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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Investigation of post-injection strategies for diesel engine Catalyst Heating Operation using a vapor-liquid-equilibrium-based spray model

Most multidimensional engine simulations spend much time solving for non-equilibrium spray dynamics (atomization, collision, vaporization). However, their accuracy is limited by significant grid dependency, and the need for extensive calibration. This is critical for modeling cold-start diesel fuel post injections, which occur at low temperatures and pressures, far from typical model validation ranges. At the same time, resolving micron-scale spray phenomena would render full Eulerian multiphase calculations prohibitive. In this study, an improved phase equilibrium based approach was implemented and assessed for simulating diesel catalyst heating operation strategies. A phase equilibrium solver based on the model by Yue and Reitz [1] was implemented: a fully multiphase CFD solver is employed with an engineering-size engine grid, and fuel injection is modeled using the standard Lagrangian parcels approach. Mass and energy from the liquid parcels are released to the Eulerian multiphase mixture according to an equilibrium-based liquid jet model. An improved phase equilibrium solver was developed to handle large real-gas mixtures such as those from accurate chemical kinetics mechanisms. The liquid-jet model was improved such that momentum transfer to the Eulerian solver better reproduces the physical spray jet structure. Validation of liquid/vapor penetration predictions showed that the model yields accurate results with very limited tuning and low sensitivity to the few calibration constants. In-cylinder simulations of diesel catalyst heating operation strategies showed that capturing spray structure is paramount when short, transient injection pulses and low temperatures are present. Furthermore, the EP model provides improved predictions of post-injection spray structure and ignitability, while conventional spray modeling does not capture the increase of liquid penetration during the expansion stroke. Finally, the only important EP model calibration constant, Cliq, does not affect momentum transfer, but it changes the local charge cooling distribution through the local energy transfer, which makes it candidate to additional research. The results confirm that non-equilibrium spray processes do not need to be resolved in engineering simulations of high-pressure diesel sprays.

42 ENGINEERING↗
DOE OSTI2023

Environmental life cycle assessment of treatment and management strategies for food waste and sewage sludge

A consequential life cycle assessment (LCA) was utilized to compare the environmental impacts of food waste and sewage sludge management strategies. The strategies included a novel two-phase anaerobic digestion (AD) system and alternatives including landfill, waste-to-energy, composting, anaerobic membrane bioreactor, and conventional AD (wet continuous stirred-tank reactor [CSTR]). The co-management of food waste with sewage sludge was also considered for the two-phase AD system and for a conventional AD reactor. Further, a multidimensional LCA approach was taken, considering the five-midpoint impact categories of global warming, smog, human health particulate, acidification, and eutrophication estimated using the U.S. EPA Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts. Co-management of food waste and sewage sludge using the novel two-phase AD system was shown to maximize energy recovery and had a net global warming benefit while reducing other environmental impacts compared with the alternative management strategies. It had similar relative environmental advantages across all categories as conventional AD, with the advantage of a smaller physical footprint. However, both approaches featured net environmental burdens when the background electric grid intensity fell below 0.25 kg CO 2 -eq kWh -1 , as could be expected in a decarbonized electric future. Upgrading the biogas produced from AD to renewable natural gas can displace the use of fossil natural gas for other non-electricity energy requirements that are difficult to decarbonize and may extend the time period of significant environmental benefits of utilizing AD for organic waste management. Treatment of the nutrient-rich supernatant generated by the novel two-phase AD system could be an obstacle for utilities with stringent nutrient discharge limits. Future research and full-scale implementation are needed to demonstrate the benefits of the two-phase AD system predicted through this analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Tensor-based flow reconstruction from optimally located sensor measurements

Reconstructing high-resolution flow fields from sparse measurements is a major challenge in fluid dynamics. Existing methods often vectorize the flow by stacking different spatial directions on top of each other, hence confounding the information encoded in different dimensions. Here, we introduce a tensor-based sensor placement and flow reconstruction method which retains and exploits the inherent multidimensionality of the flow. We derive estimates for the flow reconstruction error, storage requirements and computational cost of our method. We show, with examples, that our tensor-based method is significantly more accurate than similar vectorized methods. Furthermore, the variance of the error is smaller when using our tensor-based method. While the computational cost of our method is comparable to similar vectorized methods, it reduces the storage cost by several orders of magnitude. The reduced storage cost becomes even more pronounced as the dimension of the flow increases. We demonstrate the efficacy of our method on three examples: a chaotic Kolmogorov flow, in situ and satellite measurements of the global sea surface temperature and three-dimensional unsteady simulated flow around a marine research vessel.

