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ULF Wave-Associated Density Irregularities and Scintillation at the Equator
This paper presents independent multi-instrument observations that address the physical mechanisms of how ultralow-frequency (ULF) wave-associated electric fields initiate ionospheric density fluctuation and scintillation at the equator. Since the magnetic field at the equator is entirely embedded in a relatively high-collision and high-conductivity medium, the condition may not be possible for the geomagnetic field to fluctuate due to ULF wave activity. This implies that the fluctuating electric field at the equator may not be produced through equatorial dynamo action due to fluctuating magnetic fields. Instead, the electric field penetrates from high latitudes and produces fluctuating magnetic field as well as modulates the vertical drift and hence causes the density to fluctuate at the equatorial region. We demonstrate this by estimating the ULF associated fluctuating electric field at high latitudes and at the equatorial region by applying the appropriate attenuation factor as it penetrates to lower latitudes. The periodicity of both electric field and density fluctuations appears to be between 6 and 9 min, which is a typical period of ULF waves in the Pc5 range. Because of its large amplitude and long periods compared to other ULF wave frequency bands, the Pc5 wave-associated electric field, which can even be estimated using magnetograms with low sensitivity and low sampling rate (e.g., 1 min), can easily penetrate to the lower latitude region and produce significant ionospheric density fluctuations that can be strong enough to create scintillation at the equatorial region.
Atomically Precise Membranes for the Separation of Gases
Industrial separations require enormous amounts of energy, accounting for approximately half the industrial energy use and 10–15% of the total energy consumption. Distillation alone accounts for about half the energy demand for industrial separations. If these processes could be replaced by an energy-efficient membrane separation process, this energy demand could be reduced by 90%. However, although some membrane processes have made inroads into thermal distillation, for membrane-based separations to replace the distillation process to a far more significant and practical level, new membranes with higher robustness, selectivity, and flux still need be developed. In this membrane development program, we leveraged Temple University’s prior work in spiroligomers to develop robust membrane structures. These structures can be formed into atomically precise pores by controlling the chemical synthesis and the oligomer building blocks to first build precisely controlled macrocycles and then subsequently crosslinking these macrocycles to produce a membrane. Furthermore, through the highly controllable chemistry of our molecular building blocks, not only can we integrate pores with highly controllable and reproducible size and morphology, but we can also target internal functionalization. By using a range of scalable membrane synthesis approaches, combined with control of each and every pores internal chemistry and molecular conformation we can achieve membrane structures that can display the ultimate in high selectivity and permeance. The Mainstream-Temple University membranes can be designed to achieve ultra-selective separations based on the key factors of molecular size, shape, and functionality. In our approach to fabricate scalable atomically precise membranes, we used our molecular Lego nanostructures. We demonstrated an approach to create atomically precise pores within the membrane with pores that are the dimensions of the molecule we are trying to separate. Moreover, in addition to controlling the morphology of the membrane structure by controlling the size of every pore, we can also decorate every pore with precisely targeted and placed functional groups. These accurately placed functional groups can provide selective binding to molecules and provide enhanced selectivity via a facilitated transport mechanism. In Phase II, the Mainstream-Temple University team demonstrated the scalable synthesis of oligomers and the ability to control the pore internal and external functionality, or chemistry, to allow the fabrication of a thin-film membrane. The initial steps of the spiroligomer synthesis to produce the two key bis-amino enantiomer building blocks were scaled from the gram scale to the kilogram scale, obtaining 25 kg of the materials by transferring the procedure to a toll manufacturer. Finally, we successfully optimized the fabrication of these macrocycles into robust membranes. We successfully transitioned from a laboratory based, hard-to-scale Langmuir trough synthesis to a highly scalable, roll-to-roll applicable, interfacial polymerization process. During this Phase II program, we established a platform of atomically precise membranes where our highly controllable, atomically precise macrocycles served as a scaffold with precisely and uniformly controlled pores. Furthermore, this layer can be tailored to accommodate a diverse range of functional groups, which both further controlled the pore size to enhance the sieving effect as well as imparting precisely controlled targeted selectivity through biomimetic molecular interactions. In this Phase II, we established and scaled up a platform approach to both tune the pore size and chemistry as well as scale it to produce membranes that can be applied to a wide range of industries. In future development, the project team expects to scale up both the macrocycle building block production and interfacial polymerization process to produce the atomically precise membrane with targeted pore sizes and pore chemistries.
