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NASA NTRS2016

Interpreting the Ultraviolet Aerosol Index Observed with the OMI Satellite Instrument to Understand Absorption by Organic Aerosols: Implications for Atmospheric Oxidation and Direct Radiative Effects

Satellite observations of the ultraviolet aerosol index (UVAI) are sensitive to absorption of solar radiation by aerosols; this absorption affects photolysis frequencies and radiative forcing. We develop a global simulation of the UVAI using the 3-D chemical transport model GEOSChem coupled with the Vector Linearized Discrete Ordinate Radiative Transfer model (VLIDORT). The simulation is applied to interpret UVAI observations from the Ozone Monitoring Instrument (OMI) for the year 2007. Simulated and observed values are highly consistent in regions where mineral dust dominates the UVAI, but a large negative bias (-0.32 to -0.97) exists between simulated and observed values in biomass burning regions. We determine effective optical properties for absorbing organic aerosol, known as brown carbon (BrC), and implement them into GEOS-Chem to better represent observed UVAI values over biomass burning regions. The inclusion of absorbing BrC decreases the mean bias between simulated and OMI UVAI values from -0.57 to -0.09 over West Africa in January, from -0.32 to +0.0002 over South Asia in April, from -0.97 to -0.22 over southern Africa in July, and from -0.50 to +0.33 over South America in September. The spectral dependence of absorption after including BrC in the model is broadly consistent with reported observations for biomass burning aerosol, with absorbing Angstrom exponent (AAE) values ranging from 2.9 in the ultraviolet (UV) to 1.3 across the UV-Near IR spectrum. We assess the effect of the additional UV absorption by BrC on atmospheric photochemistry by examining tropospheric hydroxyl radical (OH) concentrations in GEOS-Chem. The inclusion of BrC decreases OH by up to 30% over South America in September, up to 20% over southern Africa in July, and up to 15% over other biomass burning regions. Global annual mean OH concentrations in GEOS-Chem decrease due to the presence of absorbing BrC, increasing the methyl chloroform lifetime from 5.62 to 5.68 years, thus reducing the bias against observed values. We calculate the direct radiative effect (DRE) of BrC using GEOS-Chem coupled with the radiative transfer model RRTMG (GC-RT). Treating organic aerosol as containing more strongly absorbing BrC changes the global annual mean all-sky top of atmosphere (TOA) DRE by +0.03Wm(exp -2) and all-sky surface DRE by -0.08Wm(exp -2). Regional changes of up to +0.3Wm(exp -2) at TOA and down to -1.5Wm(exp -2) at the surface are found over major biomass burning regions.

Air Pollution↗

DOE OSTI2023

Photoexcitation of Fe 3 O Nodes in MOF Drives Water Oxidation at pH=1 When Ru Catalyst Is Present

Abstract Artificial photosynthesis strives to convert the energy of sunlight into sustainable, eco‐friendly solar fuels. However, systems with light‐driven water oxidation reaction (WOR) at pH=1 are rare. Broadly used [Ru(bpy) 3 ] 2+ (bpy=2,2’‐bipyridine) photosensitizer has a fixed +1.23 V potential which is insufficient to drive most water oxidation catalysts (WOCs) in acid, while Fe 2 O 3 , featuring the highly oxidizing holes, is not stable at low pH. Here, the key examples of Fe‐based metal–organic framework (MOF) water oxidation photoelectrocatalysts active at pH=1 are presented. Fe‐MIL‐126 and Fe MOF‐dcbpy structures were formed with 4,4’‐biphenyl dicarboxylate (bpdc), 2,2’‐bipyridine‐5,5’‐dicarboxylate (dcbpy) linkers and their mixtures. Presence of dcbpy linkers allows integration of metal‐based catalysts via coordination to 2,2’‐bipyridine fragments. Fe‐based MOFs were doped with Ru‐based precursors to achieve highly active MOFs bearing [Ru(bpy)(dcbpy)(H 2 O) 2 ] 2+ WOC. Materials were analyzed with X‐ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infra‐red (FTIR) spectroscopy, resonance Raman, X‐ray absorption spectroscopy, fs optical pump‐probe, electron paramagnetic resonance (EPR), diffuse reflectance and electric conductivity measurements and were modeled by band structure calculations. It is shown that under reaction conditions, Fe III and Ru III oxidation states are present, indicating rate‐limiting electron transfer in MOF. Fe 3 O nodes emerge as photosensitizers able to drive prolonged O 2 evolution in acid. Further developments are possible via MOF's linker modification for enhanced light absorption, electrical conductivity, reduced MOF solubility in acid, Ru‐WOC modification for faster WOC catalysis, or Ru‐WOC substitution to 3d metal‐based systems. The findings give further insight for development of light‐driven water splitting systems based on Earth‐abundant metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Role of Intermolecular Interactions in Deep Eutectic Solvents for CO 2 Capture: Vibrational Spectroscopy and Quantum Chemical Studies

