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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗
DOE OSTI2022

An Analysis of the Novel Fluorocycline TP-6076 Bound to Both the Ribosome and Multidrug Efflux Pump AdeJ from Acinetobacter baumannii

Antibiotic resistance among bacterial pathogens continues to pose a serious global health threat. Multidrug-resistant (MDR) strains of the Gram-negative organism Acinetobacter baumannii utilize a number of resistance determinants to evade current antibiotics. One of the major resistance mechanisms employed by these pathogens is the use of multidrug efflux pumps. These pumps extrude xenobiotics directly out of bacterial cells, resulting in treatment failures when common antibiotics are administered. Here, the structure of the novel tetracycline antibiotic TP-6076, bound to both the Acinetobacter drug efflux pump AdeJ and the ribosome from Acinetobacter baumannii, using single-particle cryo-electron microscopy (cryo-EM), is elucidated. In this work, the structure of the AdeJ–TP-6076 complex is solved, and we show that AdeJ utilizes a network of hydrophobic interactions to recognize this fluorocycline. Concomitant with this, we elucidate three structures of TP-6076 bound to the A. baumannii ribosome and determine that its binding is stabilized largely by electrostatic interactions. We then compare the differences in binding modes between TP-6076 and the related tetracycline antibiotic eravacycline in both targets. These differences suggest that modifications to the tetracycline core may be able to alter AdeJ binding while maintaining interactions with the ribosome. Together, this work highlights how different mechanisms are used to stabilize the binding of tetracycline-based compounds to unique bacterial targets and provides guidance for the future clinical development of tetracycline antibiotics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundation models for interatomic potentials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Advancing density functional tight-binding method for large organic molecules through equivariant neural networks

Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett., 2021, 11, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN rep to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN rep , we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body Δ TB potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules—for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

Medrano Sandonas, Leonardo [Technische Universität↗
DOE OSTI2021

Antitumor T-cell Immunity Contributes to Pancreatic Cancer Immune Resistance

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Pancreatic tumors are minimally infiltrated by T cells and are largely refractory to immunotherapy. Accordingly, the role of T-cell immunity in pancreatic cancer has been somewhat overlooked. Here, we hypothesized that immune resistance in pancreatic cancer was induced in response to antitumor T-cell immune responses and that understanding how pancreatic tumors respond to immune attack may facilitate the development of more effective therapeutic strategies. We now provide evidence that T-cell–dependent host immune responses induce a PDAC-derived myeloid mimicry phenomenon and stimulate immune resistance. Three KPC mouse models of pancreatic cancer were used: the mT3-2D (Kras +/LSL-G12D ; Trp53 +/LSL-R172H ; Pdx1-Cre) subcutaneous and orthotopic models, as well as the KP1 (p48-CRE/LSL-Kras/Trp53 flox/flox ) subcutaneous model. KPC cancer cells were grown in immunocompetent and immunodeficient C57BL/6 mice and analyzed to determine the impact of adaptive immunity on malignant epithelial cells, as well as on whole tumors. We found that induced T-cell antitumor immunity, via signal transducer and activator of transcription 1 (STAT1), stimulated malignant epithelial pancreatic cells to induce the expression of genes typically expressed by myeloid cells and altered intratumoral immunosuppressive myeloid cell profiles. Targeting the Janus Kinase (JAK)/STAT signaling pathway using the FDA-approved drug ruxolitinib overcame these tumor-protective responses and improved anti–PD-1 therapeutic efficacy. Overall, these findings provide future directions for treatments that specifically disable this mechanism of resistance in PDAC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Novobiocin blocks nucleic acid binding to Polθ and inhibits stimulation of its ATPase activity

