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This case study describes nine industrial energy efficiency projects in Wisconsin supported by $14.4 million from the American Recovery and Reinvestment Act of 2009. Participating companies provided $15.6 million in cost share funding. Each project achieved energy savings of 25% or more, helping to improve the companies’ competitiveness and protect jobs.
Radiocarbon in CO 2 ( 14 CO 2 ) measurements can aid in discriminating between fast (<1 year) and slower (>5–10 years) cycling of C between the atmosphere and the terrestrial biosphere due to the 14 C disequilibrium between atmospheric and terrestrial C. However, 14 CO 2 in the atmosphere is typically much more strongly impacted by fossil fuel emissions of CO 2 , and, thus, observations often provide little additional constraints on respiratory flux estimates at regional scales. Here we describe a data set of 14 CO 2 observations from a tall tower in northern Wisconsin (USA) where fossil fuel influence is far enough removed that during the summer months, the biospheric component of the 14 CO 2 budget dominates. We find that the terrestrial biosphere is responsible for a significant contribution to 14 CO 2 that is 2–3 times higher than predicted by the Carnegie-Ames-Stanford approach terrestrial ecosystem model for observations made in 2010. This likely includes a substantial contribution from the North American boreal ecoregion, but transported biospheric emissions from outside the model domain cannot be ruled out. The 14 CO 2 enhancement also appears somewhat decreased in observations made over subsequent years, suggesting that 2010 may be anomalous. Furthermore, with these caveats acknowledged, we discuss the implications of the observation/model comparison in terms of possible systematic biases in the model versus short-term anomalies in the observations. Going forward, this isotopic signal could be exploited as an important indicator to better constrain both the long-term carbon balance of terrestrial ecosystems and the short-term impact of disturbance-based loss of carbon to the atmosphere.
Measurements of nonlinear modulus and attenuation of fractures provide the opportunity to probe their mechanical state. We have adapted a low-frequency torsional apparatus to explore the seismic signature of fractures under low normal stress, simulating low effective stress environments such as shallow or high pore pressure reservoirs. We report strain-dependent modulus and attenuation for fractured samples of Duperow dolomite (a carbon sequestration target reservoir in Montana), Blue Canyon Dome rhyolite (a geothermal analog reservoir in New Mexico), and Montello granite (a deep basement disposal analog from Wisconsin). We use a simple single effective asperity partial slip model to fit our measured stress-strain curves, and solve for the friction coefficient, contact radius, and full slip condition. These observations have the potential to develop into new field techniques for measuring differences in frictional properties during reservoir engineering manipulations and estimate the stress conditions where reservoir fractures and faults begin to fully slip.
Quinones are appealing targets as organic electrolytes for aqueous redox flow batteries (RFBs), but their utility continues to be constrained by limited stability under operating conditions. The present study evaluates the stability of a series water-soluble quinones, with redox potentials ranging from 605–885 mV vs. NHE, under acidic aqueous conditions (1 M H2SO4). Four of the quinones are examined as cathodic electrolytes in an aqueous RFB, paired with anthraquinone-2,7-disulfonate (AQDS) as the anodic electrolyte. The RFB data complement the solution stability measures and show that the most stable electrolyte is a tetrasubstituted quinone containing four sulfonated thioether substituents. The results highlight the importance of substituting all C–H positions of the quinone in order to maximize the quinone stability and set the stage for design of improve organic electrolytes for aqueous RFBs. The authors thank Joseph Fourie for assisting in the synthesis of compound 8. Financial support for this work was provided by the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences (SSS, TWR, JBG, CWA, YP), with supplemental contributions by the Wisconsin Alumni Research Foundation (WARF) through the WARF Accelerator Program (YQ and partial support for JBG, CWA, YP). NMR spectroscopy facilities were partially supported by the NSF (CHE-0342998 and CHE-1048642), a UW Madison Instructional Laboratory Modernization Award, and a gift from Paul J. and Margaret M. Bender.