Recent research and reviews on CO 2 capture methods, along with advancements in industry, have highlighted high costs and energy-intensive nature as the primary limitations of conventional direct air capture and storage (DACS) methods. In response to these challenges, deep eutectic solvents (DESs) have emerged as promising absorbents due to their scalability, selectivity, and lower environmental impact compared to other absorbents. However, the molecular origins of their enhanced thermal stability and selectivity for DAC applications have not been explored before. Therefore, the current study focuses on a comprehensive investigation into the molecular interactions within an alkaline DES composed of potassium hydroxide (KOH) and ethylene glycol (EG). Combining Fourier transform infrared (FT-IR) and quantum chemical calculations, the study reports structural changes and intermolecular interactions induced in EG upon addition of KOH and its implications on CO 2 capture. Experimental and computational spectroscopic studies confirm the presence of noncovalent interactions (hydrogen bonds) within both EG and the KOH-EG system and point to the aggregation of ions at higher KOH concentrations. Additionally, molecular electrostatic potential (MESP) surface analysis, natural bond orbital (NBO) analysis, quantum theory of atoms-in-molecules (QTAIM) analysis, and reduced density gradient-noncovalent interaction (RDG-NCI) plot analysis elucidate changes in polarizability, charge distribution, hydrogen bond types, noncovalent interactions, and interaction strengths, respectively. Evaluation of explicit and hybrid models assesses their effectiveness in representing intermolecular interactions. This research enhances our understanding of molecular interactions in the KOH-EG system, which are essential for both the absorption and desorption of CO 2 . The study also aids in predicting and selecting DES components, optimizing their ratios with salts, and fine-tuning the properties of similar solvents and salts for enhanced CO 2 capture efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Single-Molecule Conductance through Hybrid Radially and Linearly π-Conjugated Macromolecules Reveals an Unusual Intramolecular π-Interaction

We describe the design, synthesis, and single-molecule junction conductance of π-electron molecules bearing both radial and linear π-conjugation pathways, whereby cycloparaphenylene (CPP) radial cores are π-extended linearly with aryl alkyne substituents as models for previously reported CPP-arylene ethynylene conjugated polymers. Although radially and linearly conjugated molecules have been studied previously in isolation as junction-bridging molecular electronic units, this is the first study to examine molecules where both topologies are operative. Our results reveal that the presence of radial CPP components within the junction-spanning pathway leads to a reduction in the conductance of the backbone compared to model linear phenyl substituents. Through tight-binding and DFT-based calculations, we attribute this conductance change to intramolecular van der Waals (vdW) interactions between the CPP ring and the junction-spanning arylene-ethynylene molecular backbone. These interactions induce changes in the dihedral angles of the backbone, leading to a reduced overlap of π orbitals within the molecular junction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Direct Measurement of the Selective Microwave-Induced Heating of Agglomerates of Dipolar Molecules: The Origin of and Parameters Controlling a Microwave Specific Superheating Effect

