SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.
Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.
Self-powered biosensors are innovative devices that can detect and analyze biological or chemical substances without the need for an external power source. These biosensors can convert energy from the surrounding environment or the analyte itself into electrical signals for sensing and data transmission. The self-powered nature of these biosensors offers several advantages, such as portability, autonomy, and reduced waste generation from disposable batteries. They find applications in various fields, including healthcare, environmental monitoring, food safety, and wearable devices. While self-powered biosensors are a promising technology, there are still challenges to address, such as improving energy efficiency, sensitivity, and stability to make them more practical and widely adopted. This review article focuses on exploring the evolving trends in self-powered biosensor design, outlining potential advantages and limitations. With a focal point on enzymatic biofuel cell power generation, this article describes various sensing mechanisms that employ the analyte as substrate or fuel for the biocatalyst’s ability to generate current. Technical aspects of biofuel cells are also examined. Research and development in the field of self-powered biosensors is ongoing, and this review describes promising areas for further exploration within the field, identifying underexplored areas that could benefit from further investigation.
Phase transitions happen at critical values of the controlling parameters, such as the critical temperature in classical phase transitions, and system critical parameters in the quantum case. However, true criticality happens only at the thermodynamic limit, when the number of particles goes to infinity with constant density. To perform the calculations for the critical parameters, a finite-size scaling approach was developed to extrapolate information from a finite system to the thermodynamic limit. With the advancement in the experimental and theoretical work in the field of ultra-cold systems, particularly trapping and controlling single atomic and molecular systems, one can ask: do finite systems exhibit quantum phase transition? To address this question, finite-size scaling for finite systems was developed to calculate the quantum critical parameters. The recent observation of a quantum phase transition in a single trapped 171 Yb + ion indicates the possibility of quantum phase transitions in finite systems. This perspective focuses on examining chemical processes at ultra-cold temperatures, as quantum phase transitions—particularly the formation and dissociation of chemical bonds—are the basic processes for understanding the whole of chemistry.
Machine learning (ML) has become a central focus of the computational chemistry community. In this paper, I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low-cost methods (e.g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open-shell transition-metal chemistry.
There are many circumstances in which complex gas-phase reaction mixtures must be interrogated and understood, including combustion, plasmas, atmospheres, and molecular clouds. Spectroscopic tools are essential to selective detection and characterization of the widening array of precursors and the reactive intermediates they generate. This research program has focused on the development and implementation of sensitive spectroscopic detection schemes that are isomer-specific and even conformation-specific. In particular, we used an array of laser-based and broadband microwave methods to carry out isomer-specific and conformation-specific spectroscopy on large flexible molecules and reactive intermediates formed by photolysis, pyrolysis, combustion, or electron-driven chemistry.
The theoretical characterization of actinide molecules is a crucial complement to their experimental study; actinides are vital to issues of national security and energy, but they are expensive and hazardous to study experimentally. However, because actinides contain a large number of electrons and are often strongly correlated, their theoretical description is difficult. We have had success studying actinide chemistry and physics using density functional theoretical approaches over the past several years, but we have found that such methods are incapable of treating strong correlations adequately. We thus shifted our research focus to the development of tractable wavefunction methods for strong correlations of actinides. Our early wavefunction-based approaches focused on extending symmetry-adapted, single-reference coupled cluster for treating strong correlations with feasible computational cost. While we have had some success along these lines, we have recently developed spin-projected unrestricted coupled cluster, which is essentially a black-box multi-reference coupled cluster theory that is superior to unrestricted coupled cluster for small- to medium-sized systems across practically all physical correlation strengths, yet preserves good quantum numbers. For actinides, however, where spin-orbit coupling is often nonnegligible, spin is no longer a fundamental symmetry. We thus propose to develop time reversal and point group projected coupled cluster, which will preserve the fundamental symmetries for actinides. Time reversal projection is achieved via the product of half-spin projection, or spin flip, and complex conjugation projection. Along with point group, these are discrete symmetries, i.e. non-continuous quantum numbers, which, in addition to being the correct symmetries for actinides, can be implemented in lower computational cost than full spin projection. We propose the development of these theories within a synergistic collaboration to use the new methods to elucidate difficult actinide chemistry.
Plutonium plays a critical role in the development of sustainable nuclear fuel cycles, and yet, our fundamental understanding of this element’s inherent radiation-induced redox chemistry and associated impacts on nuclear fuel cycle technologies is limited. Unanticipated changes in oxidation state distribution can influence the speciation and transport of plutonium in a given process. Control of these parameters is especially important for used nuclear fuel reprocessing technologies, wherein the separation and recovery of plutonium is typically achieved by the selective formation, maintenance, and complexation of specific oxidation states. Furthermore, plutonium’s inherent radiation-induced redox chemistry has the capacity to influence the radiolytic behavior of its complexes, the longevity of which are critical in the design of efficient and cost-effective advanced reprocessing technologies. These radiation-induced processes are unavoidable under fuel cycle conditions owing to the inherency of ionizing radiation fields to the decay of plutonium’s isotopes and to the various other radioisotopes generated by nuclear fission and neutron-capture process and the subsequent radioactive decay of their products. As such, mechanistically understanding the response of plutonium’s multiple oxidation states to multi-component ionizing radiation fields is essential for predicting the behavior of this critical element under used nuclear fuel reprocessing conditions. Here, through a combination of time-resolved (electron pulse) and steady-state (alpha and gamma) irradiation experiments complemented by quantitative, multiscale modeling calculations, we present advances in our understanding of radiation-induced plutonium redox chemistry!
