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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Programmable Aggregation of Artificial Cells with DNA Signals

Cell aggregation is a complex behavior, which is closely related to the viability, differentiation, and migration of cells. An effort to create synthetic analogs could lead to considerable advances in cell physiology and biophysics. Rendering and modulating such a dynamic artificial cell system require mechanisms for receiving, transducing, and transmitting intercellular signals, yet effective tools are limited at present. Here we construct synthetic cells from engineered lipids and show their programmable aggregation behaviors using DNA oligonucleotides as a signaling molecule. The artificial cells have transmembrane channels made of DNA origami that are used to recognize and process intercellular signals. We demonstrate that multiple small vesicles aggregate onto a giant vesicle after a transduction of external DNA signals by an intracellular enzyme, and that the small vesicles dissociate when receiving ‘release’ signals. Furthermore, this work provides new possibilities for building synthetic protocells capable of chemical communication and coordination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Lipid sponge droplets as programmable synthetic organelles

Living cells segregate molecules and reactions in various subcellular compartments known as organelles. Spatial organization is likely essential for expanding the biochemical functions of synthetic reaction systems, including artificial cells. Many studies have attempted to mimic organelle functions using lamellar membrane-bound vesicles. However, vesicles typically suffer from highly limited transport across the membranes and an inability to mimic the dense membrane networks typically found in organelles such as the endoplasmic reticulum. Here, we describe programmable synthetic organelles based on highly stable nonlamellar sponge phase droplets that spontaneously assemble from a single-chain galactolipid and nonionic detergents. Due to their nanoporous structure, lipid sponge droplets readily exchange materials with the surrounding environment. In addition, the sponge phase contains a dense network of lipid bilayers and nanometric aqueous channels, which allows different classes of molecules to partition based on their size, polarity, and specific binding motifs. The sequestration of biologically relevant macromolecules can be programmed by the addition of suitably functionalized amphiphiles to the droplets. We demonstrate that droplets can harbor functional soluble and transmembrane proteins, allowing for the colocalization and concentration of enzymes and substrates to enhance reaction rates. Droplets protect bound proteins from proteases, and these interactions can be engineered to be reversible and optically controlled. Our results show that lipid sponge droplets permit the facile integration of membrane-rich environments and self-assembling spatial organization with biochemical reaction systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A targeted opsonization platform for programming innate immunity against rapidly evolving novel viruses.

Recent work has shown that artificial opsonins stimulate the targeted destruction of bacteria by phagocyte immune cells. Artificial opsonization has the potential to direct the innate immune system to target novel antigens, potentially even viral pathogens. Furthermore, the engagement of innate immunity presents a potential solution for the spread of pandemics in a scenario when a vaccine is unavailable or ineffective. Funded by the LDRD late start bioscience pandemic response program, we tested whether artificial opsonins can be developed to target viral pathogens using phage MS2 and a SARS-CoV-2 surrogate. To direct opsonization against these viruses we purified antibody derived viral targeting motifs and attempted the same chemical conjugation strategies that produced bacterial targeting artificial opsonins. However, the viral targeting motifs proved challenging to conjugate using these methods, frequently resulting in precipitation and loss of product. Future studies may be successful with this approach if a smaller and more soluble viral-targeting peptide could be used.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Characterizing the Self-Assembly Properties of Monoolein Lipid Isosteres

