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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 91 records · Page 5

Profiling expression strategies for a type III polyketide synthase in a lysate-based, cell-free system

Abstract Some of the most metabolically diverse species of bacteria (e.g., Actinobacteria) have higher GC content in their DNA, differ substantially in codon usage, and have distinct protein folding environments compared to tractable expression hosts like Escherichia coli . Consequentially, expressing biosynthetic gene clusters (BGCs) from these bacteria in E. coli often results in a myriad of unpredictable issues with regard to protein expression and folding, delaying the biochemical characterization of new natural products. Current strategies to achieve soluble, active expression of these enzymes in tractable hosts can be a lengthy trial-and-error process. Cell-free expression (CFE) has emerged as a valuable expression platform as a testbed for rapid prototyping expression parameters. Here, we use a type III polyketide synthase from Streptomyces griseus , RppA, which catalyzes the formation of the red pigment flaviolin, as a reporter to investigate BGC refactoring techniques. We applied a library of constructs with different combinations of promoters and rppA coding sequences to investigate the synergies between promoter and codon usage. Subsequently, we assess the utility of cell-free systems for prototyping these refactoring tactics prior to their implementation in cells. Overall, codon harmonization improves natural product synthesis more than traditional codon optimization across cell-free and cellular environments. More importantly, the choice of coding sequences and promoters impact protein expression synergistically, which should be considered for future efforts to use CFE for high-yield protein expression. The promoter strategy when applied to RppA was not completely correlated with that observed with GFP, indicating that different promoter strategies should be applied for different proteins. In vivo experiments suggest that there is correlation, but not complete alignment between expressing in cell free and in vivo. Refactoring promoters and/or coding sequences via CFE can be a valuable strategy to rapidly screen for catalytically functional production of enzymes from BCGs, which advances CFE as a tool for natural product research.

59 BASIC BIOLOGICAL SCIENCES↗

From Pollutant Removal to Renewable Energy: MoS2-Enhanced P25-Graphene Photocatalysts for Malathion Degradation and H2 Evolution

The widespread presence of pesticides—especially malathion—in aquatic environments presents a major obstacle to conventional remediation strategies, while the ongoing global energy crisis underscores the urgency of developing renewable energy sources such as hydrogen. In this context, photocatalytic water splitting emerges as a promising approach, though its practical application remains limited by poor charge carrier dynamics and insufficient visible-light utilization. Herein, we report the design and evaluation of a series of TiO2-based ternary nanocomposites comprising commercial P25 TiO2, reduced graphene oxide (rGO), and molybdenum disulfide (MoS2), with MoS2 loadings ranging from 1% to 10% by weight. The photocatalysts were fabricated via a two-step method: hydrothermal integration of rGO into P25 followed by solution-phase self-assembly of exfoliated MoS2 nanosheets. The composites were systematically characterized using X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), UV-Vis diffuse reflectance spectroscopy (DRS), and photoluminescence (PL) spectroscopy. Photocatalytic activity was assessed through two key applications: the degradation of malathion (20 mg/L) under simulated solar irradiation and hydrogen evolution from water in the presence of sacrificial agents. Quantification was performed using UV-Vis spectroscopy, gas chromatography–mass spectrometry (GC-MS), and thermal conductivity detection (GC-TCD). Results showed that the integration of rGO significantly enhanced surface area and charge mobility, while MoS2 served as an effective co-catalyst, promoting interfacial charge separation and acting as an active site for hydrogen evolution. Nearly complete malathion degradation (~100%) was achieved within two hours, and hydrogen production reached up to 6000 µmol g−1 h−1 under optimal MoS2 loading. Notably, photocatalytic performance declined with higher MoS2 content due to recombination effects. Overall, this work demonstrates the synergistic enhancement provided by rGO and MoS2 in a stable P25-based system and underscores the viability of such ternary nanocomposites for addressing both environmental remediation and sustainable energy conversion challenges.

Chemistry↗

Dielectric constant engineering of nonfullerene acceptors enables a record fill factor of 83.58% and a high efficiency of 20.80% in organic solar cells

