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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structural evolution during solution-based synthesis of Li 7 P 3 S 11 solid electrolyte by synchrotron X-ray total scattering

Solution-based synthesis has emerged as a promising approach for scalable and efficient synthesis of solid electrolytes (SEs). However, the ionic conductivity of solution-synthesized SEs is much lower than that of SEs synthesized by solid state approaches. The structural evolution during the synthesis of SEs is crucial to explaining the decreased ionic conductivities but has not been well understood. Here, in this work, we use synchrotron X-ray total scattering combined with pair distribution function (PDF) analysis to understand the structural evolution during annealing of the solution-processed precursor for the synthesis of Li 7 P 3 S 11 SEs. Combining Bragg diffraction and diffuse scattering, X-ray total scattering enables detection of structural information including phase transition and crystallinity. The results show that the formation of Li 7 P 3 S 11 glass-ceramic starts at 230 °C. Increasing the annealing temperature helps to improve the crystallinity, but the degree of crystallization is still lower than 70.3% even after annealing at 290 °C when thermal decomposition of Li 7 P 3 S 11 to Li 4 P 2 S 6 has occurred. PDF analysis also revealed the existence of acetonitrile used in the solution-based synthesis in the SEs after annealing at high temperatures. The results rationalize the synthesis–property relationship, explain why annealing at 290 °C provides the highest ionic conductivity at the studied temperatures due to an optimal balance for achieving a high crystallinity while suppressing severe decomposition of Li 7 P 3 S 11 , and provide novel insights to develop approaches for synthesizing superior Li 7 P 3 S 11 SEs on a large scale.

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

Structure and dynamics of ethane confined in silica nanopores in the presence of CO 2

Fundamental understanding of the subcritical/supercritical behavior of key hydrocarbon species inside nano-porous matrices at elevated pressure and temperature is less developed compared to bulk fluids, but this knowledge is of great importance for chemical and energy engineering industries. This study explores in detail the structure and dynamics of ethane (C 2 H 6 ) fluid confined in silica nanopores, with a focus on the effects of pressure and different ratios of C 2 H 6 and CO 2 at non-ambient temperature. Quasi-elastic neutron scattering (QENS) experiments were carried out for the pure C 2 H 6 , C 2 H 6 :CO 2 = 3:1, and 1:3 mixed fluids confined in 4-nm cylindrical silica pores at three different pressures (30 bars, 65 bars, and 100 bars) at 323 K. Two Lorentzian functions were required to fit the spectra, corresponding to fast and slow translational motions. No localized motions (rotations and vibrations) were detected. Higher pressures resulted in hindrances of the diffusivity of C 2 H 6 molecules in all systems investigated. Pore size was found to be an important factor, i.e., the dynamics of confined C 2 H 6 is more restricted in smaller pores compared to the larger pores used in previous studies. Molecular dynamics simulations were performed to complement the QENS experiment at 65 bars, providing supportive structure information and comparable dynamic information. The simulations indicate that CO 2 molecules are more strongly attracted to the pore surface compared to C 2 H 6 . The C 2 H 6 molecules interacting with or near the pore surface form a dense first layer (L1) close to the pore surface and a second less dense layer (L2) extending into the pore center. Both the experiments and simulations revealed the role that CO 2 molecules play in enhancing C 2 H 6 diffusion (“molecular lubrication”) at high CO 2 :C 2 H 6 ratios. The energy scales of the two dynamic components, fast and slow, quantified by both techniques, are in very good agreement. Herein, the simulations identified the fast component as the main contributor to the dynamics. Molecule motions in the L2 region are mostly responsible for the dynamics (fast and slow) that can be detected by the instrument.