Abstract Biodiversity conservation requires understanding how disturbance influences biodiversity patterns at multiple spatial scales. Because the total diversity of species within a given region (γ diversity) is influenced by both local diversity (α diversity) and dissimilarity in community composition (β diversity), understanding disturbance effects on both components of diversity is essential, especially if disturbance impacts α and β diversities differently. In this three‐year study, we examined how a disturbance (annual harvesting of grasslands) and environmental gradients in the proportion of sand locally, habitat size, and landscape diversity influenced the abundance and α and β diversities of ants within tallgrass prairie habitat in Wisconsin. We used a null‐model approach to examine how harvest and environmental factors influence β diversity. Following three years of treatments, we found that ant abundance was greater in harvested sites compared to control sites and ant abundance was positively correlated with soil sandiness. We also found that α diversity was lower in harvested sites compared to control sites and none of the measured environmental gradients influenced α diversity. The effects of harvest on α‐diversity patterns may have been mediated through the competitive interactions of the two dominant ant species ( Formica montana and Lasius neoniger ). In contrast, β diversity (after adjusting for random effects and changes to α diversity) was higher in harvest sites compared to control sites, and variability in community composition was largely driven by the occurrence of rare species. The proportion of sand in the local habitat and habitat size positively influenced β diversity suggesting that community dissimilarity was due in part to environmental filtering and the size of species pools. Because biomass harvest had contrasting effects on ant α and β diversities, trade‐offs in maintaining α vs. β diversity might need to be considered in land management and conservation efforts.
Abstract Microscale O 2 availability in soil, a main factor influencing microbial processing of soil carbon and nitrogen, is a function of O 2 diffusion and microbial O 2 consumption. While O 2 diffusivity is widely studied, it is difficult to quantify the rate of microbial O 2 consumption ( MO ). In this study, we quantified the MO using O 2 microsensor profiling to compare MO in different soils and vegetation systems while manipulating the soil pore structure. Soil samples were collected from three bioenergy vegetation systems, sorghum ( Sorghum bicolor L. Moench), switchgrass ( Panicum virgatum L.), and prairie, at an experimental site in Southeast Michigan, and from switchgrass and prairie systems at two sites in South and North Wisconsin. We prepared soil cores with two contrasting pore structures, that is, large (>30 µm Ø) pore‐dominated soil (LP) and small (<10 µm Ø) pore‐dominated soil (SP), from the same 1‐ to 2‐mm soil size fraction. We quantified MO (nmol O 2 cm −3 s −1 ) using a microprofiler with an oxygen (OX‐100 µm) microsensor (Unisense) pre‐ and post‐38‐day incubation. Across all studied systems, there was a trend for a decrease in SP MO post‐incubation, suggesting a change in microbial metabolism to lower O 2 utilization. However, sizeable microbial O 2 consumption continued in the LP soils, further corroborated by a positive correlation between the C‐CO 2 emission rate and the post‐incubation MO , highlighting that LP provided a better physical microenvironment for the soil microbes.
Short rotation woody crops (SRWCs) including Populus species and their hybrids (i.e., poplars) are ideal for incorporating biomass production with phytotechnologies such as phytoremediation. To integrate these applications, 15 poplar plantings from nine long-term phytoremediation installations were sampled from 2012 to 2013 in the Midwest (Illinois, Iowa, Wisconsin) and Southeast (Alabama, Florida, North Carolina) United States. In this review, we report summary results of this sampling and how performance at each site compared with comparable phytoremediation systems in the literature. We review significant genotypic differences from each planting within the context of provisioning (i.e., biomass production) and regulating (i.e., carbon sequestration) ecosystem services and how they relate to the need for a cleaner environment during times of accelerated ecological degradation. Overall, the contaminated poplar sites provided these ecosystem services comparable to noncontaminated poplar sites used for bioenergy and biofuels feedstock production. For example, phytoremediation trees at the Midwestern sites had biomass values ranging from 4.4 to 15.5 Mg ha –1 y –1 , which was 20% less relative to bioenergy trees (p = .0938). Results were similar for diameter and carbon, with some genotype x environment interactions resulting in phytoremediation trees exhibiting substantially greater growth and productivity (i.e., +131% at one site). As illustrated in the current review, phytoremediation success can be increased with the identification and deployment of genotypes tailored to grow well and tolerate a broad diversity of contaminants (generalists) (i.e., 'DN34', 'NM6', '7300501') versus those that significantly outperform their counterparts under unique site conditions (specialists) (i.e., '220-5', '51-5', 'S13C20').