Organic solar cells (OSCs) have achieved power conversion efficiencies (PCEs) surpassing 20%, but their development remains hindered by the inherently low dielectric constant (εr) of organic semiconductors, which limits charge transport and contributes to serious recombination losses. Herein, we present a comprehensive strategy to overcome the challenge by engineering the dielectric properties of nonfullerene acceptors (NFAs). Two Y-series NFAs of BTP-N3F and BTP-C3F have been synthesized featuring trifluoromethyl (CF 3 ) end-capped alkyl side chains. This molecular design significantly enhances the dipole moment and ε r (up to ∼5.9) when compared to the reference acceptor Y6 (∼3), reducing the exciton binding energy (E b ) and improving charge transport. Furthermore, the incorporation of a high-εr polymer additive, poly(pentafluorostyrene) (PPFS), synergistically improves the active layer morphology and dielectric properties, enabling efficient charge extraction and reduced recombination losses. Devices based on the optimized D18-Cl/BTP-C3F system have achieved a record-high fill factor (FF) of 83.58% and an impressive PCE of 20.80%, setting a new benchmark for OSCs. Finally, our results underscore the pivotal role of ε r in enhancing device performance and establish a versatile pathway for advancing OSC efficiency and stability through molecular and morphological optimization.

36 MATERIALS SCIENCE↗

Chemoselective, osmium-free, dihydroxylation/oxidative cleavage of heteroaryl isoprenes by a contemporary Malaprade reaction

The methyl ketone is a central synthetic building block for the construction of advanced heteroaryl scaffolds and systems. Reactions, including oxidative cyclization strategies, are often predicated on efficient access to this ubiquitous moiety. In the context of arenes, standard approaches leveraging Markovnikov hydration/oxidation or oxidative cleavage of the C–C π bond often afford satisfactory performance. However, when the substrate contains an electron-deficient heteroaryl core, the traditional Malaprade reaction, and related oxidative-cleavage strategies, frequently result in diminished performance over carbon-based arenes. In this work we present the development and application of an oxidative cleavage reaction of various pyridinyl isoprenes towards accessing the downstream methyl ketone for utilization in advanced cyclizations for the preparation of soft-N-donor complexant scaffolds. Further, this efficient protocol parallels the principles of Green chemistry by exchanging KMnO 4 for the toxic OsO 4 and offers the end-user an efficient, more environmentally friendly option for accessing heteroaryl methyl ketones in one hour of reaction time using potassium permanganate and sodium paraperiodate as a synergistically potent oxidative cleavage system. The wide substrate scope defined access to simple, as well as advanced heteroaryl methyl ketones. Method development, optimization, substrate scope, preliminary mechanistic observations, and a scale up reaction are delineated herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Development of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an Integrated Energy System (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. A new RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization (HERON) has been developed for optimizing the production variables of the IES given the demand profile. Currently, only the limits that affect the production variables and their corresponding time rates of change are considered (explicit constraints). However, other variables are additionally subject to constraints, but the associated limits are not accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response effects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. The Reference Governor (RG) algorithm is traditionally used for enforcing state and control constraints by modifying the set-point trajectories supplied to the feedback regulators. In our application, the RG is coupled within an iterative loop with the HERON-power dispatcher to generate optimal trajectories that ensure the operational constraints are met. A data-driven procedure to derive a representation of the dynamics of the controlled system was developed. First, the variables that represented the state of the system are selected (PCA-based approach), and then state-space representation matrices are derived from the collected measurements (DMDc algorithm). A preliminary version of the developed workflow based on Linear Time Invariant matrices was assessed by adopting a two-unit test case. More sophisticated versions of this workflow foreseeing the on-line derivation of system matrices will be deployed in FY 2021. Finally, a “plug-and-play” library of controllers and state observers was developed in Dymola. Some aspects of the current configuration of the IES unit components, e.g., the encapsulation of the control schemes into dedicated blocks, are consistent with the “plug-and-play” philosophy. Other features, e.g., the system buses collecting the input and the output variables, are not. For this reason, once listed and described the limits of the current configuration, necessary modifications to the plant model interface are presented. As a test-case, the interfaces of the SES model in the RAVEN-based HYBRID framework were reworked accordingly, and two different control schemes were applied to the same plant model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Validation and Demonstration of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an integrated energy system (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. The new RAVEN plugin for grid and capacity optimization (HERON) only addresses the limits that affect the production variables and the corresponding rates of variation (explicit constraints). However, other variables are subject to constraints, and the associated limits should be accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response affects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. To obtain a more accurate approximation of the nonlinear dynamic behavior, a parametric version of the dynamic mode decomposition with control (DMDc) algorithm was developed to derive the state-space representation matrices of the IES components at different scheduling parameter. Thanks to this approach, a more accurate approximation of the system response can be obtained, the limits imposed by thermal mechanical implicit constraints can be translated into power dispatch limits, and the feedbacks to HERON power dispatcher can be provided. To assess the developed methodology, a power dispatching test case composed of three power generating and storage units (Balance of Plant, Secondary Energy Source, Thermal Energy Storage) was developed. The power output of each one of the three units was optimized to meet the imposed time-dependent load demand trajectory and to maximize the IES profitability by meeting both the explicit and implicit constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Synergistic Solvent-Surface Interactions Enable Alkyne Semihydrogenation at Palladium

