Leveraging Structure-Based Drug Design to Identify Next-Generation MAT2A Inhibitors, Including Brain
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Li, Mingzong↗
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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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Heterogeneous, Defect-Rich Battery Particles and Electrodes: Why Do They Matter, and How Can One Leverage Them?
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99 GENERAL AND MISCELLANEOUS↗
Leveraging Multiproton-Coupled Electron Transfer to Improve Ir(III) Photocatalyst Efficiency
In photoredox reactions, charge recombination (CR) limits quantum yields, hindering the efficient conversion of light energy into catalytic activity. To address this, we drew inspiration from redox relays in photosystem II (PSII) and developed a new series of iridium(III) complexes featuring covalently attached benzimidazole-phenol-pyridine (BIP-Py) groups to facilitate intramolecular multiproton-coupled electron transfer (MPCET). Herein, we evaluate the effects of MPCET through an extended and well-defined hydrogen-bond network to improve photocatalytic activity and mitigate rapid charge recombination. Infrared spectroelectrochemistry reveals pyridine protonation upon phenol oxidation, while visible spectroelectrochemistry and transient absorption spectroscopy confirm the electro- and photochemical formation of chargeseparated states (CSS) involving oxidized BIP, resulting from intramolecular proton-coupled electron transfer (PCET). The application of the BIP-Py platform in a photocatalytic Nhydroxyphthalimide ester reduction reaction resulted in a ∼106-fold reduction in CR rate and a quantum yield enhancement of up to 157%. Our findings suggest that incorporating MPCET-based redox relays into photocatalyst frameworks is an effective strategy to enhance the efficiency of photocatalytic systems.
Catalysts↗
Leveraging Sequential Doping of Semiconducting Polymers to Enable Functionally Graded Materials for Organic Thermoelectrics
With the ability to modulate electronic properties through molecular doping coupled with ease in processability, semiconducting polymers are at the forefront in enabling organic thermoelectric devices for thermal energy management. In contrast to uniform thermoelectric material properties, an alternative route focuses on functionally graded materials (FGMs) where one spatially controls and optimizes transport properties across the length of a thermoelectric material. While primarily studied in the context of inorganic materials, the concept of FGMs for organic thermoelectrics has not been explored. Herein, we introduce how molecular doping of semiconducting polymers enables spatial compositional control of thin-film FGMs. Specifically, we utilize sequential vapor doping of poly[2,5-bis(3-tetradecylthiophen-2-yl) thieno [3,2-b]thiophene] (PBTTT) with the small molecule acceptor 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) to fabricate the simplest form of FGMs – double- segmented thin films. The two thin-film segments are of equal length (7.5 mm), but each set to different doping levels. Our study focuses on understanding the thermoelectric properties (Seebeck coefficient, α, and electronic conductivity, σ) and structural properties (through X-ray scattering, UV-vis-NIR spectroscopy, and Raman spectroscopy) within and across the two segments. We observe the presence of a small diffuse interfacial region of 0.5 – 1 mm between the two segments where the doping level and transport properties vary continuously. Despite the diffuse interface, the measured effective Seebeck coefficient (αeff) across the two segments is simply the average of α within each segment. Importantly, this experimental result is consistent with reported mathematical models describing αeff in graded thermoelectric materials. Finally, our results demonstrate the facile fabrication and characterization of functionally graded organic thermoelectric materials, providing guidelines for further development on more complex FGMs.
36 MATERIALS SCIENCE↗
Leveraging the Polymer Glass Transition to Access Thermally Switchable Shear Jamming Suspensions
Suspensions of polymeric nano-and microparticles are fascinating stress-responsive material systems that, depending on their composition, can display a diverse range of flow properties under shear, such as drastic thinning, thickening, and even jamming (reversible solidification driven by shear). However, investigations to date have almost exclusively focused on nonresponsive particles, which do not allow in situ tuning of the flow properties. Polymeric materials possess rich phase transitions that can be directly tuned by their chemical structures, which has enabled researchers to engineer versatile adaptive materials that can respond to targeted external stimuli. Reported herein are suspensions of (readily prepared) micrometer-sized polymeric particles with accessible glass transition temperatures (T g ) designed to thermally control their non-Newtonian rheology. The underlying mechanical stiffness and interparticle friction between particles change dramatically near T g . Capitalizing on these properties, it is shown that, in contrast to conventional systems, a dramatic and nonmonotonic change in shear thickening occurs as the suspensions transition through the particles' T g . This straightforward strategy enables the in situ turning on (or off) of the system's ability to shear jam by varying the temperature relative to T g and lays the groundwork for other types of stimuli-responsive jamming systems through polymer chemistry.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SARS-CoV-2 Main Protease Inhibitors That Leverage Unique Interactions with the Solvent Exposed S3 Site of the Enzyme
Not Available
60 APPLIED LIFE SCIENCES↗
Leveraging Hydration Forces for Size-Specific Nanoparticle Enrichment with a Redox-Responsive Silica-Binding Elastin-Like Polypeptide
Elastin-like polypeptides (ELPs) are low-complexity proteins that coacervate above a characteristic lower critical solution temperature (LCST). While the thermoresponsiveness of ELPs has been widely exploited in the biomedical and biomaterials fields, their ability to mediate nanoparticle assembly below their transition temperature remains largely unexplored. Here, we show that unmodified ELPs induce the reversible flocculation of silica nanoparticles (SiNPs) by forming backbone hydrogen bonds with surface silanols. Interparticle bridging is modulated by ELP length and concentration and by the presence of N- and C-terminal anchoring groups such as a cysteine residue and a Car9 silica-binding peptide. Using a redox-responsive fusion protein consisting of disulfide-bonded ELP domains terminated by Car9 segments, we stabilize 20 nm SiNPs under oxidizing conditions while triggering particle flocculation upon addition of reductant. We find that SiNP sedimentation under reducing conditions exhibits a sharp dependency on particle size that arises from the curvature-dependent structure of surface silanols. While the isolated silanols of SiNPs smaller than 30 nm are efficiently engaged by the ELP domains of Car9-anchored proteins, repulsion forces associated with the presence of a layer of molecular water together with increased electrostatic repulsion preclude efficient engagement of H-bonded silanols displayed on the surface of SiNPs larger than 60 nm. We harness these findings to selectively enrich SiNPs based on size and expand the concept to titania (TiO2) by demonstrating that rutile nanoparticles can be stabilized or sedimented with solid-binding ELPs by adjusting the solution pH to promote or discourage the formation of a hydration layer. These strategies should prove broadly useful for the separation of other oxides and their polymorphs and provide a tunable strategy for nanoparticle assembly and bioinspired colloidal design.