Mechanics↗
DOE OSTI2018

Optimisation of confinement in a fusion reactor using a nonlinear turbulence model

We report the confinement of heat in the core of a magnetic fusion reactor is optimised using a multidimensional optimisation algorithm. For the first time in such a study, the loss of heat due to turbulence is modelled at every stage using first-principles nonlinear simulations which accurately capture the turbulent cascade and large-scale zonal flows. The simulations utilise a novel approach, with gyrofluid treatment of the small-scale drift waves and gyrokinetic treatment of the large-scale zonal flows. A simple near-circular equilibrium with standard parameters is chosen as the initial condition. The figure of merit, fusion power per unit volume, is calculated, and then two control parameters, the elongation and triangularity of the outer flux surface, are varied, with the algorithm seeking to optimise the chosen figure of merit. A twofold increase in the plasma power per unit volume is achieved by moving to higher elongation and strongly negative triangularity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Stability of a tokamak plasma with diffuse toroidal rotation

The present work considers the stability of a high- $\beta$ , large aspect ratio, circular plasma with diffuse profiles for the safety factor and the angular toroidal frequency (López & Guazzotto, Phys. Plasmas , vol. 24, 032501). An application of the Frieman–Rotenberg formalism results in a system of scalar eigenmode equations whose coupling is retained at the plasma–vacuum transition but is disregarded across the plasma column, which is a standard practice. The solution technique consists of a multidimensional shooting method for the poloidal harmonics; robust initial guesses are constructed by solving the dispersion relation in the static scenario with vanishing magnetic shear. Flow shear appears as a high- $\beta$ toroidal contribution, and we illustrate its destabilizing influence on $n=1$ external kink modes in the presence of ideal and resistive walls. Internal resonances are avoided by means of the selection of appropriate equilibrium parameters. The stabilizing influence of a finite positive average magnetic shear is also exemplified.

Physics↗
DOE OSTI2017

Full Information Acquisition in Scanning Probe Microscopy

Scanning Probe Microscopy (SPM) has unlocked the nanoworld for exploration and control. While substantial effort has been dedicated towards the development of better instrumental platforms and probes, opportunities related to signal processing and data acquisition in SPMareoftenoverlooked. In this work, we discuss opportunities offered by capturing the full information of thedata stream from the detector, referred to as General Mode (G-Mode) SPM. This approach allows exploration of the complex tip-surface interactions, spatial mapping of multidimensional variability of material properties and their mutual interactions, and imaging at the information channel capacity limit, providing a new paradigm for SPM detection.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Aerosol Optical Tweezers Elucidate the Chemistry, Acidity, Phase Separations, and Morphology of Atmospheric Microdroplets

Aerosol particles represent unique chemical environments because of their high surface area-to-volume ratio that promotes the effects of interfacial chemistry in confined environments. Properties such as viscosity, diffusivity, water content, pH, and morphology—following liquid–liquid phase separation—can strongly alter how a particle interacts with condensable vapors and reactive trace gases, thus modifying its continual evolution and environmental effects. Our understanding of this chemical evolution of atmospheric particulate matter and its environmental impacts is largely limited by our ability to directly observe how these critical particle properties respond to the addition or reactive uptake of new chemical components. Aerosol optical tweezers (AOT) stably trap particles in focused laser beams, providing positional control and the retrieval of many of these critical properties required to understand and predict the chemistry of aerosolized microdroplets. The analytical power of the AOT stems from the retrieval of the cavity-enhanced Raman spectrum induced by the trapping laser. Analysis of the whispering gallery modes (WGMs) that resonate as a standing wave around the droplet’s interface, provide high accuracy measurements of the droplet’s size, refractive index (and thus a measurement of composition), and can distinguish between core–shell, partially engulfed, and homogeneous morphologies. In this study, we have advanced the ability to determine the properties of the core and shell phases in biphasic droplets, including obtaining high-accuracy pH measurements. These capabilities were applied to perform AOT physical chemistry experiments on authentic secondary organic aerosol (SOA) produced directly in the AOT chamber by ozonolysis of terpene vapors. The propensity of the SOA to phase separate as a shell from a wide range of nonpolar to polar core phases was observed, along with the discovery of a stable emulsified state of SOA particles in an aqueous salt droplet. Micron-thick SOA shells did not impede the gain or loss of water or squalane from the core to the surrounding air, indicating no significant diffusional limitations to condensational growth or partitioning even under dry conditions. These experiments formed the foundation of a new framework that predicts how the phase-separated morphology of complex aerosols containing organic carbon evolves during continual atmospheric oxidation processes. Increases in oxidation state will quickly drive conversion from a partially engulfed to core–shell morphology that has dramatically different chemical reactivity since the core phase is completely concealed by the shell. The recent advances in the experimental capabilities of the AOT technique such as presented here enable novel experimental methodologies that provide insights into the chemistry and multidimensional properties of aerosol microdroplets, and how these coevolve and respond to continual chemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Interfaces in Heterogeneous Catalysts: Advancing Mechanistic Understanding through Atomic-Scale Measurements