Living cells feature lipid compartments which exhibit a variety of shapes and structures that assist essential cellular processes. Many natural cell compartments frequently adopt convoluted nonlamellar lipid architectures that facilitate specific biological reactions. Improved methods for controlling the structural organization of artificial model membranes would facilitate investigations into how membrane morphology affects biological functions. Monoolein (MO) is a single-chain amphiphile which forms nonlamellar lipid phases in aqueous solution and has wide applications in nanomaterial development, the food industry, drug delivery, and protein crystallization. However, even if MO has been extensively studied, simple isosteres of MO, while readily accessible, have seen limited characterization. An improved understanding of how relatively minor changes in lipid chemical structure affect self assembly and membrane topology could instruct the construction of artificial cells and organelles for modeling biological structures and facilitate nanomaterial-based applications. Here, we investigate the differences in self-assembly and large-scale organization between MO and two MO lipid isosteres. We show that replacing the ester linkage between the hydrophilic headgroup and hydrophobic hydrocarbon chain with a thioesther or amide functional group results in the assembly of lipid structures with different phases not resembling those formed by MO. Using light and cryo-electron microscopy, small-angle X-ray scattering, and infrared spectroscopy, we demonstrate differences in the molecular ordering and large-scale architectures of the self-assembled structures made from MO and its isosteric analogues. These results improve our understanding of the molecular underpinnings of lipid mesophase assembly and may facilitate the development of MO-based materials for biomedicine and as model lipid compartments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A microfluidic platform for the synthesis of polymer and polymer-protein-based protocells

In this study, we demonstrate the fabrication of polymersomes, protein-blended polymersomes, and polymeric microcapsules using droplet microfluidics. Polymersomes with uniform, single bilayers and controlled diameters are assembled from water-in-oil-in-water double-emulsion droplets. This technique relies on adjusting the interfacial energies of the droplet to completely separate the polymer-stabilized inner core from the oil shell. Protein-blended polymersomes are prepared by dissolving protein in the inner and outer phases of polymer-stabilized droplets. Cell-sized polymeric microcapsules are assembled by size reduction in the inner core through osmosis followed by evaporation of the middle phase. All methods are developed and validated using the same glass-capillary microfluidic apparatus. This integrative approach not only demonstrates the versatility of our setup, but also holds significant promise for standardizing and customizing the production of polymer-based artificial cells.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dynamic self-assembly of compartmentalized DNA nanotubes

Abstract Bottom-up synthetic biology aims to engineer artificial cells capable of responsive behaviors by using a minimal set of molecular components. An important challenge toward this goal is the development of programmable biomaterials that can provide active spatial organization in cell-sized compartments. Here, we demonstrate the dynamic self-assembly of nucleic acid (NA) nanotubes inside water-in-oil droplets. We develop methods to encapsulate and assemble different types of DNA nanotubes from programmable DNA monomers, and demonstrate temporal control of assembly via designed pathways of RNA production and degradation. We examine the dynamic response of encapsulated nanotube assembly and disassembly with the support of statistical analysis of droplet images. Our study provides a toolkit of methods and components to build increasingly complex and functional NA materials to mimic life-like functions in synthetic cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Synthesis of Novel Hybrid Nanostructures Employing Advanced Structural Characterization Techniques

This proposal was for carrying out research on creating novel hybrid nanostructures with novel and interesting functional properties with the help of advanced structural on adding 0.001, 0.01 and 0.02 mg of Au nanoparticles. From the reflectivity measurements, and the intensities of the various order diffraction peaks from the multilayers, we were able to reconstruct the electron density profiles of the films normal to their surfaces. characterization techniques. There were 3 components to the research: (1) seeing if new hybrid nanostructures could be synthesized from Au nanoparticles and lipid multilayers (2) making artificial cells from Galactopyranose-Derived Single-Chain Amphiphiles and sponge phases of lipid/amphiphile mixtures, and (3) characterizing the pair distribution function for complex nanoparticles of melanin to understand/predict the structural colors seen in aggregates of such nanoparticles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Integration of Solid Oxide Fuel Cell Systems Into Artificial Intelligence Data Centers

This report presents the results of a techno-economic analysis (TEA) that evaluates the economic benefits of integrating solid oxide fuel cell (SOFC) systems with artificial intelligence (AI) data centers. The analysis was completed in two phases: a scoping-level analysis was performed to identify impactful integration opportunities, followed by a more detailed TEA. Results show that, due to their modularity, SOFC can meet the 99.999% availability requirement of data centers with minimal additional costs. Heat integration via absorption chillers decreases data center electricity consumption at the tradeoff of increased water consumption. Higher SOFC exhaust temperatures are important for achieving larger electricity savings. Finally, power electronics integration with SOFC direct current electricity can reduce electricity consumption by 9 percent and reduce water consumption by 6.4 percent.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Long-life LiNi 0.5 Mn 1.5 O 4 /graphite lithium-ion cells with an artificial graphite-electrolyte interface