Initial Engineering and Design for CO 2 Capture from Ethanol Facilities
The Energy & Environmental Research Center, in partnership with the U.S. Department of Energy (DOE) and North Dakota ethanol producer Red Trail Energy, LLC (RTE) and with technical support from Trimeric Corporation and the KLJ engineering firm, completed an initial engineering design (IED) for a hybrid capture system to estimate associated costs for retrofitting the operational RTE ethanol plant. The project goal supports the DOE objective to achieve negative emissions using commercially available technologies to attain Technology Readiness Level 6+ from a facility emitting >100,000 tonnes of carbon dioxide (CO 2 ) annually. The RTE CCS (carbon capture and storage) Project is currently operating a CO 2 capture facility, adjacent to the RTE ethanol facility in western North Dakota, and injecting the CO 2 more than a mile below RTE property for permanent storage. This novel hybrid capture system would process about 310,000 tonnes of CO 2 annually for the RTE host site and includes CO 2 capture and compression from bioprocessing as well as capturing CO 2 produced from natural gas boilers. The bioprocessing capture process, based on past RTE studies investigating ammonia liquefaction technology with CO 2 produced from natural gas boilers captured utilizing chemical absorption with amine, was shown to be technically feasible for the RTE site. Activities conducted to support the project goal included 1) designing a hybrid capture system using CO 2 emissions from both bioprocessing and heat production at the host site facility; 2) conducting a pre-front-end engineering and design analysis of the hybrid capture system to include environmental health and safety, a constructability report, identification of permits, and corporate approvals; and 3) performing a techno-economic assessment in accordance with DOE’s methodology, as demonstrated by the bituminous baseline study. In addition, a life cycle assessment (LCA) was completed for the RTE site to estimate the carbon life cycle for ethanol-CCS implementation using the designed hybrid capture system to determine potential for net-negative carbon emissions. Results of the IED showed the hybrid system to be technically viable, with a moderate estimated cost of $55/tonne CO 2 captured for the hybrid system. The cradle-to-gate LCA showed preliminary net-negative carbon emissions potential anticipated from implementing a hybrid CCS system at a commercial scale. Recommended next steps toward potential implementation include hybrid capture system demonstration testing for detailed engineering and LCA model comparisons with low-carbon fuel incentive programs for financial support.
D2U: Data Driven User Emulation for the Enhancement of Cyber Testing, Training, and Data Set Generation
Whether testing intrusion detection systems, conducting training exercises, or creating data sets to be used by the broader cybersecurity community, realistic user behavior is a critical component of a cyber range. Existing methods either rely on network level data or replay recorded user actions to approximate real users in a network. Our work is the first to produce generative models trained on actual user data (sequences of application usage) collected on endpoints. Once trained to the user's behavioral data, these models can generate novel sequences of actions %that appear to come from the same distribution as the training data. These sequences of actions are then fed to our custom software via configuration files, which replicate those behaviors on end devices. Notably, our models are platform agnostic and could generate behavior data for any emulation software package. In this paper we present our model generation process, software architecture, and an initial evaluation of the fidelity of our models. Our software is currently deployed in a cyber range to help evaluate the efficacy of defensive cyber technologies. We suggest additional ways that the cyber community as a whole can benefit from more realistic user behavior emulation. The data used to train our model, as well as sample configuration files produced by the model, are available at [redacted].
