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In 2013, the Pacific Northwest National Laboratory led a geologic carbon sequestration field demonstration where ~1000 tonnes of CO 2 was injected into several deep Columbia River Basalt zones near Wallula, Washington. Rock core samples extracted from the injection zone two years after CO 2 injection revealed nascent carbonate mineralization that was qualitatively consistent with expectations from laboratory experiments and reactive transport modeling. In this study, we report on a new detailed analysis of the 2012 pre-injection and 2015 post-injection hydrologic tests that capitalizes on the difference in fluid properties between scCO 2 and water to assess changes in near-field, wellbore, and reservoir conditions that are apparent approximately two years following the end of injection. This comparative hydrologic test analysis method provides a new way to quantify the amount of injected CO 2 that was mineralized in the field test. Modeling results indicate that approximately 60% of the injected CO 2 was sequestered via mineralization within two years, with the resulting carbonates occupying ~4% of the available reservoir pore space. The method presented here provides a new monitoring tool to assess the fate of CO 2 injected into chemically reactive basalt formations but could also be adapted for long-term monitoring and verification within more traditional subsurface carbon storage reservoirs.
This paper is the second part of a study on the effects of a substituted 5'-phenylenevinylene (PV) functionality on the emission properties of 2-(2'-hydroxyphenyl)benzoxazole (HBO)-a dye that is known for excited-state intramolecular proton transfer. The topical compounds are referred to as PVHBOs, each of which is a structural fusion of HBO and a 4-hydroxy-4'-R-stilbene fluorophore that occurs at the hydroxyphenyl moiety. Therefore, the resulting fusion fluorophore manifests the properties of one component or the other, as governed by its interactions with the environment. In part I (the preceding paper), PVHBOs are divided into two groups depending on whether the R substituent is electron-donating/neutral (group I) or electron-withdrawing (group II). The difference in absorption and emission properties between groups I and II is explained based on observations from spectroscopic experiments (both steady-state and time-resolved) and quantum chemical calculations. In the current paper, the same set of tools is applied to characterize the photophysical properties of the conjugate bases-that is, the anions-of PVHBOs. The emission energy of the anion of any group I compound, where the R substituent is either electron-donating or neutral, is situated between those of the neutral enol and keto forms. The emission of the anion of any given group II compound, on the other hand, has a lower energy than both the enol and keto emissions. In this work, the frontier molecular orbitals (i.e., HOMO, LUMO, and LUMO + 1) of a PVHBO localized on either HBO or stilbenoid are impacted by the substituent R and the solvent/additive differently, which leads to the differences in the optical properties of group I and II PVHBOs in both neutral and anion forms.
Colloidal quantum dots (QDs) are semiconductor nanocrystals that have unique size-tunable optoelectronic properties and are suitable for wet processing. QD research aims to answer fundamental questions about the chemical and physical properties of nanoscale materials and use these tools for technological applications ranging from bio-imaging to quantum optics. At the core of this field is a set of synthetic, processing and analytical methods designed to produce QDs in uniform ensembles that meet the highest performance standards. Here, this Primer reviews QD fabrication methods with a focus on the applications of QDs in printed optoelectronics and quantum optics. After outlining the current state-of-the-art QD syntheses, the experimental and computational analysis of QDs is discussed. These topics are then connected to the methodologies, processes and concepts required for developing QD-based photodetectors, light-emitting devices and quantum optics applications. Special attention is paid to challenges in reproducibility and current limitations of the field, such as the need to balance non-restricted material composition with high performing technology while achieving long-term stability in QD devices under operating conditions. Finally, the ongoing advancement in QD synthesis, precise atomic-level analysis and computational methodologies are highlighted as key drivers towards rational QD design, particularly in understanding how structural changes under loading impact QD properties.
Stimulated Raman scattering (SRS) microscopy using picosecond near-IR pulses have provided a great penetration depth with reduced fluorescence interference when imaging biological samples for bioenergy applications. These tools have provided insight into 1) tracking the degradation of chemical composites in biomass feedstocks to investigate the recalcitrant factors during the deconstruction processes, 2) monitoring the production of chemicals in photosynthetic plants and wood-digesting microorganisms, and 3) probing plant-bacteria interactions. However, the above processes are usually slow and require continuous imaging for an extended period. This is challenging for classic SRS because the laser power needed to achieve enough sensitivity causes photodamage in the samples during such long experiments. Quantum-squeezed light with reduced noise in the intensity quadrature can improve the sensitivity of classic SRS microscopy beyond the shot noise limit. The successful squeezing of one of the picosecond pulses in the above SRS will improve sensitivity and reduce photodamage, greatly expanding the range of studies available to SRS microscopy.