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

Dispersion of surface-modified, aggregated, fumed silica in polymer nanocomposites

Surface modification of model silica to enhance compatibility in nanocomposites has been widely studied. In addition to model spherical silica, several authors have investigated the impact of surface conditions on compatibility in commercial aggregated carbon black and silica. Here, dispersion is investigated for a series of nanocomposites produced from commercially modified fumed silica mixed with styrene butadiene rubber, polystyrene, and polydimethylsiloxane. Surface modification includes variation in surface hydroxyl content, siloxane, and silane treatment. Qualitatively, hydroxyl groups on the silica surface are considered incompatible with non-polar polymers, while methyl groups are compatible with oleophilic polymers. X-ray scattering was used to analyze the filler aggregate structure before and after dispersion, and the second virial coefficient was used to quantify nanodispersion. The content of surface moieties was determined from Fourier-transform infrared spectroscopy. It is observed that modified silica can display mean field or specific interactions as reflected by the presence of a correlation peak in x-ray scattering. For systems with specific interactions, a critical ordering concentration is observed related to the free energy change for structuring. A van der Waals model was used to model the second virial coefficient as a function of accumulated strain, yielding the excluded volume and an energetic term. The excluded volume could be predicted from the structural information, and the bound polymer layer was directly related to the surface methyl content, whereas the energetic term was found to synergistically depend on both the methyl and hydroxyl surface content.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-resolution CH stretch spectroscopy of jet-cooled cyclopentyl radical: First insights into equilibrium structure, out-of-plane puckering, and IVR dynamics

First, high-resolution sub-Doppler infrared spectroscopic results for cyclopentyl radical (C 5 H 9 ) are reported on the α-CH stretch fundamental with suppression of spectral congestion achieved by adiabatic cooling to T rot ≈ 19(4) K in a slit jet expansion. Surprisingly, cyclopentyl radical exhibits a rotationally assignable infrared spectrum, despite 3N – 6 = 36 vibrational modes and an upper vibrational state density (ρ ≈ 40–90 #/cm –1 ) in the critical regime (ρ ≈ 100 #/cm –1 ) necessary for onset of intramolecular vibrational relaxation (IVR) dynamics. Such high-resolution data for cyclopentyl radical permit detailed fits to a rigid-rotor asymmetric top Hamiltonian, initial structural information for ground and vibrationally excited states, and opportunities for detailed comparison with theoretical predictions. Specifically, high level ab initio calculations at the coupled-cluster singles, doubles, and perturbative triples (CCSD(T))/ANO0, 1 level are used to calculate an out-of-plane bending potential, which reveals a C 2 symmetry double minimum 1D energy surface over a C 2v transition state. Here, the inversion barrier [V barrier ≈ 3.7(1) kcal/mol] is much larger than the effective moment of inertia for out-of-plane bending, resulting in localization of the cyclopentyl wavefunction near its C 2 symmetry equilibrium geometry and tunneling splittings for the ground state too small (<1 MHz) to be resolved under sub-Doppler slit jet conditions. The persistence of fully resolved high-resolution infrared spectroscopy for such large cyclic polyatomic radicals at high vibrational state densities suggests a “deceleration” of IVR for a cycloalkane ring topology, much as low frequency torsion/methyl rotation degrees of freedom have demonstrated a corresponding “acceleration” of IVR processes in linear hydrocarbons.

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

Synergy of semiempirical models and machine learning in computational chemistry

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
DOE OSTI2021

Structural basis for differential recognition of phosphohistidine-containing peptides by 1-pHis and 3-pHis monoclonal antibodies

In 2015, monoclonal antibodies (mAbs) that selectively recognize the 1-pHis or 3-pHis isoforms of phosphohistidine were developed by immunizing rabbits with degenerate Ala/Gly peptides containing the nonhydrolyzable phosphohistidine (pHis) analog- phosphotriazolylalanine (pTza). Here, we report structures of five rabbit mAbs bound to cognate pTza peptides: SC1-1 and SC50-3 that recognize 1-pHis, and their 3-pHis–specific counterparts, SC39-4, SC44-8, and SC56-2. These cocrystal structures provide insights into the binding modes of the pTza phosphate group that are distinct for the 1- and 3-pHis mAbs with the selectivity arising from specific contacts with the phosphate group and triazolyl ring. The mode of phosphate recognition in the 3-pHis mAbs recapitulates the Walker A motif, as present in kinases. The complementarity-determining regions (CDRs) of four of the Fabs interact with the peptide backbone rather than peptide side chains, thus conferring sequence independence, whereas SC44-8 shows a proclivity for binding a GpHAGA motif mediated by a sterically complementary CDRL3 loop. Specific hydrogen bonding with the triazolyl ring precludes recognition of pTyr and other phosphoamino acids by these mAbs. Kinetic binding experiments reveal that the affinity of pHis mAbs for pHis and pTza peptides is submicromolar. Bound pHis mAbs also shield the pHis peptides from rapid dephosphorylation. The epitope–paratope interactions illustrate how these anti-pHis antibodies are useful for a wide range of research techniques and this structural information can be utilized to improve the specificity and affinity of these antibodies toward a variety of pHis substrates to understand the role of histidine phosphorylation in healthy and diseased states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