Bandgap Shifting of an Ultra-Thin InGaAs/InP Quantum Well Infrared Photodetector
We demonstrate that SiO(sub 2) cap annealing in the ultra-thin p-type InGaAs/InP quantum wells can be used to produce large blue shifts of the band edge. A substantial bandgap blue shift, as much a 292.5 meV at 900 degrees C have been measured and the value of the bandgap shift can be controlled by the anneal time.
Radiolabeling and in vitro studies of scandium-44 with HOPO chelator for PET imaging
44 Sc has been attracting interest as a clinically viable PET (positron emission tomography) imaging radionuclide due to its high positron branching ratio, 3.97 hour half-life, ideal positron energy of 632 keV and ability to be produced from a generator as well as from a cyclotron. Scandium is also of interest since the coordination is similar to the lanthanides and Ga 3+ that have demonstrated labeling methods. Further, 47 Sc (a potential therapeutic radionuclide) can serve as a matched isotope pair for theranostic applications. While the labeling studies of 44 Sc with DOTA and DOTA derivatives are quite extensive, there is little data on complexation of scandium with the chelator 3,4,3-(LI-1,2-HOPO). This ligand has been used successfully to radiolabel 89 Zr where it was shown to have high stability in vivo. Additionally, preliminary studies with 86 Y, and non-radioactive scandium show promising complexation constants similar to those found with DOTA derivatives. One major advantage of using HOPO is that radiolabeling can be achieved at room temperature while DOTA requires elevated temperatures, that may be detrimental when radiolabeling biomolecules. Radiolabeling of generator produced 44 Sc versus cyclotron produced 44 Sc is also under investigation as there are currently several different post-elution purification methods of generator 44 Sc, and the potential radiolabeling complications of these eluents has not yet been studied. This work focuses on radiolabeling HOPO with generator produced 44 Sc and the in vitro and radioanalytical evaluation of the resulting complexes.
Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitation Clouds: Results from In-Situ Observations in TRMM Field Campaigns
In this study, we report on the evolution of particle size distributions (PSDs) and habits as measured during slow, Lagrangian-type spiral descents through deep subtropical and tropical cloud layers in Florida, Brazil, and Kwajalein, Marshall Islands, most of which were precipitating. The objective of the flight patterns was to learn more about how the PSDs evolved in the vertical and to obtain information of the vertical structure of microphysical properties. New instrumentation yielding better information on the concentrations of particles in the size (D) range between 0.2 and 2 cm, as well as improved particle imagery, produced more comprehensive observations for tropical stratiform precipitation regions and anvils than have been available previously. Collocated radar observations provided additional information on the vertical structure of the cloud layers sampled. Most of the spirals began at cloud top, with temperatures (T) as low as -50 C, and ended at cloud base or below the melting layer (ML). The PSDs broadened from cloud top towards cloud base, with the largest particles increasing in size from several millimeters at cloud top to one centimeter or larger towards cloud base. Some continued growth was noted in the upper part of the ML. Concentrations of particles less than 1 mm in size decreased with decreasing height. The result was a consistent change in the PSDs in the vertical. Similarly, systematic changes in the size dependence of the particle cross-sectional area was noted with decreasing height. Aggregation-as ascertained from both the changes in the PSDs and evolution of particle habits as observed in high detail with the cloud particle imager (CPI) probe-was responsible for these trends. The PSDs were generally well-represented by gamma distributions of the form N = N0 gamma D microns e- lambda gamma D that were fitted to the PSDs over 1-km horizontal intervals throughout the spirals. The intercept (N0 gamma), slope (lambda gamma), and dispersion (microns) values were derived for each PSD. Exponential curves (N = N0e- lambdaD; micron = 0) were also fitted to the distributions. The lambda gamma values for given spirals varied systematically with temperature as did the values of lambda (exponential), and the data generally conformed to values found in previous studies involving exponential fits to size distributions in mid-latitude frontal and cirrus layers. Considerable variability often noted in the PSD properties during the loops of individual spirals was manifested primarily in large changes in N0 gamma and N0, but micron, lambda gamma and lambda remained fairly stable. Temperature is not found to be the sole factor controlling lambda gamma or lambda but is a primary one. Direct relationships were found between lambda gamma and N0 gamma or lambda gamma and micron for the gamma distributions and lambda and N0 for the exponential. The latter relationship was not found as distinctly in earlier studies; observed PSDs in this study had better fidelity with less scatter. The micron values changed monotonically with T over the range of temperatures and were directly related to N0 gamma or lambda gamma, thereby reducing the number of variables in the PSD functional equation to two. In the upper part of the ML, N0 and lambda continued to decrease, and in the lower part these values began to increase as the largest particles melted. We developed general expressions relating various bulk microphysical, radar, and radiative transfer-related variables to N0 gamma and lambda gamma, useful for both tropical and mid-latitude clouds. These relationships facilitate the specification of a number of bulk properties in cloud and climate models. The results presented in this paper apply best to temperatures between 0 and -40 C, for which the measured radar reflectivities fall in the range of 0 to 25 dBZe.