Catalysis science is founded on understanding the structure, number, and reactivity of active sites. Kinetic models that consider active sites to be static and noninteracting entities are routinely successful in describing the behavior of heterogeneous catalysts. Yet, active site ensembles often restructure in response to their external environment and even during steady-state catalytic turnover, sometimes requiring non-mean-field kinetic treatments to describe distance-dependent interactions among sites. Such behavior is being recognized more frequently in modern catalysis research, with the advent of experimental methods to quantify turnover rates with increasing precision, an expanding arsenal of operando characterization tools, and computational descriptions of atomic structure and motion at chemical potentials and timescales increasingly relevant to reaction conditions. This review focuses on dynamic changes to metal active site ensembles on zeolite supports, which are silica-based crystalline materials substituted with Al that generate binding sites for isolated and low-nuclearity metal site ensembles. Metal sites can become solvated and mobilized during reaction, facilitating interactions among sites that change their nuclearity and function. Such intersite communication can be regulated by the zeolite support, resulting in non-single-site and potentially non-mean-field kinetic behavior arising from mechanisms of catalytic action that combine elements of those canonically associated with homogeneous and heterogeneous catalysis. Here, we discuss recent literature examples that document dynamic active site behavior in metal-zeolites and outline methodologies to identify and interpret such behavior. We conclude with our outlook on future research directions to develop this evolving branch of catalysis science and harness it for practical applications.
Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology ( e . g ., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.
All-solid-state lithium metal batteries hold tremendous potential as next-generation energy storage solutions. However, their performance is hindered by complex chemo-mechanical instabilities at the interface, which encompass electrolyte decomposition, loss of contact due to volume changes, and the growth of lithium filaments. Given the complexity of these failure mechanisms, it is imperative to employ multidimensional diagnostics that leverage a combination of tools capable of quantifying formed voids and filaments, identifying the chemical and mechanical properties of lithium dendrites and electrolyte decomposition products, as well as monitoring the evolution of these processes in situ. The primary objective of this project is to develop a comprehensive characterization platform for solid-state battery interfaces, integrating FIB-SEM tomography, ToF-SIMS, in-SEM nanoindentation, and in-SIMS AFM-based stiffness mapping to enable structural, chemical, and mechanical analyses. The project team has accomplished significant milestones, including: 1) The successful development of a characterization platform that combines FIB-SEM, ToF-SIMS, nanoindentation, and AFM. 2) The creation of solid-state cells at various scales. 3) Real-time visualization of the plating and stripping of lithium. 4) Operando analysis of the chemical composition and evolution of electrolyte decomposition products, encompassing intermediate and metastable species, at both the cathode and anode interfaces. 5) In situ analysis of mechanical properties coupled with chemical mapping. 6) The implementation of the multiscale characterization technique has yielded valuable insights into cell failure mechanisms and has led to the development of a solid-state lithium cell that exhibits exceptional durability, surpassing 2500 cycles. This project has resulted in 10 publications, 1 invention disclosure, 8 manuscripts under review or in preparation.
The tight atomic packing generally exhibited by alloys and intermetallics can create the impression of their being composed of hard spheres arranged to maximize their density. As such, the atomic size factor has historically been central to explanations of the structural chemistry of these systems. However, the role atomic size plays structurally has traditionally been inferred from empirical considerations. The recently developed DFT-Chemical Pressure (CP) analysis has opened a path to investigating these effects with theory. In this article, we provide a step-by-step tutorial on the DFT-CP method for non-specialists, along with advances in the approach that broaden its applicability. A new version of the CP software package is introduced, which features an interactive system that guides the user in preparing the necessary electronic structure data and generating the CP scheme, with the results being readily visualized with a web browser (and easily incorporated into websites). For demonstration purposes, we investigate the origins of the crystal structure of K3Au5Tl, which represents an intergrowth of Laves and Zintl phase domains. Here, CP analysis reveals that the intergrowth is supported by complementary CP features of NaTl-type KTl and MgCu2-type KAu2 phases. In this way, K3Au5Tl exemplifies how CP effects can drive the merging for geometrical motifs derived from different families of intermetallics through a mechanism referred to as epitaxial stabilization.