From antenna to reaction center: Pathways of ultrafast energy and charge transfer in photosystem II

The photosystem II core complex (PSII-CC) is the smallest subunit of the oxygenic photosynthetic apparatus that contains core antennas and a reaction center, which together allow for rapid energy transfer and charge separation, ultimately leading to efficient solar energy conversion. However, there is a lack of consensus on the interplay between the energy transfer and charge separation dynamics of the core complex. Here, we report the application of two-dimensional electronic-vibrational (2DEV) spectroscopy to the spinach PSII-CC at 77 K. The simultaneous temporal and spectral resolution afforded by 2DEV spectroscopy facilitates the separation and direct assignment of coexisting dynamical processes. Our results show that the dominant dynamics of the PSII-CC are distinct in different excitation energy regions. By separating the excitation regions, we are able to distinguish the intraprotein dynamics and interprotein energy transfer. Additionally, with the improved resolution, we are able to identify the key pigments involved in the pathways, allowing for a direct connection between dynamical and structural information. Specifically, we show that C505 in CP43 and the peripheral chlorophyll Chlz D1 in the reaction center are most likely responsible for energy transfer from CP43 to the reaction center.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Torsional force microscopy of van der Waals moirés and atomic lattices

In a stack of atomically thin van der Waals layers, introducing interlayer twist creates a moiré superlattice whose period is a function of twist angle. Changes in that twist angle of even hundredths of a degree can dramatically transform the system’s electronic properties. Setting a precise and uniform twist angle for a stack remains difficult; hence, determining that twist angle and mapping its spatial variation is very important. Techniques have emerged to do this by imaging the moiré, but most of these require sophisticated infrastructure, time-consuming sample preparation beyond stack synthesis, or both. In this work, we show that torsional force microscopy (TFM), a scanning probe technique sensitive to dynamic friction, can reveal surface and shallow subsurface structure of van der Waals stacks on multiple length scales: the moirés formed between bi-layers of graphene and between graphene and hexagonal boron nitride (hBN) and also the atomic crystal lattices of graphene and hBN. In TFM, torsional motion of an Atomic Force Microscope (AFM) cantilever is monitored as it is actively driven at a torsional resonance while a feedback loop maintains contact at a set force with the sample surface. TFM works at room temperature in air, with no need for an electrical bias between the tip and the sample, making it applicable to a wide array of samples. It should enable determination of precise structural information including twist angles and strain in moiré superlattices and crystallographic orientation of van der Waals flakes to support predictable moiré heterostructure fabrication.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Nanoscale dynamics of Dynamin 1 helices reveals squeeze-twist deformation mode critical for membrane fission