Algorithmic and theoretical results on computation of incompressible viscous flows by finite element methods
Primitive variable as well as streamfunction-vorticity and pure streamfunction formulations are discussed. For the primitive variable case alternative choices of the viscous stress term are shown to produce natural boundary conditions which are well suited for matching to various far field conditions. For the other cases recent analytical results, including error estimates are described, and an optical algorithm for pressure recovery as well as treatment for multiply connected domains are given.
Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output
Emulation through pattern scaling is a well-established method of rapidly producing climate fields (like temperature or precipitation) from existing Earth System Model (ESM) output that, while inaccurate, is often useful for a variety of downstream purposes. Conducting pattern scaling has historically been a laborious process, in large part due to the increasing volume of ESM output data that has often required downloading and storing locally to train on. Here we describe the Pangeo-Enabled ESM Pattern Scaling (PEEPS) dataset, a repository of trained annual and monthly patterns from CMIP6 outputs. This manuscript describes and validates these updated patterns so that users can save effort calculating and reporting error statistics in manuscripts focused on the use of patterns. The trained patterns are available as NetCDF files on Zenodo for ease of use in the impact community, and are reproducible with the code provided via GitHub in both Jupyter notebook and Python script formats. Because all training data for the PEEPS data set is cloud-based, users do not need to download and house the ESM output data to reproduce the patterns in the zenodo archive, should that be more efficient. Validating the PEEPS data set on the CMIP6 archive for annual and monthly temperature, precipitation, and near-surface relative humidity, pattern scaling performs well over a variety of future scenarios except for regions in which there are strong, potentially nonlinear climate feedbacks. Although pattern scaling is normally conducted on annual mean ESM output data, it works equally well on monthly mean ESM output data. We identify several downstream applications of the PEEPS data set, including impacts assessment and evaluating certain types of Earth system uncertainties.