Due to their rarity and radioactive nature, comparatively little is known about the actinides, particularly those with atomic numbers higher than that of plutonium, and their compounds. In this work, we describe how transmission electron microscopy can provide comprehensive, safe, and cost-effective characterization using only single nanogram amounts of highly-radioactive, solid compounds. Chlorides of the rare elements berkelium and californium are dropcast and then converted in situ to oxides using the electron beam. The f-band occupancies are probed using electron energy loss spectroscopy and an unexpectedly weak spin-orbit-coupling is identified for berkelium. In contrast, californium follows a jj coupling scheme. These results have important implications for the chemistries of these elements and solidify the status of californium as a transitional element in the actinide series.
Isotope harvesting is a method of collecting the long-lived radioisotopes that build up during the operation of ion-beam facilities in a way that is useful for subsequent research. As a demonstration of this method for the collection of a group IV metal at a fragmentation facility, the high-energy Zr 88 secondary beam produced from a 140-MeV/u Mo 92 primary beam at the National Superconducting Cyclotron Laboratory (NSCL) was stopped in a water target. The setup aimed to mimic the aqueous beam dump that will be implemented at the Facility for Rare Isotope Beams (FRIB). The collected Zr 88 and accompanying Y 88 decay daughter were radiochemically extracted from the solution and made into target samples suitable for neutron-capture cross-section measurements. These samples were then irradiated at two reactor facilities, and the Zr 88 average thermal-neutron-capture cross section ( σ T ) and resonance integral ( I ) were determined to be σ T = ( 8.04 ± 0.63 ) × 10 5 b and I = ( 2.53 ± 0.28 ) × 10 6 b. The σ T value agrees well with previous results and I , determined for the first time here, was found to be the largest measured resonance integral by two orders of magnitude. The Y 88 thermal-neutron-capture cross section was determined to be less than 1.8 × 10 4 b. This work demonstrates the steps needed to make cross-section measurements with samples produced via aqueous isotope harvesting.
Dynamic catalysis—the forced oscillation of catalytic reaction coordinate potential energy surfaces (PES)—has recently emerged as a promising method for the acceleration of heterogeneously-catalyzed reactions. Theoretical study of enhancement of rates and supra-equilibrium product yield via dynamic catalysis has, to-date, been severely limited by onerous computational demands of forward integration of stiff, coupled ordinary differential equations (ODEs) that are necessary to quantitatively describe periodic cycling between PESs. Here, we establish a new approach that reduces, by ≳108×, the computational cost of finding the time-averaged rate at dynamic steady state (i.e. the limit cycle for linear and nonlinear systems of kinetic equations). Our developments are motivated by and conceived from physical and mathematical insight drawn from examination of a simple, didactic case study for which closed-form solutions of rate enhancement are derived in explicit terms of periods of oscillation and elementary step rate constants. Generalization of such closed-form solutions to more complex catalytic systems is achieved by introducing a periodic boundary condition requiring the dynamic steady state solution to have the same periodicity as the kinetic oscillations and solving the corresponding differential equations by linear algebra or Newton-Raphson-based approaches. The methodology is well-suited to extension to non-linear systems for which we detail the potential for multiple solutions or solutions with different periodicities. For linear and non-linear systems alike, the acute decrement in computational expense enables rapid optimization of oscillation waveforms and, consequently, accelerates understanding of the key catalyst properties that enable maximization of reaction rates, conversions, and selectivities during dynamic catalysis.
Recent studies have shown that dynamic nuclear polarization (DNP) can be used to detect 17 O solid-state NMR spectra of naturally abundant samples within a reasonable experimental time. Observations using indirect DNP, which relies on 1 H mediation in transferring electron hyperpolarization to 17 O, are currently limited mostly to hydroxyls. Direct DNP schemes can hyperpolarize non-protonated oxygen near the radicals; however, they generally offer much lower signal enhancements. In this study, we demonstrate the detection of signals from non-protonated 17 O in materials containing silicon. The sensitivity boost that made the experiment possible originates from three sources: indirect DNP excitation of 29 Si via protons, indirect detection of 17 O through 29 Si nuclei using two-dimensional 29 Si{ 17 O} D-HMQC, and Carr-Purcell-Meiboom-Gill refocusing of 29 Si magnetization during acquisition. This 29 Si-detected scheme enabled, for the first time, 2D 17 O– 29 Si heteronuclear correlation spectroscopy in mesoporous silica and silica-alumina surfaces at natural abundance. In contrast to the silanols showing motion-averaged 17 O signals, the framework oxygens exhibit unperturbed powder patterns as unambiguous fingerprints of surface sites. Furthermore, along with hydroxyl oxygens, detection of these moieties will help in gaining more atomistic-scale insights into surface chemistry.
Advances in nanophotonics, quantum optics, and low-dimensional materials have enabled precise control of light–matter interactions down to the nanoscale. Combining concepts from each of these fields, there is now an opportunity to create and manipulate photonic matter via strong coupling of molecules to the electromagnetic field. Toward this goal, here we demonstrate a first principles framework to calculate polaritonic excited-state potential-energy surfaces, transition dipole moments, and transition densities for strongly coupled light–matter systems. In particular, we demonstrate the applicability of our methodology by calculating the polaritonic excited-state manifold of a formaldehyde molecule strongly coupled to an optical cavity. This proof-of-concept calculation shows how strong coupling can be exploited to alter photochemical reaction pathways by influencing avoided crossings with tuning of the cavity frequency and coupling strength. Therefore, by introducing an ab initio method to calculate excited-state potential-energy surfaces, our work opens a new avenue for the field of polaritonic chemistry.