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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mechanistic Pathways for the Molecular Step Growth of Calcium Oxalate Monohydrate Crystal Revealed by In Situ Liquid-Phase Atomic Force Microscopy

Calcium oxalate monohydrate (COM) crystal is the most common crystalline component of human kidney stones. The molecular-scale inhibitory mechanisms of COM crystal growth by urinary biomolecules such as citrate and osteopontin adsorbed onto the crystal surface are now well understood. However, the pathways by which dissolved calcium and oxalate ions are incorporated into the molecular step of the COM crystal surface, leading to COM crystal growth—a prerequisite to be elucidated for developing effective therapeutics to inhibit COM stones—remain unknown. Here, using in situ liquid-phase atomic microscopy along with a step kinetic model, we reveal the pathways of the calcium and oxalate ions into the COM molecular step via the growth speed analysis of the molecular steps with respect to their step width at the nanoscale. Our results show that, primarily, the ions are adsorbed onto the terrace of the crystal surface from the solution—the rate-controlling stage for the molecular step growth, i.e., COM crystal growth—and then diffuse over it and are eventually incorporated into the steps. This primary pathway of the ions is unaffected by the model peptide D-Asp6 adsorbed on the COM crystal surface, suggesting that urinary biomolecules will not alter the pathway. These new findings rendering an essential understanding of the fundamental growth mechanism of COM crystal at the nanoscale provide crucial insights beneficial to the development of effective therapeutics for COM kidney stones.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Compact Peptoid Molecular Brushes for Nanoparticle Stabilization

Controlling the interfaces and interactions of colloidal nanoparticles (NPs) via tethered molecular moieties is crucial for NP applications in engineered nanomaterials, optics, catalysis, and nanomedicine. Despite a broad range of molecular types explored, there is a need for a flexible approach to rationally vary the chemistry and structure of these interfacial molecules for controlling NP stability in diverse environments, while maintaining a small size of the NP molecular shell. Here, we demonstrate that low-molecular-weight, bifunctional comb-shaped, and sequence-defined peptoids can effectively stabilize gold NPs (AuNPs). The generality of this robust functionalization strategy was also demonstrated by coating of silver, platinum, and iron oxide NPs with designed peptoids. Each peptoid (PE) is designed with varied arrangements of a multivalent AuNP-binding domain and a solvation domain consisting of oligo-ethylene glycol (EG) branches. Among designs, a peptoid (PE5) with a diblock structure is demonstrated to provide a superior nanocolloidal stability in diverse aqueous solutions while forming a compact shell (similar to 1.5 nm) on the AuNP surface. We demonstrate by experiments and molecular dynamics simulations that PE5-coated AuNPs (PE5/AuNPs) are stable in select organic solvents owing to the strong PE5 (amine)-Au binding and solubility of the oligo-EG motifs. At the vapor-aqueous interface, we show that PE5/AuNPs remain stable and can self-assemble into ordered 2D lattices. The NP films exhibit strong near-field plasmonic coupling when transferred to solid substrates.

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

Proton switching molecular magnetoelectricity

Abstract The convergence of proton conduction and multiferroics is generating a compelling opportunity to achieve strong magnetoelectric coupling and magneto-ionics, offering a versatile platform to realize molecular magnetoelectrics. Here we describe machine learning coupled with additive manufacturing to accelerate the design strategy for hydrogen-bonded multiferroic macromolecules accompanied by strong proton dependence of magnetic properties. The proton switching magnetoelectricity occurs in three-dimensional molecular heterogeneous solids. It consists of a molecular magnet network as proton reservoir to modulate ferroelectric polarization, while molecular ferroelectrics charging proton transfer to reversibly manipulate magnetism. The magnetoelectric coupling induces a reversible 29% magnetization control at ferroelectric phase transition with a broad thermal hysteresis width of 160 K (192 K to 352 K), while a room-temperature reversible magnetic modulation is realized at a low electric field stimulus of 1 kV cm −1 . The findings of electrostatic proton transfer provide a pathway of proton mediated magnetization control in hierarchical molecular multiferroics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ceramic thin-film composite membranes with tunable subnanometer pores for molecular sieving

Abstract Ceramic membranes are a promising alternative to polymeric membranes for selective separations, given their ability to operate under harsh chemical conditions. However, current fabrication technologies fail to construct ceramic membranes suitable for selective molecular separations. Herein, we demonstrate a molecular-level design of ceramic thin-film composite membranes with tunable subnanometer pores for precise molecular sieving. Through burning off the distributed carbonaceous species of varied dimensions within hybrid aluminum oxide films, we created membranes with tunable molecular sieving. Specifically, the membranes created with methanol showed exceptional selectivity toward monovalent and divalent salts. We attribute this observed selectivity to the dehydration of the large divalent ions within the subnanometer pores. As a comparison, smaller monovalent ions can rapidly permeate with an intact hydration shell. Lastly, the flux of neutral solutes through each fabricated aluminum oxide membrane was measured for the demonstration of tunable separation capability. Overall, our work provides the scientific basis for the design of ceramic membranes with subnanometer pores for molecular sieving using atomic layer deposition.