SNC Meteorites, Organic Matter and a New Look at Viking
Recently, evidence has begun to grow supporting the possibility that the Viking GC-MS would not have detected certain carboxylate salts that could have been present as metastable oxidation products of high molecular weight organic species. Additionally, despite the instrument's high sensitivity, the possibility had remained that very low levels of organic matter, below the instrument's detection limit, could have been present. In fact, a recent study indicates that the degradation products of several million microorganisms per gram of soil on Mars would not have been detected by the Viking GC-MS. Since the strength of the GC-MS findings was considered enough to dismiss the biology packet, particularly the LR results, any subsequent evidence suggesting that organic molecules may in fact be present on the Martian surface necessitates a re-evaluation of the Viking LR data. In addition to an advanced mass spectrometer to look for isotopic signatures of biogenic processes, future lander missions will include the ability to detect methane produced by methanogenic bacteria, as well as techniques based on biotechnology. Meanwhile, the identification of Mars samples already present on Earth in the form of the SNC meteorites has provided us with the ability to study samples of the Martian upper crust a decade or more in advance of any planned sample return missions. While contamination issues are of serious concern, the presence of indigenous organic matter in the form of polycyclic aromatic hydrocarbons has been detected in the Martian meteorites ALH84001 and Nakhla, while there is circumstantial evidence for carbonaceous material in Chassigny. The radiochronological ages of these meteorites are 4.5 Ga, 1.3 Ga, and 165 Ma respectively representing a span of time in Earth history from the earliest single-celled organisms to the present day. Given this perspective on organic material, a biological interpretation to the Viking LR results can no longer be ruled out. In the LR experiment, a solution containing C-14 labeled organic compounds was injected into soil samples. The detection of radioactivity in the overhead space would indicate that one or more of the substrates had been chemically converted into a carbon-containing gas. To serve as a control, some samples were heated enough to destroy most known terrestrial microbes so that an indication for life would be a positive response from unheated samples and a negative response from heated samples. On Mars, the LR results had met minimum criteria for a biological interpretation but due to the GC-MS results, the LR responses were later attributed to putative soil inorganic oxidants. Since the time of Viking, studies have been carried out with the objective of determining an oxidant or combination of oxidants that might exist on Mars and have produced the observed kinetics of the LR response. To date, no such agent has been found that produces all aspects of the LR results on Mars. While the above considerations in no way imply the existence of life forms at the two Viking landing sites, inorganic and biological explanations for the Viking LR data should now be considered equally plausible until more complete studies of the Martian surface are carried out. Therefore, in light of the SNC meteorites data and their implications for the possibility of organic matter near or on the Martian surface the Viking biology experiments should thus be seen, not as failures for their inability to provide unambiguous evidence for or against Martian life, but as a foundation for the development of future life-detection instruments. Additional information is contained in the original extended abstract.
Hyporheic‐Zone Processes and Stream Oxygen Dynamics: Insights From a Multiscale Reactive Transport Model
Aquatic ecosystem metabolism encapsulates the daily fixation (gross primary production, GPP d ) and mineralization (ecosystem respiration, ER d ) of organic carbon. In fluvial systems, these are commonly estimated by inverse solutions to field observations using a model that describes oxygen concentrations varying in the water column in response to metabolic fluxes and air‐water gas exchange controlled by a rate coefficient (K 600 ). The most common conceptual model is the single‐station metabolism (SSM) model. The simplicity and flexibility of this conceptualization make it attractive; however, it implicitly assumes that all the processes that consume oxygen in fluvial systems can be lumped into a bulk estimate of respiration with poorly understood consequences for estimates of GPP d , ER d , and K 600 . Here, we focus on the implications of using SSM conceptualization when estimating metabolic fluxes from oxygen dynamics in channels where hyporheic exchange occurs. We use a new multiscale numerical model for reactive transport in streams that represents hyporheic exchange and streambed heterotrophic respiration. Nondimensionalization of this model reveals dimensionless groups that collectively control oxygen dynamics. Numerical experiments offer a mechanistic understanding of the impacts of hyporheic exchange on diel oxygen dynamics revealing that potential biases arise from neglecting mass transfer limitations. Specifically, we found that hyporheic exchange significantly affects diel oxygen dynamics, even for nonreactive streambed sediments. Moreover, while the SSM performs well in many situations, we find conditions where significant bias is produced by hyporheic exchange, even when oxygen data are well‐fitted. These situations pose a major challenge in the interpretation of metabolism assessment estimates.
Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks
Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.
