained consistent in deeper layers. Our findings show that >10 years of vegetation and rooting depth changes may not be long enough to alter microbial growth parameters, and suggest that at least a portion of the microbial community in deep soils can grow rapidly in response to added resources. Our study determined microbial growth parameters that can be used in microbially-explicit models to simulate carbon dynamics in deep soil layers.

14C↗