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Scown, Corinne D.

Publications and source records attributed to Scown, Corinne D..

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Greenhouse Gas and Air Pollutant Emissions from Composting

Composting can divert organic waste from landfills, reduce landfill methane emissions, and recycle nutrients back to soils. However, the composting process is also a source of greenhouse gas and air pollutant emissions. Researchers, regulators, and policy decision-makers all rely on emissions estimates to develop local emissions inventories and weigh competing waste diversion options, yet reported emission factors are difficult to interpret and highly variable. This review explores the impacts of waste characteristics, pretreatment processes, and composting conditions on CO 2 , CH 4 , N 2 O, NH 3 , and VOC emissions by critically reviewing and analyzing 388 emission factors from 46 studies. The values reported to date suggest that CH 4 is the single largest contributor to 100-year global warming potential (GWP 100 ) for yard waste composting, comprising approximately 80% of the total GWP 100 . For nitrogen-rich wastes including manure, mixed municipal organic waste, and wastewater treatment sludge, N 2 O is the largest contributor to GWP 100 , accounting for half to as much as 90% of the total GWP 100 . If waste is anaerobically digested prior to composting, N 2 O, NH 3 , and VOC emissions tend to decrease relative to composting the untreated waste. Effective pile management and aeration are key to minimizing CH 4 emissions. However, forced aeration can increase NH 3 emissions in some cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine learning for surrogate process models of bioproduction pathways

Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.

09 BIOMASS FUELS↗
DOE OSTI2022

Prospects for carbon-negative biomanufacturing

Biomanufacturing has the potential to reduce demand for petrochemicals and mitigate climate change. Recent studies have also suggested that some of these products can be net carbon negative, effectively removing CO 2 from the atmosphere and locking it up in products. This review explores the magnitude of carbon removal achievable through biomanufacturing and discusses the likely fate of carbon in a range of target molecules. Solvents, cleaning agents, or food and pharmaceutical additives will likely re-release their carbon as CO 2 at the end of their functional lives, while carbon incorporated into non-compostable polymers can result in long-term sequestration. Future research can maximize its impact by focusing on reducing emissions, achieving performance advantages, and enabling a more circular carbon economy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multifunctional landscapes for dedicated bioenergy crops lead to low-carbon market-competitive biofuels

Switchgrass is a promising feedstock for cellulosic biorefineries, due to its ability to maintain comparatively high biomass yields across a wide range of soil and climatic conditions. However, there is an incomplete understanding of the economic and environmental tradeoffs associated with cultivating switchgrass on low-productivity land for conversion to biofuels. This study surveys prior literature and demonstrates a new integrated assessment framework, including agroecosystem, ecosystem services valuation, technoeconomic, and life-cycle assessment models, to quantify and contextualize the economic and environmental impacts of switchgrass cultivation on marginal land with downstream conversion to biofuels. Monetizing and incorporating the value of ecosystem services, such as improved water quality benefits from nitrate and sediment reductions, climate change mitigation benefits from CO 2 emission reduction, and recreational and pollination benefits from increased biodiversity, the modeled multifunctional landscape reduces the ethanol production cost by 33.3–58.9 cents/$\scriptsize{L}$-gasoline-equivalent ($\$$1.3–2.2/gge). Planting switchgrass in low productivity land improves soil health, resulting in the carbon footprint reduction credit of 12.8–20.2 gCO 2e /MJ. For an improved switchgrass-to-ethanol conversion pathway with the maximum benefits from ecosystem services, the minimum ethanol selling price and carbon footprint of ethanol, respectively, could reach to 31 cents/$\scriptsize{L}$-gasoline-equivalent (47% reduction relative to average gasoline price) and 3 gCO 2e /MJ (97% reduction relative to gasoline). In conclusion, this low carbon renewable ethanol leads to substantial State and/or Federal policy incentives (~$\$$1/$\scriptsize{L}$-gasoline-equivalent) providing a large benefit to biorefinery operators, farmers, and the public as a whole.

09 BIOMASS FUELS↗
DOE OSTI2022

Circularity in mixed-plastic chemical recycling enabled by variable rates of polydiketoenamine hydrolysis

Footwear, carpet, automotive interiors, and multilayer packaging are examples of products manufactured from several types of polymers whose inextricability poses substantial challenges for recycling at the end of life. Here, we show that chemical circularity in mixed-polymer recycling becomes possible by controlling the rates of depolymerization of polydiketoenamines (PDK) over several orders of magnitude through molecular engineering. Stepwise deconstruction of mixed-PDK composites, laminates, and assemblies is chemospecific, allowing a prescribed subset of monomers, fillers, and additives to be recovered under pristine condition at each stage of the recycling process. We provide a theoretical framework to understand PDK depolymerization via acid-catalyzed hydrolysis and experimentally validate trends predicted for the rate-limiting step. The control achieved by PDK resins in managing chemical and material entropy points to wide-ranging opportunities for pairing circular design with sustainable manufacturing.