The high operating voltage of a spinel LiNi 0.5 Mn 1.5 O 4 (LNMO) cathode leads to electrolyte decomposition and accelerated deterioration of the electrode/electrolyte interface in lithium-ion cells. Aggressive side reactions prevent long-term cycling and hinder its practical application. Advanced characterization shows that the high-voltage operation makes the graphite anode react with the electrolyte, particularly with the lithium salt in the electrolyte, resulting in massive active lithium inventory loss and capacity fade. The side reactions at the graphite are identified as the main culprit for the LNMO/graphite full cell decay. Here, we demonstrate that an artificial solid-electrolyte interface layer consisting of an ultra-thin Al 2 O 3 film, generated via atomic layer deposition (ALD) at the graphite surface, can successfully isolate the graphite from the electrolyte and inhibit the undesired reactions. Consequently, the capacity and cycling stability of the LNMO/graphite cell are drastically improved. After 300 cycles, the capacity retention of LNMO/graphite cell increases from 57.7% to 98.6% with the ALD-coated graphite. Furthermore, our findings provide valuable insights into the LNMO/graphite cell degradation and suggest a simple and robust, yet highly effective, approach for the practical viability of high-voltage lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Motility of Synthetic Cells from Engineered Lipids

Synthetic cells are artificial systems that resemble natural cells. Significant efforts have been made over the years to construct synthetic protocells that can mimic biological mechanisms and perform various complex processes. These include compartmentalization, metabolism, energy supply, communication, and gene reproduction. Cell motility is also of great importance, as nature uses elegant mechanisms for intracellular trafficking, immune response, and embryogenesis. In this review, we discuss the motility of synthetic cells made from lipid vesicles and relevant molecular mechanisms. Synthetic cell motion may be classified into surface-based or solution-based depending on whether it involves interactions with surfaces or movement in fluids. Collective migration behaviors have also been demonstrated. The swarm motion requires additional mechanisms for intercellular signaling and directional motility that enable communication and coordination among the synthetic vesicles. In addition, intracellular trafficking for molecular transport has been reconstituted in minimal cells with the help of DNA nanotechnology. These efforts demonstrate synthetic cells that can move, detect, respond, and interact. Finally, we envision that new developments in protocell motility will enhance our understanding of biological processes and be instrumental in bioengineering and therapeutic applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr 0.78 Y 0.22 O 3-δ

Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr 0.78 Y 0.22 O 3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Here, our results reproduce the experimentally observed non-monotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration—two mechanisms that influence the lattice expansion in opposite directions. The importance of this work lies in its detailed demonstration of how advanced computational techniques can accurately capture complex environmental effects, providing a valuable framework for modeling similar phenomena in a variety of material systems and applications.

Proton conducting fuel cell↗
DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗
DOE OSTI2020

A molecular tandem cell for efficient solar water splitting

Artificial photosynthesis provides a way to store solar energy in chemical bonds. Achieving water splitting without an applied external potential bias provides the key to artificial photosynthetic devices. We describe here a tandem photoelectrochemical cell design that combines a dye-sensitized photoelectrosynthesis cell (DSPEC) and an organic solar cell (OSC) in a photoanode for water oxidation. When combined with a Pt electrode for H 2 evolution, the electrode becomes part of a combined electrochemical cell for water splitting, 2H 2 O → O 2 + 2H 2 , by increasing the voltage of the photoanode sufficiently to drive bias-free reduction of H + to H 2 . The combined electrode gave a 1.5% solar conversion efficiency for water splitting with no external applied bias, providing a mimic for the tandem cell configuration of PSII in natural photosynthesis. The electrode provided sustained water splitting in the molecular photoelectrode with sustained photocurrent densities of 1.24 mA/cm 2 for 1 h under 1-sun illumination with no applied bias.