Developing novel catalysts with high efficiency and selectivity is critical for enabling future clean energy conversion technologies. Interfaces in catalyst systems have long been considered the most critical factor in controlling catalytic reaction mechanisms. Interfaces include not only the catalyst surface but also interfaces within catalyst particles and those formed by constructing heterogeneous catalysts. The atomic and electronic structures of catalytic surfaces govern the kinetics of binding and release of reactant molecules from surface atoms. Interfaces within catalysts are introduced to enhance the intrinsic activity and stability of the catalyst by tuning the surface atomic and chemical structures. Examples include interfaces between the core and shell, twin or domain boundaries, or phase boundaries within single catalyst particles. In supported catalyst nanoparticles (NPs), the interface between the metallic NP and support serves as a critical tuning factor for enhancing catalytic activity. Surface electronic structure can be indirectly tuned and catalytically active sites can be increased through the use of supporting oxides. Additionally, tuning interfaces in catalyst systems has been identified as an important strategy in the design of novel catalysts. However, the governing principle of how interfaces contribute to catalyst behavior, especially in terms of interactions with intermediates and their stability during electrochemical operation, are largely unknown. This is mainly due to the evolving nature of such interfaces. Small changes in the structural and chemical configuration of these interfaces may result in altering the catalytic performance. These interfacial arrangements evolve continuously during synthesis, processing, use, and even static operation. A technique that can probe the local atomic and electronic interfacial structures with high precision while monitoring the dynamic interfacial behavior in situ is essential for elucidating the role of interfaces and providing deeper insight for fine-tuning and optimizing catalyst properties.Scanning transmission electron microscopy (STEM) has long been a primary characterization technique used for studying nanomaterials because of its exceptional imaging resolution and simultaneous chemical analysis. Over the past decade, advances in STEM, that is, the commercialization of both aberration correctors and monochromators, have significantly improved the spatial and energy resolution. Imaging atomic structures with subangstrom resolution and identifying chemical species with single-atom sensitivity are now routine for STEM. These advancements have greatly benefitted catalytic research. For example, the roles of lattice strain and surface elemental distribution and their effect on catalytic stability and reactivity have been well documented in bimetallic catalysts. In addition, three-dimensional atomic structures revealed by STEM tomography have been integrated in theoretical modeling for predictive catalyst NP design. Recent developments in stable electronic and mechanical devices have opened opportunities to monitor the evolution of catalysts in operando under synthesis and reaction conditions; high-speed direct electron detectors have achieved sub-millisecond time resolutions and allow for rapid structural and chemical changes to be captured. Investigations of catalysts using these latest microscopy techniques have provided new insights into atomic-level catalytic mechanisms. Further integration of new microscopy methods is expected to provide multidimensional descriptions of interfaces under relevant synthesis and reaction conditions.In this Account, we discuss recent insights on understanding catalyst activity, selectivity, and stability using advanced STEM techniques, with an emphasis on how critical interfaces dictate the performance of precious metal-based heterogeneous catalysts. The role of extended interfacial structures, including those between core and shell, between separate phases and twinned grains, between the catalyst surface and gas, and between metal and support are discussed. We also provide an outlook on how emerging electron microscopy techniques, such as vibrational spectroscopy and electron ptychography, will impact future catalysis research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Development of an Ion Mobility Spectrometry-Orbitrap Mass Spectrometer Platform