Polymerase theta (Polθ) acts in DNA replication and repair, and its inhibition is synthetic lethal in BRCA1 and BRCA2-deficient tumor cells. Novobiocin (NVB) is a first-in-class inhibitor of the Polθ ATPase activity, and it is currently being tested in clinical trials as an anti-cancer drug. Here, we investigated the molecular mechanism of NVB-mediated Polθ inhibition. Using hydrogen deuterium exchange-mass spectrometry (HX-MS), biophysical, biochemical, computational and cellular assays, we found NVB is a non-competitive inhibitor of ATP hydrolysis. NVB sugar group deletion resulted in decreased potency and reduced HX-MS interactions, supporting a specific NVB binding orientation. Collective results revealed that NVB binds to an allosteric site to block DNA binding, both in vitro and in cells. Comparisons of The Cancer Genome Atlas (TCGA) tumors and matched controls implied that POLQ upregulation in tumors stems from its role in replication stress responses to increased cell proliferation: this can now be tested in fifteen tumor types by NVB blocking ssDNA-stimulation of ATPase activity, required for Polθ function at replication forks and DNA damage sites. Structural and functional insights provided in this study suggest a path for developing NVB derivatives with improved potency for Polθ inhibition by targeting ssDNA binding with entropically constrained small molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

TwoFold: Highly accurate structure and affinity prediction for protein-ligand complexes from sequences

We describe our development of ab initio protein-ligand binding pose prediction models based on transformers and binding affinity prediction models based on the neural tangent kernel (NTK). Folding both protein and ligand, the TwoFold models achieve efficient and quality predictions matching state-of-the-art implementations while additionally reconstructing protein structures. In conclusion, solving NTK models points to a new use case for highly optimized linear solver benchmarking codes on HPC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Analysis of the Innovation Trend in Cell-Free Synthetic Biology

Cell-free synthetic biology is a maturing field that aims to assemble biomolecular reactions outside cells for compelling applications in drug discovery, metabolic engineering, biomanufacturing, diagnostics, and education. Cell-free systems have several key features. They circumvent mechanisms that have evolved to facilitate species survival, bypass limitations on molecular transport across the cell wall, enable high-yielding and rapid synthesis of proteins without creating recombinant cells, and provide high tolerance towards toxic substrates or products. Here, we analyze ~750 published patents and ~2000 peer-reviewed manuscripts in the field of cell-free systems. Three hallmarks emerged. First, we found that both patent filings and manuscript publications per year are significantly increasing (five-fold and 1.5-fold over the last decade, respectively). Second, we observed that the innovation landscape has changed. Patent applications were dominated by Japan in the early 2000s before shifting to China and the USA in recent years. Finally, we discovered an increasing prevalence of biotechnology companies using cell-free systems. Our analysis has broad implications on the future development of cell-free synthetic biology for commercial and industrial applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Structural Characterization of TRAF6 N-Terminal for Therapeutic Uses and Computational Studies on New Derivatives

Tumor necrosis factor receptor-associated factors (TRAFs) are a protein family with a wide variety of roles and binding partners. Among them, TRAF6, a ubiquitin ligase, possesses unique receptor binding specificity and shows diverse functions in immune system regulation, cellular signaling, central nervous system, and tumor formation. TRAF6 consists of an N-terminal Really Interesting New Gene (RING) domain, multiple zinc fingers, and a C-terminal TRAF domain. TRAF6 is an important therapeutic target for various disorders and structural studies of this protein are crucial for the development of next-generation therapeutics. Here, we presented a TRAF6 N-terminal structure determined at the Turkish light source “Turkish DeLight” to be 3.2 Å resolution at cryogenic temperature (PDB ID: 8HZ2). This structure offers insight into the domain organization and zinc-binding, which are critical for protein function. Since the RING domain and the zinc fingers are key targets for TRAF6 therapeutics, structural insights are crucial for future research. Separately, we rationally designed numerous new compounds and performed molecular docking studies using this template (PDB ID:8HZ2). According to the results, 10 new compounds formed key interactions with essential residues and zinc ion in the N-terminal region of TRAF6. Molecular dynamic (MD) simulations were performed for 300 ns to evaluate the stability of three docked complexes (compounds 256, 322, and 489). Compounds 256 and 489 was found to possess favorable bindings with TRAF6. These new compounds also showed moderate to good pharmacokinetic profiles, making them potential future drug candidates as TRAF6 inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Zinc Oxide–Graphene Nanocomposite-Based Sensor for the Electrochemical Determination of Cetirizine