14 SOLAR ENERGY↗
DOE OSTI2020

In Vivo Molecular Insights into Syntrophic Geobacter Aggregates

Direct interspecies electron transfer (DIET) has been considered as a novel and highly efficient strategy in both natural anaerobic environments and artificial microbial fuel cells. A syntrophic model consisting of Geobacter metallireducens and Geobacter sulfurreducens was studied in this work. We conducted in vivo molecular mapping of the outer surface of the syntrophic community as the interface of nutrients and energy exchange. System for Analysis at the Liquid Vacuum Interface combined with time-of-flight secondary ion mass spectrometry was employed to capture the molecular distribution of syntrophic Geobacter communities in the living and hydrated state. Principal component analysis with selected peaks revealed that syntrophic Geobacter aggregates were well differentiated from other control samples, including syntrophic planktonic cells, pure cultured planktonic cells, and single population biofilms. Our in vivo imaging indicated that a unique molecular surface was formed. Specifically, aromatic amino acids, phosphatidylethanolamine components, and large water clusters were identified as key components that favored the DIET of syntrophic Geobacter aggregates. Moreover, the molecular changes in depths of the Geobacter aggregates were captured using dynamic depth profiling. Our findings shed new light on the interface components supporting electron transfer in syntrophic communities based on in vivo molecular imaging.

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

Perspectives on biorefineries in microbial production of fuels and chemicals

Microbes drive our complex biosphere by regulating the global ecosystem through cycling elements and energy. Humankind has barely begun leveraging this biotransformation capacity to impact global economies and ecologies. Advances in genetic engineering, molecular analysis, metabolic flux modeling, microbial consortia/biome mapping and engineering, cell-free bioproduction, artificial intelligence/machine learning and the ever expanding -omics frontiers have set the stage for paradigm changes to how humankind produces, uses, transforms, and recycles carbon and energy through microbes. Harnessing this enormous potential could drive a global bioeconomy and manage carbon at a planetary level but requires understanding and application at a grand scale across a broad range of science and engineering disciplines. The penultimate manifestation of these advances is the “bio-refinery”, which is often referenced, but is a long way from being fully developed as a global carbon management platform. Broadening the feed stocks, processing operations, and product portfolio to a sequential cascade optimizing the conversion as a whole instead of limited outputs could greatly advance deployment and stability of a bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2024

Automated 3D cytoplasm segmentation in soft X-ray tomography

Cells’ structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma

Abstract Background Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients. Objectives To determine if artificial intelligence and machine learning might help to stratify patients with cSCC by risk using more than solely clinical and histopathological factors. Methods We retrieved a retrospective cohort of 104 patients whose cSCCs had been excised with clear margins. Clinical and histopathological risk factors were evaluated. Haematoxylin and eosin-stained slides were scanned and analysed by an algorithm based on the stacked predictive sparse decomposition technique. Cellular morphometric biomarkers (CMBs) were identified via machine learning and used to derive a cellular morphometric risk score (CMRS) that classified cSCCs into clusters of differential prognoses. Concordance analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated and compared with results obtained with the Brigham and Women’s Hospital (BWH) staging system. The performance of the combination of the BWH staging system and the CMBs was also analysed. Results There were no differences among the CMRS groups in terms of clinical and histopathological risk factors and T-stage assignment, but there were significant differences in prognosis. Combining the CMRS with BWH staging systems increased distinctiveness and improved prognostic performance. C-indices were 0.91 local recurrence and 0.91 for nodal metastasis when combining the two approaches. The NPV was 94.41% and 96.00%, the PPV was 36.36% and 41.67%, and accuracy reached 86.75% and 89.16%, respectively, with the combined approach. Conclusions CMRS is helpful for cSCC risk stratification beyond classic clinical and histopathological risk features. Combining the information from the CMRS and the BWH staging system offers outstanding prognostic performance for patients with high-risk cSCC.

Pérez-Baena, Manuel J.↗
DOE OSTI2024

TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets

Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are ‘unseen’ during training of the ML model. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope ‘hard splitting’ (i.e., on test sets with all epitopes unseen during ML training), 0.92 for TCR hard splitting and 0.89 for ‘strict splitting’ in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore, we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction.

60 APPLIED LIFE SCIENCES↗