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

Toward nanoscale molecular mass spectrometry imaging via physically constrained machine learning on co-registered multimodal data

Mass spectrometry imaging (MSI) plays a pivotal role in investigating the chemical nature of complex systems that underly our understanding in biology and medicine. Multiple fields of life science such as proteomics, lipidomics and metabolomics benefit from the ability to simultaneously identify molecules and pinpoint their distribution across a sample. However, achieving the necessary submicron spatial resolution to distinguish chemical differences between individual cells and generating intact molecular spectra is still a challenge with any single imaging approach. Here, we developed an approach that combines two MSI techniques, matrix-assisted laser desorption/ionization (MALDI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The known relationships between the two MSI channels of information are enforced via a physically constrained machine-learning approach and directly incorporated in the data processing. We demonstrate the robustness of this method by generating intact molecular MALDI-type spectra and chemical maps at ToF-SIMS resolution when imaging mouse brain thin tissue sections. This approach can be readily adopted for other types of bioimaging where physical relationships between methods have to be considered to boost the confidence in the reconstruction product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

In search of molecular ions for optical cycling: a difficult road

Optical cycling, a continuous photon scattering off atoms or molecules, plays a central role in the quantum information science. While optical cycling has been experimentally achieved for many neutral species, few molecular ions have been investigated. We present a systematic theoretical search for diatomic molecular ions suitable for optical cycling using equation-of-motion coupled-cluster methods. Inspired by the electronic structure patterns of laser-cooled neutral molecules, we establish the design principles for molecular ions and explore various possible cationic molecular frameworks. The results show that finding a perfect molecular ion for optical cycling is challenging, yet possible. Among various possible diatomic molecules we suggest several candidates, which require further attention from both theory and experiment: YF + , SiO + , PN + , SiBr + , and BO + .

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

Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders

In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Molecular qubits based on photogenerated spin-correlated radical pairs for quantum sensing

Photogenerated spin-correlated radical pairs (SCRPs) in electron donor–bridge–acceptor (D–B–A) molecules can act as molecular qubits and inherently spin qubit pairs. SCRPs can take singlet and triplet spin states, comprising the quantum superposition state. Their synthetic accessibility and well-defined structures, together with their ability to be prepared in an initially pure, entangled spin state and optical addressability, make them one of the promising avenues for advancing quantum information science. Coherence between two spin states and spin selective electron transfer reactions form the foundation of using SCRPs as qubits for sensing. We can exploit the unique sensitivity of the spin dynamics of SCRPs to external magnetic fields for sensing applications including resolution-enhanced imaging, magnetometers, and magnetic switch. Molecular quantum sensors, if realized, can provide new technological developments beyond what is possible with classical counterparts. While the community of spin chemistry has actively investigated magnetic field effects on chemical reactions via SCRPs for several decades, we have not yet fully exploited the synthetic tunability of molecular systems to our advantage. This review offers an introduction to the photogenerated SCRPs-based molecular qubits for quantum sensing, aiming to lay the foundation for researchers new to the field and provide a basic reference for researchers active in the field. We focus on the basic principles necessary to construct molecular qubits based on SCRPs and the examples in quantum sensing explored to date from the perspective of the experimentalist.

Mani, Tomoyasu (ORCID:0000000241255195)↗
DOE OSTI2023

Nature of contaminants introduced in silicon wafers during molecular beam epitaxy chamber annealing

Epitaxial monolithic III–V/Si tandem solar cells are one of the most promising technologies to be adopted by the industry after the efficiency of the current market dominating single junction silicon solar cell saturates at its fundamental limit. One of the key limitations of this technology is the degradation of silicon wafers during in situ annealing in the molecular beam epitaxy chamber. Determining the nature of contaminants in this process is key to improve the efficiency of epitaxial tandem solar cells. However, to date, the nature of contaminants from molecular beam epitaxy chambers remains unknown. In this work, we use photoluminescence imaging, lifetime spectroscopy, and deep level transient spectroscopy to measure the electronic properties of extrinsic impurities incorporated during annealing in the molecular beam epitaxy chamber. Photoluminescence images reveal that at least two impurities diffuse into silicon wafers during molecular beam epitaxy annealing. One is highly localized, while the other one is distributed uniformly across the whole wafer. Phosphorus diffusion is found to confine the localized impurity within the diffused layer but is ineffective at preventing the indiffusion of other impurities. Lifetime spectroscopy shows that metastable impurities with characteristic similar to Cr and CrB in our molecular beam epitaxy annealed silicon wafers. No evidence of Fe or FeB was observed. The emission rates and concentrations of the electrically active defects were measured with deep-level transient spectroscopy: The emission rates of detected defects do not match that of known Cr-related defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Linear response of molecular polaritons