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

Comparing in planta accumulation with microbial routes to set targets for a cost-competitive bioeconomy

Plants and microbes share common metabolic pathways for producing a range of bioproducts that are potentially foundational to the future bioeconomy. However, in planta accumulation and microbial production of bioproducts have never been systematically compared on an economic basis to identify optimal routes of production. A detailed technoeconomic analysis of four exemplar compounds (4-hydroxybenzoic acid [4-HBA], catechol, muconic acid, and 2-pyrone-4,6-dicarboxylic acid [PDC]) is conducted with the highest reported yields and accumulation rates to identify economically advantaged platforms and breakeven targets for plants and microbes. The results indicate that in planta mass accumulation ranging from 0.1 to 0.3 dry weight % (dwt%) can achieve costs comparable to microbial routes operating at 40 to 55% of maximum theoretical yields. These yields and accumulation rates are sufficient to be cost competitive if the products are sold at market prices consistent with specialty chemicals ($20 to $50/kg). Prices consistent with commodity chemicals will require an order-of-magnitude-greater accumulation rate for plants and/or yields nearing theoretical maxima for microbial production platforms. This comparative analysis revealed that the demonstrated accumulation rates of 4-HBA (3.2 dwt%) and PDC (3.0 dwt%) in engineered plants vastly outperform microbial routes, even if microbial platforms were to reach theoretical maximum yields. Their recovery and sale as part of a lignocellulosic biorefinery could enable biofuel prices to be competitive with petroleum. Muconic acid and catechol, in contrast, are currently more attractive when produced microbially using a sugar feedstock. Ultimately, both platforms can play an important role in replacing fossil-derived products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties

Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool.

09 BIOMASS FUELS↗
DOE OSTI2022

Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100

Abstract Aims High‐resolution information on soils’ vulnerability to climate‐induced soil organic carbon (SOC) loss can enable environmental scientists, land managers, and policy makers to develop targeted mitigation strategies. This study aims to estimate baseline and decadal changes in continental US surface SOC stocks under future emission scenarios. Location Continental United States. Time period 2014–2100. Methods We used recent SOC field observations ( n = 6,213 sites), environmental factors ( n = 32), and an ensemble machine learning (ML) approach to estimate baseline SOC stocks in surface soils across the continental United States at 100‐m spatial resolution, and decadal changes under the projected climate scenarios of Coupled Model Intercomparison Project Phase Six (CMIP6) earth system models (ESMs). Results Baseline SOC projections from ML approaches captured more than 50% of variability in SOC observations, whereas ESMs represented only 6–16% of observed SOC variability. ML estimates showed a mean total loss of 1.8 Pg C from US surface soils under the high‐emission scenario by 2100, whereas ESMs showed no significant change in SOC stocks with wide variation among ESMs. Both ML and ESM predictions agree on the direction of SOC change (net emissions or sequestration) across 46–51% of continental US land area. These differences are attributable to the high‐resolution site‐specific data used in the ML models compared to the relatively coarse grid represented in CMIP6 ESMs. Main conclusions Our high‐resolution estimates of baseline SOC stocks, identification of key environmental controllers, and projection of SOC changes from US land cover types under future climate scenarios suggest the need for high‐resolution simulations of SOC in ESMs to represent the heterogeneity of SOC. We found that the SOC change is sensitive to key soil related factors (e.g. soil drainage and soil order) that have not been historically considered as input parameters in ESMs, because currently more than 95% variability in the SOC of CMIP6 ESMs is controlled by net primary productivity, temperature, and precipitation. Using additional environmental factors to estimate the baseline SOC stocks and predict the future trajectory of SOC change can provide more accurate results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Genomics Characterization of an Engineered Corynebacterium glutamicum in Bioreactor Cultivation Under Ionic Liquid Stress

Corynebacterium glutamicum is an ideal microbial chassis for production of valuable bioproducts including amino acids and next generation biofuels. Here we resequence engineered isopentenol (IP) producing C. glutamicum BRC-JBEI 1.1.2 strain and assess differential transcriptional profiles using RNA sequencing under industrially relevant conditions including scale transition and compare the presence vs absence of an ionic liquid, cholinium lysinate ([Ch][Lys]). Analysis of the scale transition from shake flask to bioreactor with transcriptomics identified a distinct pattern of metabolic and regulatory responses needed for growth in this industrial format. These differential changes in gene expression corroborate altered accumulation of organic acids and bioproducts, including succinate, acetate, and acetoin that occur when cells are grown in the presence of 50 mM [Ch][Lys] in the stirred-tank reactor. This new genome assembly and differential expression analysis of cells grown in a stirred tank bioreactor clarify the cell response of an C. glutamicum strain engineered to produce IP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Minimal Information Set To Enable Verifiable Theoretical Battery Research

Batteries are an enabling technology for addressing sustainability through the electrification of various forms of transportation (1) and grid storage. (2) Batteries are truly multi-scale, multi-physics devices, and accordingly various theoretical descriptions exist to understand their behavior (3-5) ranging from atomistic details to techno-economic trends. As we explore advanced battery chemistries (6,7) or previously inaccessible aspects of existing ones, (8-10) new theories are required to drive decisions. (11-13) The decisions are influenced by the limitations of the underlying theory. Advanced theories used to understand battery phenomena are complicated and require substantial effort to reproduce. However, such constraints should not limit the insights from these theories. We can strive to make the theoretical research verifiable such that any battery stakeholder can assess the veracity of new theories, sophisticated simulations or elaborate analyses. We distinguish verifiability, which amounts to “Can I trust the results, conclusions and insights and identify the context where they are relevant?”, from reproducibility, which ensures “Would I get the same results if I followed the same steps?” With this motivation, we propose a checklist to guide future reports of theoretical battery research in Table 1. We hereafter discuss our thoughts leading to this and how it helps to consistently document necessary details while allowing complete freedom for creativity of individual researchers. Given the differences between experimental and theoretical studies, the proposed checklist differs from its experimental counterparts. (14,15) This checklist covers all flavors of theoretical battery research, ranging from atomic/molecular calculations (16-19) to mesoscale (20,21) and continuum-scale interactions, (9,22) and techno-economic analysis. (23,24) Finally, as more and more experimental studies analyze raw data, (25) we feel this checklist would be broadly relevant.

25 ENERGY STORAGE↗