A nanocomposite electrode of graphene (Gr) and zinc oxide (ZnO) nanoparticles was fabricated to study the electrochemical oxidation behavior of an anti-inflammatory drug, i.e., cetirizine (CET). The voltametric response of CET for bare CPE, Gr/CPE, ZnO/CPE, and the ZnO-Gr nanocomposite electrode was studied. The modifier materials were characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray powder diffraction (XRD) to comprehend the surface morphology of the utilized modifiers. The influence of pH, scan rate, and accumulation time on the electrooxidation of CET was examined. It was found that the electrochemical oxidation of CET was diffusion-controlled, in which two protons and two electrons participated. The detection limit was found to be 2.8 × 10 -8 M in a linearity range of 0.05–4.0 µM. Study of excipients was also performed, and it was found that they had negligible interference with the peak potential of CET. The validation and utility of the fabricated nanocomposite sensor material were examined by analyzing clinical and biological samples. Stability testing of the nanocomposite electrode was conducted to assess the reproducibility, determining that the developed biosensor has good stability and high efficiency in producing reproducible results.

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

Heterologous Expression of Cryptomaldamide in a Cyanobacterial Host

Filamentous marine cyanobacteria make a variety of bioactive molecules that are produced by polyketide synthases, nonribosomal peptide synthetases, and hybrid pathways that are encoded by large biosynthetic gene clusters. These cyanobacterial natural products represent potential drug leads; however, thorough pharmacological investigations have been impeded by the limited quantity of compound that is typically available from the native organisms. Additionally, investigations of the biosynthetic gene clusters and enzymatic pathways have been difficult due to the inability to conduct genetic manipulations in the native producers. Here we report a set of genetic tools for the heterologous expression of biosynthetic gene clusters in the cyanobacteria Synechococcus elongatus PCC 7942 and Anabaena (Nostoc) PCC 7120. To facilitate the transfer of gene clusters in both strains, we engineered a strain of Anabaena that contains S. elongatus homologous sequences for chromosomal recombination at a neutral site and devised a CRISPR-based strategy to efficiently obtain segregated double recombinant clones of Anabaena. These genetic tools were used to express the large 28.7 kb cryptomaldamide biosynthetic gene cluster from the marine cyanobacterium Moorena (Moorea) producens JHB in both model strains. S. elongatus did not produce cryptomaldamide; however, high-titer production of cryptomaldamide was obtained in Anabaena. Furthermore, the methods developed in this study will facilitate the heterologous expression of biosynthetic gene clusters isolated from marine cyanobacteria and complex metagenomic samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Structural insights into the substrate specificity of the endonuclease activity of the influenza virus cap-snatching mechanism

Abstract The endonuclease activity within the influenza virus cap-snatching process is a proven therapeutic target. The anti-influenza drug baloxavir is highly effective, but is associated with resistance mutations that threaten its clinical efficacy. The endonuclease resides within the N-terminal domain of the PA subunit (PAN) of the influenza RNA dependent RNA polymerase, and we report here complexes of PAN with RNA and DNA oligonucleotides to understand its specificity and the structural basis of baloxavir resistance mutations. The RNA and DNA oligonucleotides bind within the substrate binding groove of PAN in a similar fashion, explaining the ability of the enzyme to cleave both substrates. The individual nucleotides occupy adjacent conserved pockets that flank the two-metal active site. However, the 2′ OH of the RNA ribose moieties engage in additional interactions that appear to optimize the binding and cleavage efficiency for the natural substrate. The major baloxavir resistance mutation at position 38 is at the core of the substrate binding site, but structural studies and modeling suggest that it maintains the necessary virus fitness via compensating interactions with RNA. These studies will facilitate the development of new influenza therapeutics that spatially match the substrate and are less likely to elicit resistance mutations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A single inactivating amino acid change in the SARS-CoV-2 NSP3 Mac1 domain attenuates viral replication in vivo