Agglomerates of polar molecules in nonpolar solvents are selectively heated by microwave radiation. The magnitude of the selective heating was directly measured by using the temperature dependence of the intensities of the Stokes and anti-Stokes bands in the Raman spectra of p-nitroanisole (pNA) and mesitylene. Under dynamic heating conditions, a large apparent temperature difference (ΔT) of over 100 °C was observed between the polar pNA solute and the nonpolar mesitylene solvent. This represents the first direct measurement of the selective microwave heating process. The magnitude of the selective microwave heating was affected by the properties of the agglomerated pNA. As the concentration of the pNA increases, the magnitude of the selective heating of the pNA was observed to decrease. This is explained by the tendency of the pNA dipoles to orient in an antiparallel fashion in the aggregates as measured by the Kirkwood g value, which decreased with increasing concentration. This effect reduces the net dipole moment of the agglomerates, which decreases the microwave absorption. After the radiation was terminated, the effective temperature of the dipolar molecules returned slowly to that of the medium. The slow heat transfer was modeled successfully by treating the solutions as a biphasic solvent/solute system. Based on modeling and the fact that the agglomerate can be heated above the boiling temperature of the solvent, an insulating layer of solvent vapor is suggested to form around the heated agglomerate, slowing convective heat transfer out of the agglomerate. Furthermore, this is an effect unique to microwave heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2016

Full Field X-Ray Fluorescence Imaging Using Micro Pore Optics for Planetary Surface Exploration

Many planetary surface processes leave evidence as small features in the sub-millimetre scale. Current planetary X-ray fluorescence spectrometers lack the spatial resolution to analyse such small features as they only provide global analyses of areas greater than 100 mm(exp 2). A micro-XRF spectrometer will be deployed on the NASA Mars 2020 rover to analyse spots as small as 120m. When using its line-scanning capacity combined to perpendicular scanning by the rover arm, elemental maps can be generated. We present a new instrument that provides full-field XRF imaging, alleviating the need for precise positioning and scanning mechanisms. The Mapping X-ray Fluorescence Spectrometer - "Map-X" - will allow elemental imaging with approximately 100μm spatial resolution and simultaneously provide elemental chemistry at the scale where many relict physical, chemical and biological features can be imaged in ancient rocks. The arm-mounted Map-X instrument is placed directly on the surface of an object and held in a fixed position during measurements. A 25x25 mm(exp 2) surface area is uniformly illuminated with X-rays or alpha-particles and gamma-rays. A novel Micro Pore Optic focusses a fraction of the emitted X-ray fluorescence onto a CCD operated at a few frames per second. On board processing allows measuring the energy and coordinates of each X-ray photon collected. Large sets of frames are reduced into 2d histograms used to compute higher level data products such as elemental maps and XRF spectra from selected regions of interest. XRF spectra are processed on the ground to further determine quantitative elemental compositions. The instrument development will be presented with an emphasis on the characterization and modelling of the X-ray focussing Micro Pore Optic. An outlook on possible alternative XRF imaging applications will be discussed.

micro-XRF spectrometer↗

DOE OSTI2021

Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Toward accelerating rare-earth metal extraction using equivariant neural networks

The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their thio-analogues, selected for their established efficacy in binding these elements. Trained on this valuable resource, our Allegro models demonstrate excellent performance, particularly when trained to directly predict DFT-level binding energies, yielding highly accurate results that closely correlate with theoretical calculations on a diverse test set. Furthermore, this strategy exhibited strong out-of-sample generalization, accurately predicting binding energies for an isomeric HOPO-derivative ligand not seen during training. By substantially reducing computational demands, this machine learning framework, alongside the provided dataset, represent powerful tools to accelerate the high-throughput screening and rational design of novel ligands for efficient rare-earth metal separation.

Gupta, Ankur K. [Lawrence Berkeley National Labora↗

DOE OSTI2022

Rank-reduced coupled-cluster. III. Tensor hypercontraction of the doubles amplitudes

In this work, we develop a quartic-scaling implementation of coupled-cluster singles and doubles (CCSD) based on low-rank tensor hypercontraction (THC) factorizations of both the electron repulsion integrals (ERIs) and the doubles amplitudes. This extends our rank-reduced (RR) coupled-cluster method to incorporate higher-order tensor factorizations. The THC factorization of the doubles amplitudes accounts for most of the gain in computational efficiency as it is sufficient, in conjunction with a Cholesky decomposition of the ERIs, to reduce the computational complexity of most contributions to the CCSD amplitude equations. Further THC factorization of the ERIs reduces the complexity of certain terms arising from nested commutators between the doubles excitation operator and the two-electron operator. We implement this new algorithm using graphical processing units and demonstrate that it enables CCSD calculations for molecules with 250 atoms and 2500 basis functions using a single computer node. Furthermore, we show that the new method computes correlation energies with comparable accuracy to the underlying RR-CCSD method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Toward more accurate adiabatic connection approach for multireference wavefunctions