At two sites in the North Central USA (Michigan (KBS) and Wisconsin (ARL)), we evaluated the effect of N fertilization on the yield and quality of five perennial bioenergy feedstock cropping systems: (1) switchgrass (Panicum virgatum L.), (2) giant miscanthus (Miscanthus × giganteus), (3) a native grass mixture (5 species), (4) an early successional field (volunteer herbaceous species), and (5) a restored prairie (18 species). In a randomized complete block design with 5 replicates and 2 split plots, N was applied at 0 and 56 kg ha –1 to split plots for each cropping system from 2010 to 2016. No yield response to N was detected in switchgrass at either location in any year. Giant miscanthus exhibited a positive yield response to N at both sites (11% at KBS and 83% at ARL). Nitrogen fertilizer addition significantly reduced glucose (KBS 12.9 and 13.8 g kg –1 year –1 , ARL 11.2 and 9.7 g kg –1 year –1 ) in the native grass mix and restored prairie systems respectively. Here, nitrogen fertilizer also reduced xylose at KBS in the switchgrasss, native grass mix, and restored prairie (4.9, 7.5, and 5.0 g kg –1 year –1 ). At ARL, N fertilization reduced xylose levels in switchgrass, giant miscanthus, and restored prairie (7.4, 6.8, and 6.2 g kg –1 year –1 ) and increased xylose levels in the early successional system (5.0 g kg –1 year –1 ).
Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic quantum approach for training support vector machines. We show that the time complexity of our quantum approach is an order of magnitude better than the classical approach. Next, we compare the test accuracy of our quantum approach against a classical approach that uses the Scikit-learn library in Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC), Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies on par with the classical approach. Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with an increasing number of features and an increasing number of data points in the training dataset. In conclusion, our scalability results show that the quantum approach obtains a 3.5–4.5x speedup over the classical approach on datasets with many (millions of) features.
Hydropower with a small elevation change from inlet to outlet, known as “low-head” hydropower, is a relatively untapped resource for reliable green power generation. One major barrier to entry is the cost of the components needed to generate the power. Each installation site is unique, with various head levels, flow rates, and other unique site characteristics that drive up the cost of development and installation. As a result, custom-made components are necessary because the sites are intrinsically inefficient. However, customized parts are generally more expensive to manufacture than ready-made parts. Often times, the cost of custom-made components is so high that the low-head hydropower installation becomes non-viable. Additive manufacturing offers the ability to make custom components, ideal for one-off applications, at low costs that are well suited for the needs of low-head hydropower. Indirect additive manufacturing, such as making tools or dies rather than end use components, can also be used to make low-cost composite tooling as needed for these custom applications. This paper explores the use of additive manufacturing, both directly and indirectly, to produce the components of a turbine system for a low-head hydropower site. The parts were designed to form a unique modular system, which saves time for future designs and iterations. The system has operated for more than three years without failure at a test site in Wisconsin, USA. This work serves as a basis for future application of AM to low-head systems, in which the modular components can be customized for each unique hydropower installation.