Despite unprecedented efforts, our therapeutic arsenal against SARS-CoV-2 remains limited. The conserved macrodomain 1 (Mac1) in NSP3 is an enzyme exhibiting ADP-ribosylhydrolase activity and a possible drug target. To determine the role of Mac1 catalytic activity in viral replication, we generated recombinant viruses and replicons encoding a catalytically inactive NSP3 Mac1 domain by mutating a critical asparagine in the active site. While substitution to alanine (N40A) reduced catalytic activity by ~10-fold, mutations to aspartic acid (N40D) reduced activity by ~100-fold relative to wild-type. Importantly, the N40A mutation rendered Mac1 unstable in vitro and lowered expression levels in bacterial and mammalian cells. When incorporated into SARS-CoV-2 molecular clones, the N40D mutant only modestly affected viral fitness in immortalized cell lines, but reduced viral replication in human airway organoids by 10-fold. In mice, the N40D mutant replicated at >1000-fold lower levels compared to the wild-type virus while inducing a robust interferon response; all animals infected with the mutant virus survived infection. Our data validate the critical role of SARS-CoV-2 NSP3 Mac1 catalytic activity in viral replication and as a promising therapeutic target to develop antivirals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Enhanced production of taxadiene in Saccharomyces cerevisiae

Cost-effective production of the highly effective anti-cancer drug, paclitaxel (Taxol ® ), remains limited despite growing global demands. Low yields of the critical taxadiene precursor remains a key bottleneck in microbial production. In this study, the key challenge of poor taxadiene synthase (TASY) solubility in S. cerevisiae was revealed, and the strains were strategically engineered to relieve this bottleneck. Multi-copy chromosomal integration of TASY harbouring a selection of fusion solubility tags improved taxadiene titres 22-fold, up to 57 ± 3 mg/L at 30 °C at microscale, compared to expressing a single episomal copy of TASY. The scalability of the process was highlighted through achieving similar titres during scale up to 25 mL and 250 mL in shake flask and bioreactor cultivations, respectively at 20 and 30 °C. Maximum taxadiene titres of 129 ± 15 mg/L and 127 mg/L were achieved through shake flask and bioreactor cultivations, respectively, of the optimal strain at a reduced temperature of 20 °C. The results of this study highlight the benefit of employing a combination of molecular biology and bioprocess tools during synthetic pathway development, with which TASY activity was successfully improved by 6.5-fold compared to the highest literature titre in S. cerevisiae cell factories.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Self-Assembling and Pore-Forming Peptoids as Antimicrobial Biomaterials

Bacterial infections have been a serious threat to mankind throughout history. Natural antimicrobial peptides (AMPs) and their membrane-disruption mechanism have generated an immense interest in the design and development of synthetic mimetics that could overcome the intrinsic drawbacks of AMPs, such as their susceptibility to proteolytic degradation. Herein, by exploiting the self-assembly and pore-forming capabilities of sequence-defined peptoids, we discovered a new family of low molecular weight peptoid antibiotics that exhibit excellent broad-spectrum activity and high selectivity toward a panel of clinically significant Gram-positive and Gram-negative bacterial strains, including vancomycin-resistant E. faecalis (VREF), methicillin-resistant S. aureus (MRSA), methicillin-resistant S. epidermidis (MRSE), E. coli, P. aeruginosa, and K. pneumoniae. Tuning peptoid sidechain chemistry and structure enabled us to tune the efficacy of antimicrobial activity. Mechanistic studies using Transmission Electron Microscopy (TEM), bacterial membrane depolarization and lysis, and time-kill kinetics assays along with molecular dynamics simulations reveal that these peptoids kill both Gram-positive and Gram-negative bacteria through a membrane-disruption mechanism. In conclusion, these robust and biocompatible peptoid-based antibiotics can provide a valuable tool for combating the emerging drug resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Inhibition of the C1s Protease and the Classical Complement Pathway by 6-(4-Phenylpiperazin-1-yl)Pyridine-3-Carboximidamide and Chemical Analogs