A multiconfigurational adiabatic connection (AC) formalism is an attractive approach to compute the dynamic correlation within the complete active space self-consistent field and density matrix renormalization group (DMRG) models. Practical realizations of AC have been based on two approximations: (i) fixing one- and two-electron reduced density matrices (1- and 2-RDMs) at the zero-coupling constant limit and (ii) extended random phase approximation (ERPA). This work investigates the effect of removing the “fixed-RDM” approximation in AC. The analysis is carried out for two electronic Hamiltonian partitionings: the group product function- and the Dyall Hamiltonians. Exact reference AC integrands are generated from the DMRG full configuration interaction solver. Two AC models are investigated, employing either exact 1- and 2-RDMs or their second-order expansions in the coupling constant in the ERPA equations. Calculations for model molecules indicate that lifting the fixed-RDM approximation is a viable way toward improving the accuracy of existing AC approximations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Cleavable Strand‐Fusing Cross‐Linkers as Additives for Chemically Deconstructable Thermosets with Preserved Thermomechanical Properties

Abstract Permanently cross‐linked polymer networks—thermosets—are often difficult to chemically deconstruct. The installation of cleavable bonds into the strands of thermosets using cleavable comonomers as additives can facilitate thermoset deconstruction without replacement of permanent cross‐links, but such monomers can lead to reduced thermomechanical properties and require high loadings to function effectively, motivating the design of new and optimal cleavable additives. Here, we introduce “strand‐fusing cross‐linkers” (SFCs), which fuse two network strands via a four‐way cleavable cross‐link. SFCs enable deconstruction of model polydicyclopentadiene (pDCPD) thermosets with as little as one‐fifth of the molar loading needed to achieve deconstruction using traditional cleavable comonomers. SFCs function under traditional oven curing as well as low‐energy frontal ring‐opening metathesis polymerization (FROMP) conditions and lead to improved thermomechanical properties, for example, glass transition temperatures, compared to prior cleavable comonomer designs. This work motivates the development of increasingly improved cleavable additives to enable thermoset deconstruction without compromising material performance.

Zhang, Shuyi [Department of Chemistry Massachusett↗

DOE OSTI2025

Cleavable Strand‐Fusing Cross‐Linkers as Additives for Chemically Deconstructable Thermosets with Preserved Thermomechanical Properties

Zhang, Shuyi [Department of Chemistry Massachusett↗

DOE OSTI2021

PDG: A Composite Method Based on the Resolution of the Identity

The Gaussian-3 (G3) composite approach for thermochemical properties is revisited in light of the enhanced computational efficiency and reduced memory costs by applying the resolution-of-the-identity (RI) approximation for two-electron repulsion integrals (ERIs) to the computationally demanding component methods in the G3 model: the energy and gradient computations via the second-order Møller–Plesset perturbation theory (MP2) and the energy computations using the coupled-cluster singles–doubles method augmented with noniterative triples corrections [CCSD(T)]. Efficient implementation of the RI-based methods is achieved by employing a hybrid distributed/shared memory model based on MPI and OpenMP. The new variant of the G3 composite approach based on the RI approximation is termed the RI-G3 scheme, or alternatively the PDG method. The accuracy of the new RI-G3/PDG scheme is compared to the “standard” G3 composite approach that employs the memory-expensive four-center ERIs in the MP2 and CCSD(T) calculations. Taking the computation of the heats of formation of the closed-shell molecules in the G3/99 test set as a test case, it is demonstrated that the RI approximation introduces negligible changes to the mean absolute errors relative to the standard G3 model (less than 0.1 kcal/mol), while the standard deviations remain unaltered. Furthermore, the efficiency and memory requirements for the RI-MP2 and RI-CCSD(T) methods are compared to the standard MP2 and CCSD(T) approaches, respectively. The hybrid MPI/OpenMP-based RI-MP2 energy plus gradient computation is found to attain a 7.5× speedup over the standard MP2 calculations. For the most demanding CCSD(T) calculations, the application of the RI approximation is found to nearly halve the memory demand, confer about a 4–5× speedup for the CCSD iterations, and reduce the computational time for the compute-intensive triples correction step by several hours.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Severe 21st-century ocean acidification in Antarctic Marine Protected Areas