Open and Close-Packed, Shape-Engineered Polygonal Nanoparticle Metamolecules with Tailorable Fano Resonances
In this study, a top-down lithographic patterning and deposition process is reported for producing nanoparticles (NPs) with well-defined sizes, shapes, and compositions that are often not accessible by wet-chemical synthetic methods. These NPs are ligated and harvested from the substrate surface to prepare colloidal NP dispersions. Using a template-assisted assembly technique, fabricated NPs are driven by capillary forces to assemble into size- and shape-engineered templates and organize into open or close-packed multi-NP structures or NP metamolecules. The sizes and shapes of the NPs and of the templates control the NP number, coordination, interparticle gap size, disorder, and location of defects such as voids in the NP metamolecules. The plasmonic resonances of polygonal-shaped Au NPs are exploited to correlate the structure and optical properties of assembled NP metamolecules. Comparing open and close-packed architectures highlights that introduction of a center NP to form close-packed assemblies supports collective interactions, altering magnetic optical modes and multipolar interactions in Fano resonances. Decreasing the distance between NPs strengthens the plasmonic coupling, and the structural symmetries of the NP metamolecules determine the orientation-dependent scattering response.
Metal‐Organic Frameworks for C6 Alkane Separation
Abstract The separation of alkane isomers is an important yet challenging process in the petrochemical industry. Being a crucial step to produce premium gasoline components as well as optimum ethylene feed, the current industrial separation by distillation is extremely energy intensive. Adsorptive separation based on zeolite is limited by insufficient adsorption capacity. Metal‐organic frameworks (MOFs) hold enormous promise as alternative adsorbents due to their diverse structural tunability and exceptional porosity. Precise control of their pore geometry/dimensions has led to superior performance. In this minireview, we highlight the recent progresses in developing MOFs for the separation of C6 alkane isomers. Representative MOFs are reviewed based on their separation mechanisms. Emphasis is put on the material design rationale for achieving optimal separation capability. Finally, we briefly discuss the existing challenges, possible solutions, and future directions of this important field.
Metal–Organic Frameworks for C6 Alkane Separation
The separation of alkane isomers is an important yet challenging process in the petrochemical industry. Being a crucial step to produce premium gasoline components as well as optimum ethylene feed, the current industrial separation by distillation is extremely energy intensive. Adsorptive separation based on zeolite is limited by insufficient adsorption capacity. Metal-organic frameworks (MOFs) hold enormous promise as alternative adsorbents due to their diverse structural tunability and exceptional porosity. Precise control of their pore geometry/dimensions has led to superior performance. In this minireview, we highlight the recent progresses in developing MOFs for the separation of C6 alkane isomers. Representative MOFs are reviewed based on their separation mechanisms. Emphasis is put on the material design rationale for achieving optimal separation capability. Lastly, we briefly discuss the existing challenges, possible solutions, and future directions of this important field.
A research agenda for the science of actionable knowledge: Drawing from a review of the most misguided to the most enlightened claims in the science-policy interface literature
Linking science with action affords a prime opportunity to leverage greater societal impact from research and increase the use of evidence in decision-making. Success in these areas depends critically upon processes of producing and mobilizing knowledge, as well as supporting and making decisions. For decades, scholars have idealized and described these social processes in different ways, resulting in numerous assumptions that now variously guide engagements at the interface of science and society. We systematically catalog these assumptions based on prior research on the science-policy interface, and further distill them into a set of 26 claims. We then elicit expert perspectives (n = 16) about these claims to assess the extent to which they are accurate or merit further examination. Out of this process, we construct a research agenda to motivate future scientific research on actionable knowledge, prioritizing areas that experts identified as critical gaps in understanding of the science-society interface. The resulting agenda focuses on how to define success, support intermediaries, build trust, and evaluate the importance of consensus and its alternatives – all in the diverse contexts of science-society-decision-making interactions. We further raise questions about the centrality of knowledge in these interactions, discussing how a governance lens might be generative of efforts to support more equitable processes and outcomes. We offer these suggestions with hopes of furthering the science of actionable knowledge as a transdisciplinary area of inquiry.
Digital polycrystalline microstructure generation using diffusion probabilistic models
Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. Here, this work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.