Complex samples benefit from multidimensional measurements where higher resolution enables more complete characterization of biological and environmental systems. To address this challenge, we developed a drift tube-based ion mobility spectrometry-Orbitrap mass spectrometer (IMS-Orbitrap MS) platform. To circumvent the time scale disparity between the fast IMS separation and the much slower Orbitrap MS acquisition, we utilized a dual gate and pseudorandom sequences to multiplexed injection of ions and allowing operation in signal averaging (SA), single multiplexing (SM) and double multiplexing (DM) IMS modes to optimize the signal-to-noise ratio of the measurements. For the SM measurements, a previously developed algorithm was used to reconstruct the IMS data. A new algorithm was developed for the DM analyses involving a two-step process that first recovers the SM data and then decodes the SM data. The algorithm also performs multiple refining procedures in order to minimize demultiplexing artifacts. The new IMS-Orbitrap MS platform was demonstrated by the analysis of proteomic and petroleum samples, where the integration of IMS and high mass resolution proved essential for accurate assignment of molecular formulae.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Deep-Dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues

Mass spectrometry-based targeted proteomics (e.g., selected reaction monitoring, SRM) is emerging as an attractive alternative to immunoassays for protein quantification. Recently we have made significant progress in SRM sensitivity for enabling quantification of low ng/mL to sub-ng/mL level proteins in nondepleted human blood plasma/serum without affinity enrichment. However, precise quantification of extremely low abundant but biologically important proteins (e.g., ≤100 pg/mL in blood plasma/serum) using targeted proteomics approaches still remains challenging. To address this need, we have developed an antibody-independent Deep-Dive SRM (DD-SRM) approach that capitalizes on multidimensional high-resolution reversed-phase liquid chromatography (LC) separation for target peptide enrichment combined with precise selection of target peptide fractions of interest, significantly improving SRM sensitivity by ~5 orders of magnitude when compared to conventional LC-SRM. Application of DD-SRM to human serum and tissue has been demonstrated to enable precise quantification of endogenous proteins at ~10 pg/mL level in nondepleted serum and at <10 copies per cell level in tissue. Thus, DD-SRM holds great promise for precisely measuring extremely low abundance proteins or protein modifications, especially when high-quality antibody is not available.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2017

Online Ozonolysis Combined with Ion Mobility-Mass Spectrometry Provides a New Platform for Lipid Isomer Analyses

One of the most significant challenges in contemporary lipidomics lies in the separation and identification of lipid isomers that differ only in site(s) of unsaturation or geometric configuration of the carbon-carbon double bonds. While analytical separation techniques including ion mobility spectrometry (IMS) and liquid chromatography (LC) can separate isomeric lipids under appropriate conditions, conventional tandem mass spectrometry cannot provide unequivocal identification. To address this challenge, we have implemented ozone-induced dissociation (OzID) in-line with LC, IMS and high resolution mass spectrometry. Modification of an IMS- capable quadrupole time-of-flight mass spectrometer was undertaken to allow the introduction of ozone into the high-pressure trapping ion funnel region preceding the IMS cell. This enabled the novel LC-OzID-IMS-MS configuration where ozonolysis of ionized lipids occurred rapidly (10 ms) without prior mass-selection. LC-elution time alignment combined with accurate mass and arrival time extraction of ozonolysis products facilitated correlation of precursor and product ions without mass-selection (and associated reductions in duty cycle). Unsaturated lipids across 11 classes were examined using this workflow in both positive and negative ion modalities and in all cases the positions of carbon-carbon double bonds were unequivocally assigned based on predictable OzID transitions. Under these conditions geometric isomers exhibited different IMS arrival time distributions and distinct OzID product ion ratios providing a means for discrimination of cis/trans double bonds in complex lipids. The combination of OzID with multidimensional separations shows significant promise for facile profiling of unsaturation patterns within complex lipidomes.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Predictive Synthesis

Current solid state synthesis intrinsically involves a multidimensional space which is challenging to parametrize and predict. The diversity of extended structures comes from the diversity of basic properties of elements of the Periodic system which may exhibit a variety of bonding modes. The fundamental challenges of prediction of the preparative outcome are further complicated by practical synthetic issues. Current development in computational and experimental methods calls for collaborative efforts to make solid state synthesis more predictable. Overall, this Perspective discusses several steps in this direction, including integration of predictions of synthetic conditions with new structure predictions, widespread in situ studies to obtain a panoramic view of the reaction mechanism, and the creation of a synthetic database to properly document all synthetic efforts, including the unsuccessful ones.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine Learning Optimization of p-Type Transparent Conducting Films

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu–Zn–S thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm –1 ). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multidimensional parameter space such that optimization for a specific application is challenging and time-consuming. Here we show that strategic design of experiment combined with machine learning (ML) allows for the efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of four experimental CBD factors. The ML model is based on support vector regression employing a radial basis function as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. Here, the optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters.