In this article, we show that the collective light–matter strong coupling regime where N molecular emitters couple to the photon mode of an optical cavity can be mapped to a quantum impurity model where the photon is the impurity that is coupled to a bath of anharmonic transitions. In the thermodynamic limit where N ≫ 1, we argue that the bath can be replaced with an effective harmonic bath, leading to a dramatic simplification of the problem into one of the coupled harmonic oscillators. We derive simple analytical expressions for linear optical spectra (transmission, reflection, and absorption) where the only molecular input required is the molecular linear susceptibility. This formalism is applied to a series of illustrative examples, showing the role of temperature, disorder, vibronic coupling, and optical saturation of the molecular ensemble, explaining that it is useful even when describing an important class of nonlinear optical experiments. For completeness, we provide Appendixes A–C that include a self-contained derivation of the relevant spectroscopic observables for arbitrary anharmonic systems (for both large and small N) within the rotating-wave approximation. While some of the presented results herein have already been reported in the literature, we provide a unified presentation of the results as well as new interpretations that connect powerful concepts in open quantum systems and linear response theory with molecular polaritonics.

Chemistry↗
DOE OSTI2025

Neuromorphic heat transport effects in a molecular junction

Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dependent driving forces, particularly in the far-from-equilibrium regime. In this work, we use nonequilibrium molecular dynamics simulations and stochastic thermodynamics to investigate energy transport in a well-studied nanoscale system—a molecular junction—subjected to a time-periodic temperature gradient. The primary observation is that molecular junctions can exhibit heat transport hysteresis, a phenomenon in which the heat flux through a system depends not only on the instantaneous value of a time-dependent temperature bias but also on the temporal history of that bias. The presented findings illustrate that molecular junctions can exhibit the specific memory effect—heat transport hysteresis—that is essential for the design of thermal neuromorphic computers. This work elucidates a potential pathway toward the realization of such devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

MASCOT: an ESO–ARO legacy survey of molecular gas in nearby SDSS-MaNGA galaxies - I. First data release, and global and resolved relations between H 2 and stellar content

We present the first data release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA)–Arizona Radio Observatory (ARO) Survey of CO Targets (MASCOT), a European Southern Observatory public spectroscopic survey conducted at the ARO. We measure the CO(1–0) line emission in a sample of 187 nearby galaxies selected from the MaNGA survey that has obtained integral field unit (IFU) spectroscopy for a sample of ~10 000 galaxies at low redshift. The main goal of MASCOT is to probe the molecular gas content of star-forming galaxies with stellar masses >10 9.5 M ⊙ and with associated MaNGA IFU observations and well-constrained quantities like stellar masses, star formation rates, and metallicities. In this paper, we present the first results of the MASCOT survey, providing integrated CO(1–0) measurements that cover several effective radii of the galaxy and present CO luminosities, CO kinematics, and estimated H 2 gas masses. We observe that the decline of the galaxy star formation rate with respect to the star formation main sequence increases with the decrease of molecular gas and with a reduced star formation efficiency, in agreement with results of other integrated studies. Relating the molecular gas mass fractions with the slope of the stellar age gradients inferred from the MaNGA observations, we find that galaxies with lower molecular gas mass fractions tend to show older stellar populations close to the galactic centre, while the opposite is true for galaxies with higher molecular gas mass fractions, providing tentative evidence for inside-out quenching.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Star formation and molecular gas properties of post-starburst galaxies

Post-starburst galaxies are believed to be in a rapid transition between major merger starbursts and quiescent ellipticals. Their optical spectrum is dominated by A-type stars, suggesting a starburst that was quenched recently. While optical observations suggest little ongoing star formation, some have been shown to host significant molecular gas reservoirs. This led to the suggestion that gas depletion is not required to end the starburst, and that star formation is suppressed by other processes. We present NOEMA CO(1−0) observations of 15 post-starburst galaxies with emission lines consistent with active galactic nucleus (AGN) photoionization. We collect post-starburst candidates with molecular gas measurements from the literature, with some classified as classical E + A, while others with line ratios consistent with AGN and/or shock ionization. Using far-infrared observations, we show that systems that were reported to host exceptionally large molecular gas reservoirs host in fact obscured star formation, with some systems showing star formation rates comparable to ULIRGs. Among E + A galaxies with molecular gas measurements, 7 out of 26 (26 per cent) host obscured starbursts. Using far-infrared observations, post-starburst candidates show similar SFR– M H 2 and Kennicutt–Schmidt relations to those observed in star-forming and starburst galaxies. In particular, there is no need to hypothesize star formation quenching by processes other than the consumption of molecular gas by star formation. The combination of optical, far-infrared, and CO observations indicates that some regions within these galaxies have been recently quenched, while others are still forming stars in highly obscured regions. All this calls into question the traditional interpretation of such galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Properties of molecular clumps and cores in colliding magnetized flows