ELP↗
Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows
Not Available
36 MATERIALS SCIENCE↗
Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation
The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.
ChemBERTa↗
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Leveraging Intramolecular π-Stacking to Access an Exceptionally Long-Lived 3 MC Excited State in an Fe(II) Carbene Complex
The ability to manipulate excited-state decay cascades using molecular structure is essential to the application of abundant-metal photosensitizers and chromophores. Ligand design has yielded some spectacular results elongating charge-transfer excited state lifetimes of Fe(II) coordination complexes, but triplet metal-centered ( 3 MC) excited states - recently demonstrated to be critical to the photoactivity of isoelectronic Co(III) polypyridyls - have to date remained elusive, with temporally isolable examples limited to the picosecond regime. Here, with this report, we show how strong-field donors and intramolecular π-stacking can conspire to stabilize a long-lived 3 MC excited state for a remarkable 4.1 ± 0.3 ns in fluid solution at ambient temperature. Analysis of variable-temperature time-resolved absorption data using theoretical models ranging from Arrhenius to semiclassical Marcus theory, combined with computational modeling and X-ray crystallography, reveal a Jahn−Teller stabilized excited state with a high activation barrier for ground-state recovery. The net result is a chromophore with a 3 MC excited-state lifetime that is orders of magnitude longer than anything yet observed for an Fe(II) complex.
carbene compounds↗
Leveraging Stereochemistry to Optimize the Properties of Polyhydroxyalkanoates
The recycling of low-density polyethylene (LDPE) is challenging due to difficulties with sorting and contamination, leading to environmental harm. Polyhydroxyalkanoates (PHAs) are at the forefront of high-performance biodegradable alternatives to olefinic plastics, but few offer LDPE-like properties such as low strength and crystallinity while maintaining high ductility and thermal stability. Herein, we report a series of isoenriched transpoly( 3-hydroxy-2-methylbutyrates) (trans-PHMBs) with tunable mechanical and thermal properties. These polymers were synthesized through ring-opening polymerization of racemic trans-3,4-dimethylpropiolactone (rac-trans-DMPL), sourced from C1 and C4 feedstocks, using a new class of “sandwich” C 2 symmetric rac-( Ar BDI*)ZnO i Pr catalysts (where BDI = β-diketiminate). Variation of aromatic groups (Ar) and polymerization temperature yielded mm%s between 45−79% and melting temperatures (T m ) between 141−174 °C. trans-PHMB with intermediate isotacticities of 73 and 75 mm% exhibit similar stress−strain profiles to LDPE, indicating that these polymers have the potential to serve as higher melting, degradable substitutes for LDPE.
Biopolymers↗
Leveraging 13C-Labeling to Assign Molecular Formulas to Unknown Yeast Metabolites
Mass spectrometry analyses have identified tens of thousands of unknown small molecule-associated peaks in different biological specimens. Notably, even the simplest and best studied organisms like Escherichia coli and Saccharomyces cerevisiae yield thousands of unknown peaks. A key question is how many of these reflect actual novel endogenous metabolites. To explore this, Mahieu and Patti used complete 13 C -labeling in E. coli to credential peaks as biological. This reduced the number of unknowns by more than 90%. Here, we carry out similar uniform 13 C-labeling in the Baker’s yeast S. cerevisiae and two less-studied bioenergy-relevant yeasts Rhodotorula toruloides (lipid producer) and Issatchenkia orientalis (organic acid producer). Identification of unknown metabolite peaks and their molecular formulas is facilitated through software tailored for 13 C labeling data and resulting knowledge of carbon atom count. A classification model evaluates the plausibility of each candidate formula, with peaks lacking plausible candidate formulas unlikely to reflect metabolite molecular ions. This approach prioritizes about one hundred candidate abundant unknown metabolites with logical molecular formulas. Most of these are species-specific rather than conserved across yeasts, and more are found in the nonmodel yeasts than S. cerevisiae. Thus, 13 C-labeling data on unknown metabolites highlights the potential for discovering new metabolites and pathways in nonmodel yeasts.
Carbon↗
Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods
Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.
58 GEOSCIENCES↗
Leveraging Groundwater Dynamics to Improve Predictions of Summer Low-Flow Discharges
Summer streamflow predictions are critical for managing water resources; however, warming-induced shifts from snow to rain regimes impact low-flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. Here, we quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low-flow prediction compared to snow metrics at a rain-dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep-storage-dominated systems. Overall, including RLGW improved low-flow prediction across diverse watersheds.
54 ENVIRONMENTAL SCIENCES↗