Dynamin 1 (Dyn1) GTPase, a principal driver of membrane fission during synaptic endocytosis, self-assembles into short mechanoactive helices cleaving the necks of endocytic vesicles. While structural information about Dyn1 helix is abundant, little is known about the nanoscale dynamics of the helical scaffolding at the moment of fission, complicating mechanistic understanding of Dyn1 action. To address the role of the helix dynamics in fission, we used High-Speed Atomic Force Microscopy (HS-AFM) and fluorescence microscopy to track and compare the spatiotemporal characteristics of the helices formed by wild-type Dyn1 and its K44A mutant impaired in GTP hydrolysis on minimal lipid membrane templates. In the absence of nucleotide, membrane-bound WT Dyn1 and K44A Dyn1 self-assembled into tubular protein scaffolding of similar diameter encaging the lipid bilayer. In both cases, the GTP addition caused scaffold constriction coupled with formation of 20 to 30 nm nanogaps in the protein coverage. While both proteins reached scaffold diameters characteristic for membrane superconstriction causing fission, the fission was detected only with WT Dyn1. We associated the fission activity with the dynamic evolution of the nanogaps: K44A Dyn1 gaps were static, while WT Dyn1 gaps actively evolved via repetitive nonaxisymmetric constriction-bending deformations caused by localized GTP hydrolysis. Modeling of the deformations implicated filament twist as an additional deformation mode which combines with superconstriction to facilitate membrane fission. Our results thus show that the dynamics of the Dyn1 helical scaffold goes beyond radial constriction and involves nonaxisymmetric deformations, where filament twist emerges as a critical driver of membrane fission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

De novo design of D-peptide ligands: Application to influenza virus hemagglutinin

D-peptides hold great promise as therapeutics by alleviating the challenges of metabolic stability and immunogenicity in L-peptides. However, current D-peptide discovery methods are severely limited by specific size, structure, and the chemical synthesizability of their protein targets. Here, we describe a computational method for de novo design of D-peptides that bind to an epitope of interest on the target protein using Rosetta’s hotspot-centric approach. The approach comprises identifying hotspot sidechains in a functional protein–protein interaction and grafting these side chains onto much smaller structured peptide scaffolds of opposite chirality. The approach enables more facile design of D-peptides and its applicability is demonstrated by design of D-peptidic binders of influenza A virus hemagglutinin, resulting in identification of multiple D-peptide lead series. The X-ray structure of one of the leads at 2.38 Å resolution verifies the validity of the approach. This method should be generally applicable to targets with detailed structural information, independent of molecular size, and accelerate development of stable, peptide-based therapeutics.

Science & Technology - Other Topics↗
DOE OSTI2021

Transmission x-ray microscopy and its applications in battery material research—a short review

Abstract Transmission x-ray microscopy (TXM), which can provide morphological and chemical structural information inside of battery component materials at tens of nanometer scale, has become a powerful tool in battery research. This article presents a short review of the TXM, including its instrumentation, battery research applications, and the practical sample preparation and data analysis in the TXM applications. A brief discussion on the challenges and opportunities in the TXM applications is presented at the end.

25 ENERGY STORAGE↗
DOE OSTI2020

Photo-z outlier self-calibration in weak lensing surveys

Calibrating photometric redshift errors in weak lensing surveys with external data is extremely challenging. In this work, we show that both Gaussian and outlier photo-z parameters can be self-calibrated from the data alone. This comes at no cost for the neutrino masses, curvature and dark energy equation of state w0, but with a 65% degradation when both w0 and wa are varied. We perform a realistic forecast for the Vera Rubin Observatory (VRO) Legacy Survey of Space and Time (LSST) 3× 2 analysis, combining cosmic shear, projected galaxy clustering and galaxy - galaxy lensing. We confirm the importance of marginalizing over photo-z outliers. We examine a subset of internal cross-correlations, dubbed "null correlations", which are usually ignored in 3× 2 analyses. Despite contributing only ~ 10% of the total signal-to-noise, these null correlations improve the constraints on photo-z parameters by up to an order of magnitude. Using the same galaxy sample as sources and lenses dramatically improves the photo-z uncertainties too. Together, these methods add robustness to any claim of detected new Physics, and reduce the statistical errors on cosmology by 15% and 10% respectively. Finally, including CMB lensing from an experiment like Simons Observatory or CMB-S4 improves the cosmological and photo-z posterior constraints by about 10%, and further improves the robustness to systematics. To give intuition on the Fisher forecasts, we examine in detail several toy models that explain the origin of the photo-z self-calibration. Our Fisher code LaSSI (Large-Scale Structure Information), which includes the effect of Gaussian and outlier photo-z, shear multiplicative bias, linear galaxy bias, and extensions to LCDM, is publicly available at \href{https://github.com/EmmanuelSchaan/LaSSI}{https://github.com/EmmanuelSchaan/LaSSI}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗
DOE OSTI2025