Enabling higher yield and better selectivity for fine-chemical synthesis through heterogeneous catalysis is intricately linked to the interplay of active sites, reaction conditions, and mass transfer influence provided by the catalyst. Alkyne semihydrogenation is ubiquitous in the production of bulk chemicals in the pharmaceutical, polymer, or fine-chemical industries, but product selectivity remains a major challenge. Here, in this study, we demonstrate that the design of catalysts encompassing nickel (Ni) foams as contiguous monolith supports, decorated with ultralow loading of Pd/PdO x nanoparticles on a carbonized polydopamine interface and tuned with a thin layer of Al 2 O 3 , in conjunction with an optimized reaction environment leads to highly selective alkyne semihydrogenation. The reactions demonstrate good functional group tolerance and applicability to flow reactor systems. Combined computational and experimental studies are presented to describe the synergistic effect between the solvent-surface interaction and the degree of Pd surface reduction that are necessary to promote this selectivity. The system highlights the opportunity for catalyst-solvent codesign as a benign alternative to more complex reactants featuring extrinsic poisons or less-favored dopants.

atomic layer deposition↗

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

Nutrient use efficiency (NUE) is typically measured as the ratio of yield to soil nutrient availability but ignores contributions of underlying plant traits. Relevant plant traits can be grouped as root acquisition efficiency, shoot radiation use efficiency, and plant metabolic efficiency. The intentional integration of these traits will lead to synergistic improvements of NUE. Recent progress in trait-focused research includes phenotyping root nutrient uptake rates and respiration, engineering reduced photorespiration, and identification of nutrient assimilation pathways. Traits need to be conceptualized in agricultural systems contexts to improve synchrony of plant demand and soil supply of nutrients, including consideration of crop mixtures. Use of simulation modeling and multi-objective optimization will allow accelerating NUE gains beyond selection for a single ratio.

59 BASIC BIOLOGICAL SCIENCES↗

Purification of Propylene and Ethylene by a Robust Metal–Organic Framework Mediated by Host–Guest Interactions

Industrial purification of propylene and ethylene requires cryogenic distillation and selective hydrogenation over palladium catalysts to remove propane, ethane and/or trace amounts of acetylene. Here, we report the excellent separation of equimolar mixtures of propylene/propane and ethylene/ethane, and of a 1/100 mixture of acetylene/ethylene by a highly robust microporous material, MFM-520, under dynamic conditions. In situ synchrotron single crystal X-ray diffraction, inelastic neutron scattering and analysis of adsorption thermodynamic parameters reveal that a series of synergistic host-guest interactions involving hydrogen bonding and π⋯π stacking interactions underpin the cooperative binding of alkenes within the pore. Notably, the optimal pore geometry of the material enables selective accommodation of acetylene. The practical potential of this porous material has been demonstrated by fabricating mixed-matrix membranes comprising MFM-520, Matrimid and PIM-1, and these exhibit not only a high permeability for propylene (≈1984 Barrer), but also a separation factor of 7.8 for an equimolar mixture of propylene/propane at 298 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Integrating quantum computing resources into scientific HPC ecosystems

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era’s inherent external noise issues. Here, this paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Application of Peracetic Acid in Poultry Processing: Effects of Treatment Dynamics and Emerging Risk of Resistance Development in Salmonella spp.

Nontyphoidal Salmonella is a leading cause of foodborne illness, with poultry representing a major source. Peracetic acid (PAA), a widely adopted antimicrobial in poultry processing, offers advantages over traditional disinfectants but has sparked interest in its combined use with other antimicrobials and potential resistance. This review evaluates the efficacy of PAA in mitigating Salmonella in combination with other food-grade antimicrobials, explores possible synergism, efficacy under varying treatment parameters, resistance development against PAA, and its role in resistance evolution. While PAA demonstrates broad-spectrum efficacy, its performance varies with environmental parameters; higher temperatures generally enhance antimicrobial action but also accelerate PAA degradation. Organic matter diminishes PAA efficacy by reactive quenching. Variability in concentration and contact time further influences outcomes. Despite its oxidative mode of action and presumed low risk for resistance, emerging studies indicate that Salmonella can develop adaptive tolerance and potential cross/coresistance following repeated or sublethal exposure to PAA. These adaptations may involve genetic upregulation of oxidative stress response pathways, efflux systems, and modifications in cell membrane integrity, raising concerns about the long-term sustainability of PAA use. Additionally, combinatorial treatments (e.g., PAA with UV-C, enzymes, or organic acids) show promise in enhancing efficacy while mitigating resistance risks. Despite recognition of PAA's safety and effectiveness, knowledge gaps remain regarding standardized resistance definitions, serotype-specific tolerance, and optimal intervention strategies in commercial settings. Therefore, there is a need for standardized testing protocols, robust studies on potential resistance, and further exploration of synergistic PAA applications to ensure sustained poultry product safety and public health protection.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗

Fast ion studies in the extended high-performance high β P plasma on EAST

Comprehending and optimizing fast ion behaviors is critical for the enhancement of performance in Experimental Advanced Superconducting Tokamak (EAST). This study explores the potential benefits of several factors that can improve the fast ion confinement. First, experiments show the change in the direction of the NBI2 from counter-I p to co-I p leads to a significant reduction in fast ion losses. TRANSP/NUBEAM simulation and tomography results based on fast-ion D-alpha measurements reveal that after the neutral beam injection (NBI) upgrade, the beam ion prompt loss is reduced by approximately 50%. Second, the upgraded ion cyclotron resonant frequency (ICRF) antenna at the N-port features twice the coupling resistance of the original antennas at EAST. This improved ICRF power coupling has enhanced the synergistic heating effect of NBI + ICRF, where the ICRF wave field accelerates beam ions at the harmonics. Experiments demonstrate that NBI + ICRF synergistic not only enhances plasma neutron yield and β P , but also accelerates beam ions to hundreds of keV. Further, the electron density and the neutral beam voltage have been optimized to reduce the fast ion slowing-down time and beam ion losses. Experimental and simulation results indicate that increasing the electron density reduces beam ion losses and enhances the bootstrap current fraction. While higher beam voltage results in a slight decrease in beam power absorption, it can increase the fraction of bootstrap current. With the understanding of these optimization of fast ion confinement, experiments have demonstrated fully non-inductive operation at high density (n e /n G ∼ 0.67, β P ∼ 3.1, β N ∼ 2.1, H 98,y2 ∼ 1.2) even without the support of co-I p beam NBI2. This investigation presents a potential regime to enhance fast ion confinement and extend performance in the high β P plasma for future experiments.

EAST tokamak↗

Microbial production of high octane and high sensitivity olefinic ester biofuels

Abstract Background Advanced spark ignition engines require high performance fuels with improved resistance to autoignition. Biologically derived olefinic alcohols have arisen as promising blendstock candidates due to favorable octane numbers and synergistic blending characteristics. However, production and downstream separation of these alcohols are limited by their intrinsic toxicity and high aqueous solubility, respectively. Bioproduction of carboxylate esters of alcohols can improve partitioning and reduce toxicity, but in practice has been limited to saturated esters with characteristically low octane sensitivity. If olefinic esters retain the synergistic blending characteristics of their alcohol counterparts, they could improve the bioblendstock combustion performance while also retaining the production advantages of the ester moiety. Results Optimization of Escherichia coli isoprenoid pathways has led to high titers of isoprenol and prenol, which are not only excellent standalone biofuel and blend candidates, but also novel targets for esterification. Here, a selection of olefinic esters enhanced blendstock performance according to their degree of unsaturation and branching. E. coli strains harboring optimized mevalonate pathways, thioester pathways, and heterologous alcohol acyltransferases (ATF1, ATF2, and SAAT) were engineered for the bioproduction of four novel olefinic esters. Although prenyl and isoprenyl lactate titers were limited to 1.48 ± 0.41 mg/L and 5.57 ± 1.36 mg/L, strains engineered for prenyl and isoprenyl acetate attained titers of 176.3 ± 16.0 mg/L and 3.08 ± 0.27 g/L, respectively. Furthermore, prenyl acetate (20% bRON = 125.8) and isoprenyl acetate (20% bRON = 108.4) exhibited blend properties comparable to ethanol and significantly better than any saturated ester. By further scaling cultures to a 2-L bioreactor under fed-batch conditions, 15.0 ± 0.9 g/L isoprenyl acetate was achieved on minimal medium. Metabolic engineering of acetate pathway flux further improved titer to attain an unprecedented 28.0 ± 1.0 g/L isoprenyl acetate, accounting for 75.7% theoretical yield from glucose. Conclusion Our study demonstrated novel bioproduction of four isoprenoid oxygenates for fuel blending. Our optimized E. coli production strain generated an unprecedented titer of isoprenyl acetate and when paired with its favorable blend properties, may enable rapid scale-up of olefinic alcohol esters for use as a fuel blend additive or as a precursor for longer-chain biofuels and biochemicals.