Abstract The classical pathway (CP) is a potent mechanism for initiating complement activity and is a driver of pathology in many complement-mediated diseases. The CP is initiated via activation of complement component C1, which consists of the pattern recognition molecule C1q bound to a tetrameric assembly of proteases C1r and C1s. Enzymatically active C1s provides the catalytic basis for cleavage of the downstream CP components, C4 and C2, and is therefore an attractive target for therapeutic intervention in CP-driven diseases. Although an anti-C1s mAb has been Food and Drug Administration approved, identifying small-molecule C1s inhibitors remains a priority. In this study, we describe 6-(4-phenylpiperazin-1-yl)pyridine-3-carboximidamide (A1) as a selective, competitive inhibitor of C1s. A1 was identified through a virtual screen for small molecules that interact with the C1s substrate recognition site. Subsequent functional studies revealed that A1 dose-dependently inhibits CP activation by heparin-induced immune complexes, CP-driven lysis of Ab-sensitized sheep erythrocytes, CP activation in a pathway-specific ELISA, and cleavage of C2 by C1s. Biochemical experiments demonstrated that A1 binds directly to C1s with a K d of ∼9.8 μM and competitively inhibits its activity with an inhibition constant (K i) of ∼5.8 μM. A 1.8-Å-resolution crystal structure revealed the physical basis for C1s inhibition by A1 and provided information on the structure–activity relationship of the A1 scaffold, which was supported by evaluating a panel of A1 analogs. Taken together, our work identifies A1 as a new class of small-molecule C1s inhibitor and lays the foundation for development of increasingly potent and selective A1 analogs for both research and therapeutic purposes.

Immunology↗
DOE OSTI2024

Surface Charge at an Oil/Water Interface Promoting the Enhancement and Assignment of Water Combination Bands

Given the different polarities and distinct chemical structures, it is no wonder that oil and water are incompatible neighbors. They can be forced to cooperate if provided sufficient energy or a suitable intermediary. Emulsions are the result of such a cooperation, where an emulsifying agent (the intermediary) in the form of a surfactant or a cocktail of surface stabilizing compounds allows for the ‘mixing’ of oil and water. Surfactant stabilized emulsions that are nanoscale in size (nanoemulsions) have caught the attention of many because of their newly discovered use in the synthesis of nanomaterials, oil recovery, drug delivery, and food science. Unlike smaller emulsions (e.g. microemulsions with 2-20 nm diameters), very little is known about the molecular level properties of nanoemulsions (NEs) whose diameters are in the 100-500 nm range. One thing that is well known is the macroscopic properties of droplets from these two size regimes are dramatically different, often for reasons not yet fully understood. Nanoemulsions do not form spontaneously and require an external shear beyond ordinary procedures used to create micelles. This shear is required to overcome the effects of surface tension and create droplets in the nanoscale regime. Once formed, however, they can be stable for long periods of time, making them very attractive for commercial and pharmaceutical products requiring a long shelf life. What is not well known is the interfacial molecular processes that lead to the formation and stability of nanoemulsions including those stabilized by surfactants and/or polymers. Such knowledge is important in understanding the molecular structure-function relationship that assists in predicting what additives and solvents are necessary for a specific application. Key to advancing nanoemulsion utility is to develop the needed molecular-level understanding of the structure and bonding interactions present at the surface of these soft nanoparticles.

36 MATERIALS SCIENCE↗