ABSTRACT We simulate the formation of molecular clouds in colliding flows of warm neutral medium with the adaptive mesh refinement code flash in eight simulations with varying initial magnetic field strength, between 0.01–5 μG. We include a chemical network to treat heating and cooling and to follow the formation of molecular gas. The initial magnetic field strength influences the fragmentation of the forming cloud because it prohibits motions perpendicular to the field direction and hence impacts the formation of large-scale filamentary structures. Molecular clump and core formation occurs anyhow. We identify 3D clumps and 3D cores, which are defined as connected, CO-rich regions. Additionally, 3D cores are heavily shielded. While we do not claim those 3D objects to be directly comparable to observations, this enables us to analyse their full virial state. With increasing field strength, we find more fragments with a smaller average mass; yet the dynamics of the forming clumps and cores only weakly depends on the initial magnetic field strength. The molecular clumps are mostly unbound, probably transient objects, which are weakly confined by ram pressure or thermal pressure, indicating that they are swept up by the turbulent flow. They experience significant fluctuations in the mass flux through their surface, such that the Eulerian reference frame shows a dominant time-dependent term due to their indistinct nature. We define the cores to encompass highly shielded molecular gas. Most cores are in gravitational-kinetic equipartition and are well described by the common virial parameter $\alpha _\mathrm{vir}$, while some undergo minor dispersion by kinetic surface effects.

Weis, M. (ORCID:0000000256838860)↗
DOE OSTI2024

A Deep Multimodal Representation Learning Framework for Accurate Molecular Properties Prediction

Drug discovery is a complex and challenging process, requiring the optimization of candidate compounds to identify those with the potential to become safe and effective drugs. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly and time-intensive, involving multiple rounds of experiments and clinical trials, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost and time required to identify novel drugs. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, which utilize a single modality of data, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep \textbf{M}ultimodal \textbf{R}epresentation \textbf{L}earning framework for accurate \textbf{Mol}ecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset~($\sim$1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets, including both classification and regression tasks. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.

Yang, Yuxin↗
DOE OSTI2020

Effect of Natural Fermentation of Sorghum on Resistant Starch Molecular Structure and Fermentation Property

Relevant research results have suggested that fermentation can increase the content of sorghum amylose chains and their retrogradation value. Therefore, this study explored the effect of fermentation pretreatment on the yield, digestibility, molecular structure, and in vitro fermentation property of sorghum-resistant starch by conducting fermentation pretreatment of sorghum and extracting the resistant starch from fermented sorghum with pressure-heat compound enzyme method. The results were as follows. After fermentation pretreatment, the yield of sorghum-resistant starch increased, the digestibility of sorghum-resistant starch reduced, the laminated structure size on the surface of the particles became more uniform, and the stacking mode became more neat and denser. The sorghum-resistant starch prepared before and after fermentation did not produce new chemical groups, and its functional group peak remained unchanged. After fermentation, the weight-average molecular weight of sorghum-resistant starch was elevated, and the percentage content of high- and low-molecular substances increased and decreased, respectively, compared with that of the unfermented sorghum-resistant starch. The percentage content of short-chain branches in the branched chain increased, whereas that of the long-chain branches decreased; the crystallinity of sorghum-resistant starch after fermentation decreased, and the intensity of X-diffraction peak changed slightly before and after fermentation. According to the results of the in vitro fermentation experiments, the fermentation broth of sorghum-resistant starch had the highest content of butyric acid and short-chain fatty acid. Research results reveal that, after fermentation pretreatment, sorghum-resistant starch presented increased yield, more complex molecular structure, heavier molecular weight and more uniform surface morphology, more efficient butyric acid generation, and greater fermentation rate than unfermented sorghum-resistant starch.

Ge, YunFei↗
DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗
DOE OSTI2026

Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules’ natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system’s virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART’s capabilities through imaging of tissue sections, cytopathology specimens, blood cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.

60 APPLIED LIFE SCIENCES↗