Cubic bi-magnetic hard–soft core–shell nanoarchitectures were prepared starting from cobalt ferrite nanoparticles, prevalently with cubic shape, as seeds to grow a manganese ferrite shell. The combined use of direct (nanoscale chemical mapping via STEM-EDX) and indirect (DC magnetometry) tools was adopted to verify the formation of the heterostructures at the nanoscale and bulk level, respectively. The results showed the obtainment of core–shell NPs (CoFe 2 O 4 @MnFe 2 O 4 ) with a thin shell (heterogenous nucleation). In addition, manganese ferrite was found to homogeneously nucleate to form a secondary nanoparticle population (homogenous nucleation). This study shed light on the competitive formation mechanism of homogenous and heterogenous nucleation, suggesting the existence of a critical size, beyond which, phase separation occurs and seeds are no longer available in the reaction medium for heterogenous nucleation. These findings may allow one to tailor the synthesis process in order to achieve better control of the materials’ features affecting the magnetic behaviour, and consequently, the performances as heat mediators or components for data storage devices.
An analysis of the diffuse interstellar cloud complex in front of HD 179406 (20 Aql) is presented. Along this sight line, multispectral absortion- and emission-line studies have uncovered at least three distinct velocity components due to individual clouds. A dominant velocity component is seen in both the absorption and emission-line data sets at 3 +/- 1 km/s. It is argued that the cloud associated with this velocity component is responsible for most of the atomic and all of the molecular gas in front of 20 Aql. The present chemical and physical analysis of the cloud combines the diagnostic tools of radio emission-line data with those of UV and optical absorption data. Using non-LTE models to synthesize the observed absorption profiles, (C-12)O and (C-13)O column densities along this line of sight are determined. The (C-12)O/(C-13)O abundance ratio was found to be 50 +/- 15, similar to that found by Wannier et al. toward Zeta Oph. The physical conditions of the cloud have been investigated using ultraviolet absorption lines. Measurements indicate that the dominant absorption cloud has a gas pressure similar to that found in the local diffuse molecular cloud in Ophiuchus with nT = 20,000/cu cm K.
Small chemical rockets are used on nearly all space missions. The small rocket program provides propulsion technology for civil and government space systems. Small rocket concepts are developed for systems which encompass reaction control for launch and orbit transfer systems, as well as on-board propulsion for large space systems and earth orbit and planetary spacecraft. Major roles for on-board propulsion include apogee kick, delta-V, de-orbit, drag makeup, final insertions, north-south stationkeeping, orbit change/trim, perigee kick, and reboost. The program encompasses efforts on earth-storable, space storable, and cryogenic propellants. The earth-storable propellants include nitrogen tetroxide (NTO) as an oxidizer with monomethylhydrazine (MMH) or anhydrous hydrazine (AH) as fuels. The space storable propellants include liquid oxygen (LOX) as an oxidizer with hydrazine or hydrocarbons such as liquid methane, ethane, and ethanol as fuels. Cryogenic propellants are LOX or gaseous oxygen (GOX) as oxidizers and liquid or gaseous hydrogen as fuels. Improved performance and lifetime for small chemical rockets are sought through the development of new predictive tools to understand the combustion and flow physics, the introduction of high temperature materials to eliminate fuel film cooling and its associated combustion inefficiency, and improved component designs to optimize performance. Improved predictive technology is sought through the comparison of both local and global predictions with experimental data. Results indicate that modeling of the injector and combustion process in small rockets needs improvement. High temperature materials require the development of fabrication processes, a durability data base in both laboratory and rocket environments, and basic engineering property data such as strength, creep, fatigue, and work hardening properties at both room and elevated temperature. Promising materials under development include iridium-coated rhenium and a ceramic composite of mixed hafnium carbide and tantalum carbide reinforced with graphite fibers.
Prompted by the needs of downstream plume-wake models, the Massachusetts Institute of Technology (MIT) and Aerodyne Research Incorporated (ART) initiated a collaborative effort, with funding from the NASA AEAP, to develop tools that would assist in understanding the fundamental drivers of chemical change within the intra-engine exhaust flow path. Efforts have been focused on the development of a modeling methodology that can adequately investigate the complex intra-engine environment. Over the history of this project, our research has increasingly pointed to the intra-engine environment as a possible site for important trace chemical activity. Modeling studies we initiated for the turbine and exhaust nozzle have contributed several important capabilities to the atmospheric effects of aviation assessment. These include a more complete understanding of aerosol precursor production, improved initial conditions for plume-wake modeling studies, and a more comprehensive analysis of ground-based test cell and in-flight exhaust measurement data. In addition, establishing a physical understanding of important flow and chemical processes through computational investigations may eventually assist in the design of engines to reduce undesirable species.