Antarctic coastal waters are home to several established or proposed Marine Protected Areas (MPAs) supporting exceptional biodiversity. Despite being threatened by anthropogenic climate change, uncertainties remain surrounding the future ocean acidification (OA) of these waters. Here we present 21st-century projections of OA in Antarctic MPAs under four emission scenarios using a high-resolution ocean–sea ice–biogeochemistry model with realistic ice-shelf geometry. By 2100, we project pH declines of up to 0.36 (total scale) for the top 200 m. Vigorous vertical mixing of anthropogenic carbon produces severe OA throughout the water column in coastal waters of proposed and existing MPAs. Consequently, end-of-century aragonite undersaturation is ubiquitous under the three highest emission scenarios. Given the cumulative threat to marine ecosystems by environmental change and activities such as fishing, our findings call for strong emission-mitigation efforts and further management strategies to reduce pressures on ecosystems, such as the continuation and expansion of Antarctic MPAs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Intra-Engine Trace Species Chemistry

Prompted by the needs of downstream plume-wake models, the Massachusetts Institute of Technology (MIT) and Aerodyne Research Incorporated (ART) initiated a collaborative effort, with funding from the NASA AEAP, to develop tools that would assist in understanding the fundamental drivers of chemical change within the intra-engine exhaust flow path. Efforts have been focused on the development of a modeling methodology that can adequately investigate the complex intra-engine environment. Over the history of this project, our research has increasingly pointed to the intra-engine environment as a possible site for important trace chemical activity. Modeling studies we initiated for the turbine and exhaust nozzle have contributed several important capabilities to the atmospheric effects of aviation assessment. These include a more complete understanding of aerosol precursor production, improved initial conditions for plume-wake modeling studies, and a more comprehensive analysis of ground-based test cell and in-flight exhaust measurement data. In addition, establishing a physical understanding of important flow and chemical processes through computational investigations may eventually assist in the design of engines to reduce undesirable species.

Waitz, Ian A.↗

NASA NTRS2016

Why Do Models Overestimate Surface Ozone in the Southeast United States?

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO(x) triple bond NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC(exp 4)RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25 deg x 0.3125 deg horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NO(x) from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC(exp 4)RS observations of NO(x) and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NO(x) emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO(x) emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NO(x) emissions. We find that only half of isoprene oxidation proceeds by the high-NO(x) pathway to produce ozone; this fraction is only moderately sensitive to changes in NO(x) emissions because isoprene and NO(x) emissions are spatially segregated. GEOS-Chem with reduced NO(x) emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NO(x) oxidation products. However, the model is still biased high by 6 plus or minus 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

Oxidation↗

DOE OSTI2026

Combined Nitrogen and Phosphorous Recovery via Electrochemical Technology Integration into Municipal Wastewater Treatment Plants

This project developed electroN-P, an electrochemical technology for recovering nitrogen and phosphorus as a fertilizer product from anaerobic digester centrate, targeting an advancement from TRL 4 to 6. The reactor was scaled 220× from a 250 mL batch cell to a continuous 4-channel system treating 55 L of wastewater, recovering >80% of phosphorus. Energy consumption under constant-voltage operation was lower than the embedded energy of conventional fertilizers, while constant-current operation produced a cost-competitive product with a smaller reactor footprint. TEA and LCA outcomes were highly sensitive to the magnesium source; alternative magnesium salt configurations projected reduced costs and embedded energy compared to sacrificial rods, but at the expense of significantly longer operation times. The recovered fertilizer performed comparably to diammonium phosphate and triple superphosphate in soil and plant trials. Integrating the technology into a whole-plant model reduced aeration energy by nearly 50%, lowering the levelized cost of water treatment from $0.668/m 3 to $0.648/m 3 . Results support targeting commercialization at smaller (≤1 MGD) facilities with high-strength digester streams. Further investigation into the transport and corrosion kinetics governing sacrificial anode wear is recommended to support continuous, longer-term operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