09 BIOMASS FUELS↗

Automation of FARM from Alpha Phase to Beta Phase

Integrated energy systems (IES) combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework and the RAVEN plugin for grid and capacity optimization (HERON) are used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES, by respecting the limits on the production variables and the corresponding rates of variation (explicit constraints). Besides, there are other process variables whose evolution needs to be bounded to avoid damaging the components (e.g., condensers, heat exchangers, steam generators, etc.) or degrading the process efficiency (e.g., electrolysis in the hydrogen production process). To avoid violating these latter limits (implicit constraints), a proof-of-concept HERON validator based on Feasible Actuator Range Modifier (FARM-Alpha) was developed by Argonne National Laboratory in January 2021. This FARM-Alpha validator calculates the evolution of process variables on whom the implicit constraints are placed, and then provides feedback to HERON dispatcher to adjust the power setpoints of three IES components, i.e., Balance of Plant, Secondary Energy Source, and Thermal Energy Storage, so as to meet both the explicit and implicit constraints. FARM-Alpha was designed to assess the performance of FARM as a HERON validator only, i.e., the list of components and implicit operational constraints were hard-coded within the source code. The lack of flexibility of the corresponding software structure does not allow the deployment in production environment. This report describes the development and the implementation of an enhanced version of the FARM-based validator (FARM-Beta), which ensures more flexibility for the end user in modeling multiple IES configurations and scenarios. Several test cases of the power dispatch problem were then selected to demonstrate the capabilities offered by FARM-beta. The test cases illustrate the efficiency of the closed-loop optimization scheme and the capability to calculate set-point trajectories satisfying both explicit and implicit constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

59 BASIC BIOLOGICAL SCIENCES↗

Pulsed Light Synthesis of High Entropy Nanocatalysts with Enhanced Catalytic Activity and Prolonged Stability for Oxygen Evolution Reaction

The ability to synthesize compositionally complex nanostructures rapidly is a key to high-throughput functional materials discovery. In addition to being time-consuming, a majority of conventional materials synthesis processes closely follow thermodynamics equilibria, which limit the discovery of new classes of metastable phases such as high entropy oxides (HEO). Herein, a photonic flash synthesis of HEO nanoparticles at timescales of milliseconds is demonstrated. By leveraging the abrupt heating and cooling cycles induced by a high-power-density xenon pulsed light, mixed transition metal salt precursors undergo rapid chemical transformations. Hence, nanoparticles form within milliseconds with a strong affinity to bind to the carbon substrate. Oxygen evolution reaction (OER) activity measurements of the synthesized nanoparticles demonstrate two orders of magnitude prolonged stability at high current densities, without noticeable decay in performance, compared to commercial IrO 2 catalyst. This superior catalytic activity originates from the synergistic effect of different alloying elements mixed at a high entropic state. It is found that Cr addition influences surface activity the most by promoting higher oxidation states, favoring optimal interaction with OER intermediates. The proposed high-throughput method opens new pathways toward developing next-generation functional materials for various electronics, sensing, and environmental applications, in addition to renewable energy conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Performance of hybridized bamboo-carbon fiber reinforced polypropylene composites processed using wet laid technique

The end-of-life vehicles (ELV) regulations motivate hybrid materials usage in automotive industries to optimize properties at reduced cost and increase eco-friendly designs. This research explores hybrid compositions of natural bamboo fiber and synthetic carbon fiber. The goal of hybridization was to synergistically benefit from each constituent– i.e., sustainability, energy absorption and superior damping from bamboo, and high strength and stiffness from carbon fiber. Carbon fibers (CF), bamboo fibers (BF) and polypropylene (PP) fibers were dispersed in water to produce wet-laid hybrid mats. The mats were compression molded into consolidated panels to obtain the hybrid composite(s) (BF-CF-PP). Four formulations with different fiber-resin weight percent were designed and produced including- BF-PP (30/70), BF-PP (50/50), BF-CF-PP (32/8/60), and BF-CF-PP (8/32/60). The effect of (a) fiber length, (b) surface treatment, (c) fiber content, and (d) consolidation pressure on the mechanical properties were examined. The improved mechanical (flexural strength 76.4 MPa, flexural modulus 4.1 GPa, ILSS 12.4 MPa and impact strength 49.9 KJ/m 2 ) and vibrational damping (1.05%) properties showed that the BF-CF-PP (8/32/60) provided higher properties compared to the other variants. The projected properties at various stoichiometric ratios of carbon and bamboo fiber revealed that the properties of hybrid composites could be tailored to produce desirable, cost-effective, and sustainable automotive components.

36 MATERIALS SCIENCE↗