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantum chemically calculated Abraham parameters for quantifying and predicting polymer hydrophobicity

The leakage and accumulation of plastic in the environment is a significant and growing problem with numerous detrimental impacts and has led to a push toward the design and development of more environmentally benign materials. To this end, we have developed a quantum chemistry-based model for predicting the mobility of polymer materials from molecular structure. Hydrophobicity is used as a surrogate for mobility given that hydrophobic interactions drive much of the partitioning of contaminants in and out of various environmentally relevant compartments. To model polymer hydrophobicity, we adjusted a previously developed Quantum Chemically Calculated Abraham Parameter model to calculate Abraham parameters of small molecules from molecular structure information. The resulting model predicted the octanol-water partition coefficient (K OW ) of polymer repeating units with a root mean square error (RMSE) of 0.48 (log scale). Additionally, the hydrophobicity of high molecular weight polymer materials was captured through solubility parameters and Nile red staining experiments from the literature and predicted with RMSEs of 1.21 (J/cc) 0.5 and 3.42 nm, respectively. Finally, to test the environmental applicability of the model, the relative adsorption capacity of three polymers was predicted and used to unify sorption isotherms across multiple sorbates and polymer sorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Genomic and transcriptomic characterization of carbohydrate-active enzymes in the anaerobic fungus Neocallimastix cameroonii var. constans

Anaerobic gut fungi effectively degrade lignocellulose in the guts of large herbivores, but there remain a limited number of isolated, publicly available, and sequenced strains that impede our understanding of the role of anaerobic fungi within microbial communities. We isolated and characterized a new fungal isolate, Neocallimastix cameroonii var. constans, providing a transcriptomic and genomic understanding of its ability to degrade diverse carbohydrates. This anaerobic fungal strain was stably cultivated for multiple years in vitro among members of an initial enrichment microbial community derived from goat feces, and it demonstrated the ability to pair with other microbial members, namely, archaeal methanogens to produce methane from lignocellulose. Genomic analysis revealed a higher number of predicted carbohydrate-active enzymes encoded in the N. cameroonii var. constans genome compared to most other sequenced anaerobic fungi. The carbohydrate-active enzyme profile for this isolate contained 660 glycoside hydrolases, 160 carbohydrate esterases, 194 glycosyltransferases, and 85 polysaccharide lyases. Differential gene expression analysis showed the upregulation of thousands of genes (including predicted carbohydrate-active enzymes) when N. cameroonii var. constans was grown on lignocellulose (reed canary grass) compared to less complex substrates, such as cellulose (filter paper), cellobiose, and glucose. AlphaFold was used to predict functions of transcriptionally active yet poorly annotated genes, revealing feruloyl esterases that likely play an important role in lignocellulose degradation by anaerobic fungi. The combination of this strain's genomic and transcriptomic characterization, omics-informed structural prediction, and robustness in microbial co-culture make it a well-suited platform to conduct future investigations into bioprocessing and enzyme discovery.

CAZymes↗
DOE OSTI2024

Improved Three-Dimensional Reconstructions in Electron Ptychography through Defocus Series Measurements

A detailed analysis of ptychography for three-dimensional (3D) phase reconstructions of thick specimens is performed. Here, we introduce multi-focus ptychography, which incorporates a 4D-STEM defocus series to enhance the quality of 3D reconstructions along the beam direction through a higher overdetermination ratio. This method is compared with established multi-slice ptychography techniques, such as conventional ptychography, regularized ptychography, and multi-mode ptychography. Additionally, we contrast multi-focus ptychography with an alternative method that uses virtual optical sectioning through a reconstructed scattering matrix (S-matrix), which offers more precise 3D structure information compared to conventional ptychography. Our findings from multiple 3D reconstructions based on simulated and experimental data demonstrate that multi-focus ptychography surpasses other techniques, particularly in accurately reconstructing the surfaces and interface regions of thick specimens.

3D reconstruction↗