The atomic force microscope (AFM) is used to inject a sample, provide shear-driven liquid flow over a functionalized substrate, and detect separated components. This is demonstrated using lipophilic dyes and normal phase chromatography. A significant reduction in both size and separation time scales is achieved with a 25-micron-length column scale, and one-second separation times. The approach has general applications to trace chemical and microfluidic analysis. The AFM is now a common tool for ultra-microscopy and nanotechnology. It has also been demonstrated to provide a number of microfluidic functions necessary for miniaturized chromatography. These include injection of sub-femtoliter samples, fluidic switching, and sheardriven pumping. The AFM probe tip can be used to selectively remove surface layers for subsequent microchemical analysis using infrared and tip-enhanced Raman spectroscopy. With its ability to image individual atoms, the AFM is a remarkably sensitive detector that can be used to detect separated components. These diverse functional components of microfluidic manipulation have been combined in this work to demonstrate AFM mediated chromatography. AFM mediated chromatography uses channel-less, shear-driven pumping. This is demonstrated with a thin, aluminum oxide substrate and a non-polar solvent system to separate a mixture of lipophilic dyes. In conventional chromatographic terms, this is analogous to thin-layer chromatography using normal phase alumina substrate with sheardriven pumping provided by the AFM tip-cantilever mechanism. The AFM detection of separated components is accomplished by exploiting the variation in the localized friction of the separated components. The AFM tip-cantilever provides the mechanism for producing shear-induced flows and rapid pumping. Shear-driven chromatography (SDC) is a relatively new concept that overcomes the speed and miniaturization limitations of conventional liquid chromatography. SDC is based on a sliding plate system, consisting of two flat surfaces, one of which has a recessed channel. A fluid flow is produced by axially sliding one plate past another, where the fluid has mechanical shear forces imposed at each point along the channel length. The shear-induced flow rates are very reproducible, and do not have pressure or voltage gradient limitations. SDC opens up a new range of enhanced separation kinetics by permitting the sample confinement with submicron dimensions. Small, highly confined liquid is advantageous for chromatographic separation because the separation rate is known to scale according to the square of the confined sample diameter. In addition, because shear-driven flows are not limited by fluid velocity, shear-driven liquid chromatography may provide up to 100,000 plate efficiency.
While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.
Nonlinear modeling and optimization is a valuable tool for aiding decisions by engineering practitioners, but programming an optimization problem based on a complex electrical, mechanical, or chemical process is a time-consuming and error-prone activity. Therefore, there is a need for model analysis and debugging tools that can detect and diagnose modeling errors. One such tool is the Dulmage–Mendelsohn decomposition, which identifies structurally under- and over-determined subsets in systems of equations and variables by partitioning the bipartite graph of the system. This work provides the necessary background to understand the Dulmage–Mendelsohn decomposition and its application to the analysis of nonlinear optimization problems, demonstrates its use in diagnosing a variety of modeling errors, and introduces software implementations for analyzing nonlinear optimization problems in the Pyomo and JuMP algebraic modeling languages.
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.
Reactive molecular dynamics (MD) is a powerful tool for atomistic-scale modeling of a diverse range of chemical processes. However, scaling these simulations to large systems and long times scales remains a challenge because of the complexity of the potential energy function required. The authors previously developed a heuristic approach, called REACTER, that incorporates reactivity in MD simulations in a less general but much more computationally efficient manner. REACTER uses standard, fixed valence force fields as the underlying potentialenergy surface for describing all interatomic interactions but adds a procedure for enforcing user-defined reactions that occur when certain geometric constraints on relative atomic positions are satisfied. Further, these bonding changes can be accepted or rejected with a probability related tothe local thermal energy. This work seeks to generalize this approach by replacing the set of user defined geometric constraints and energetic criteria with a committor function that specifies the probability of a reaction occurring on the basis of the local atomic configuration. The committor function is a useful mathematical tool for modeling rare events but, unfortunately, is very difficult to compute for realistic systems in a general way. This work describes a method for approximating the committor function using a machine learning approach, specifically a deep neural network trained with data from reactive MD and DFT-based dynamics simulations. This network is coupled to the existing REACTER protocol, as implemented in the LAMMPS MD package, and used to make on-the-fly predictions of reaction probabilities without the more extensive user input previously required. The new method is demonstrated using the polymerization of polystyrene as a case study. Although very dependent on the quality and quantity of training data, machine-learned committor functions show promise as a method for incorporating reaction probability from higher level calculations into highly scalable MD simulations.