Surface-atmosphere fluxes are known to vary at multiple time scales, but uncertainty is high as to how fluxes change spatially within regions. With an increase in the number of eddy covariance towers, we are now able to examine the geospatial coherence of ecosystem fluxes, using time-series correlation. Eighteen sites from Michigan and Wisconsin were used in this study, ranging from 100 m to 600 km apart. Surface-atmosphere fluxes from a six-month period were used to quantify spatial coherence on a pair-wise basis. Using geospatial statistics, carbon and sensible heat (H) fluxes were found to be 95% correlated directly outside of their flux footprint and 56% correlated up to a distance of ~35 km. Latent (LE) and momentum (τ) fluxes were less correlated, 83% directly outside of their flux footprint and 40% at a distance of ~130 km albeit, at a much larger spatial distance than for the carbon and sensible heat fluxes. All fluxes showed strong spectral resonance at diel and seasonal timescales, with 1-, 2- and 3-month periods being common modes of variability among H, LE, and τ fluxes. Results based on Empirical Orthogonal Function show distinct transitions of net ecosystem exchange from fall to winter before photosynthesis or respiration while H and τ do not exhibit coherent trends. Here, this work demonstrates the potential of quantifying geospatial coherence of surface-atmosphere fluxes in the Midwestern United States, with the ability to predict fluxes beyond the spatial limit of a single flux tower footprint. Ultimately, expanding the flux measurements to larger scales would allow better spatial scaling of terrestrial surface-atmosphere fluxes between tower footprint and modeling or remote sensing scales.
Biofuel demand is on the rise, yet there is limited information available about the likely implications of increased planting of cellulosic biofuel crops on water and nutrient fluxes. To help address this gap, this study quantifies subsurface water, nitrate, and dissolved phosphorus fluxes below feedstock crops managed for biofuel production on naturally well drained soils in southern Wisconsin. Automated equilibrium tension lysimeters were used to measure subsurface (below root zone) fluxes of water and nutrients below continuous corn (CC), monoculture switchgrass (SG), and hybrid poplar (HP) cropping systems for three years. Average water fluxes below SG were nearly double those below CC and HP. Most of this difference can be linked to seasonal events, particularly during late winter and early spring when subsurface water fluxes and increased soil moisture were observed below SG plots, likely due to enhanced macropore flow. Nitrate fluxes were approximately 14 times higher below CC than the 37.4 kg NO 3 -N/ha three year annual average observed for SG; the nitrate fluxes were negligible below HP despite similar nitrogen application rates. The high flux in one of the two CC plots is likely due to excessive manure applications prior to the study period; however, the nitrate loss potential from CC is much higher than the perennial crops. Dissolved reactive phosphorus (DRP) concentrations were very low below all three cropping systems (average of 0.076, 0.083, 0.034 mg/L in SG, CC, and HP respectively). For the cropping systems evaluated, nitrate fluxes were driven by concentration differences in leachate and not influenced by nominal differences in N fertilizer inputs, whereas DRP fluxes were mainly driven by water fluxes and therefore independent of phosphorus inputs and soil test phosphorus. The data in this paper provide a foundation to better understand differences in fluxes below different biofuel cropping systems.
Prescribed fire – the intentional use of fire to help achieve a land management goal – is becoming increasingly common as a land management practice. Soil physical, chemical, and biological properties can be affected by prescribed fires, but depend on the fire, soil type, residence time and frequency, and may not be changed substantially in low-severity burns. In this study, we examined soil bacterial community composition immediately post-fire (15 days) in a sandy jack pine barrens soil in Wisconsin, USA. Soil bacterial communities clustered significantly by sample site (p < 0.001) and by soil horizon (p = 0.048), but not by whether or not soil samples were visibly burned. There were also no significant differences in total relative abundance at the phylum level in visibly burned vs. not visibly burned soils, and only two significant differences in abundance or variability of individual taxa. Soil properties remained unchanged post-fire and the fire was visibly patchy, suggesting that the low severity prescribed fire most likely had a minimal soil heating effect. Furthermore, we suggest the minimal bacterial community composition shifts seen in this study were likely mediated more by plants than by direct heat-killing or changes to soil properties.
Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.
I am a Professor of Biochemistry, Biophysics and Structural Biology and Plant and Microbial Biology at the University of California in Berkeley. I was born and raised in India, emigrated to the United States to attend university, earning a B.S. in Molecular Biology and a Ph.D. in Biochemistry at the University of Wisconsin in Madison. Following post-doctoral studies with Lawrence Bogorad at Harvard University where I became interested in genetic control of trace element quotas, I joined the department of Chemistry and Biochemistry at UCLA. One of the first to appreciate essential trace metals as potential regulators of gene expression, I articulated the details of the nutritional Cu regulon in Chlamydomonas. In parallel, I used genetic approaches to discover the genes governing missing steps in tetrapyrrole metabolism, including the attachment of heme to apocytochromes in the thylakoid lumen and the factors catalyzing the formation of ring V in chlorophyll. After biochemistry and classical genetics, I embraced genomics, taking a leadership role on the Joint Genome Institute’s efforts on the Chlamydomonas genome and more recently, contributing to high quality assemblies of several genomes in the green algal radiation, and large transcriptomic and proteomic datasets — focusing on the diel metabolic cycle in synchronized cultures and acclimation to key environmental and nutritional stressors — that are well-used and appreciated by the community. Finally, a new venture in Berkeley is the promotion of Auxenochlorella protothecoides as the true “green yeast” and as a platform for engineering algae to produce useful bioproducts.
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Several advanced nuclear reactor designs promise efficiency and safety improvements over the current reactor fleet but are limited by the current set of ASME code-qualified materials. Novel alloys including high-entropy alloys (HEAs), and more broadly compositionally complex alloys (CCAs), have shown promising irradiation-tolerance. However, the vast range of alloy compositions adds to an already time-consuming alloy development process. In this study, to accelerate the development of novel alloys for nuclear applications, a high-throughput (HTP) methodology has been employed. Additive manufacturing has been used to produce a compositional array of unary, binary, ternary, and quaternary alloys, including several CCAs, which span the Cr-Fe-Mn-Ni composition space. Additionally, the compositional array was homogenized at 1000°C for 24 hours and each sample was irradiated using 4-MeV Ni2+ ions at room temperature to a peak damage of 50 dpa, as estimated using SRIM, at the University of Wisconsin Ion Beam Laboratory. A custom XY stage was built to accommodate the large compositional array and half of each sample was masked during irradiation enabling both the irradiated and unirradiated properties of each alloy to be characterized side-by-side. Each alloy was characterized using X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and nanoindentation. CALPHAD simulations spanning the entire Cr-Fe-Mn-Ni composition space at 1000°C were performed to compare predicted equilibrium phases with phases identified experimentally from the unirradiated regions of each alloy. Nanoindentation measurements indicate radiation-induced hardening ranging from ~1-1.5 GPa in each of Cr-Fe-Mn-Ni CCAs, which is relatively insensitive to modest changes in alloy composition and comparable to hardening observed in neutron irradiated Cr-Fe-Mn-Ni CCAs in the literature. Overall, a substantial time savings was realized by employing HTP synthesis, irradiation, and characterization in this study compared to conventional techniques, the implications of which are discussed.
This paper summarizes the benchmark analyses performed in a joint effort of ENEA (Italy), JAEA (Japan), KIT (Germany), and the University of Wisconsin (USA) on a computational ITER benchmark and a series of 14 MeV neutron benchmark experiments. The computational benchmark revealed a modest increase of the neutron flux levels in the deep penetration regions and a substantial increase of the gas production in steel components. The comparison to experimental results showed good agreement with no substantial differences between FENDL-3.0 and FENDL-2.1 for most of the responses. In general, FENDL-3 shows an improved performance for fusion neutronics applications.