14 SOLAR ENERGY↗
DOE OSTI2025

Constraining the Low-Temperature Oxidation Mechanism of n -Hexanol through the Detection and Identification of C 6 Elusive Intermediates

Alcohol-based fuels are currently considered to be viable energy carriers for the transportation sector. Consequently, a comprehensive mechanistic understanding of the low-temperature oxidation of alcohols is essential for application in advanced low-temperature compression engines. Here, in this work, a multidimensional approach involving experimental investigations, kinetic modeling, and theoretical calculations was used to provide new insights into the low-temperature oxidation mechanism of a C 6 alcohol, n -hexanol (CH 3 (CH 2 ) 5 OH), through the detection and identification of elusive C 6 intermediates. The oxidation of n-hexanol was investigated in a jet-stirred reactor under stoichiometric conditions (ϕ = 1.0), an initial fuel concentration of 2%, a residence time of 2 s, a temperature range between 500 and 660 K, and a pressure of 700 Torr. The reactants, intermediates, and final products were detected and identified by means of molecular-beam mass spectrometry coupled with single-photon ionization employing tunable synchrotron-generated vacuum ultraviolet radiation. Chemical kinetic simulations were performed using a previously published kinetic model (Togbé et al., Energy Fuels 2010, 11, 5859−5875) to predict the reactivity of n-hexanol and elucidate the predominant formation pathways of the observed low-temperature species. Experimental photoionization efficiency curves in conjunction with ab initio calculations, enabled the identification of important low-temperature species, such as C 6 unsaturated alcohols, C 6 olefinic hydroperoxides, C 6 cyclic ethers, C 6 diones, and C 6 ketohydroperoxides. The results of this study provide valuable insight into the mechanism of the low-temperature oxidation chemistry of n -hexanol, contributing to the development of kinetic models for the low-temperature oxidation of n-hexanol and other long-chain linear alcohols.

alcohols↗
DOE OSTI2020

Tuning the Morphology and Electronic Properties of Single-Crystal LiNi 0.5 Mn 1.5 O 4-δ : Exploring the Influence of LiCl–KCl Molten Salt Flux Composition and Synthesis Temperature

Single-crystal materials have played a unique role in the development of high-performance cathode materials for Li batteries due to their favorable chemomechanical stability. The molten salt synthesis method has become one of the most prominent techniques used to synthesize single-crystal layered and spinel materials. In this work, the molten salt synthesis method is used as a technique to tune both the morphology and Mn 3+ content of high-voltage LiNi 0.5 Mn 1.5 O 4 (LNMO) cathodes. The resulting materials are thoroughly characterized by a suite of analytical techniques, including synchrotron X-ray core-level spectroscopy, which are sensitive to the material properties on multiple length scales. Furthermore, the multidimensional characterization allows us to build a materials library according to the molten salt phase diagram as well as to establish the relationship among synthesis, material properties, and battery performance. The results of this work show that the Mn 3+ content is primarily dependent on the synthesis temperature and increases as the temperature is increased. Additionally, the particle morphology is mostly dependent on the composition of the molten salt flux, which can be tailored to obtain well-defined octahedrons enclosed by (111) facets, plates with predominant ($11\tilde{2}$) facets, irregularly shaped particles, or mixtures of these. The electrochemical measurements indicate that the Mn 3+ content has a larger contribution to the battery performance of LNMO than do morphological characteristics and that a significant amount of Mn 3+ could become detrimental to the battery performance. However, with similar Mn 3+ contents, morphology still plays a role in influencing the battery cycle life and rate performance. The insights of molten salt synthesis parameters on the formation of LNMO, with deconvolution of the roles of Mn 3+ and morphology, are crucial to continuing studies in the rational design of LNMO cathode materials for high-energy Li batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Assessment of Molten Eutectic LiF-NaF-KF Density through Experimental Determination and Semiempirical Modeling

Molten salts have favorable material properties for use in high-temperature energy systems, including thermal energy storage systems, concentrating solar power plants, nuclear reactors, and various industrial manufacturing processes. Knowledge of chemical and thermophysical property data is essential for the design and optimization of these systems, yet data are often limited or uncertain for many candidate salts due to the difficulty of thermophysical property measurements at relevant temperatures (e.g., 500–900 °C). Here, the density of molten LiF-NaF-KF eutectic is reassessed through review of previous experimental data, new density measurements from 470 to 800 °C, and semiempirical modeling. Additionally, the density was measured using the displacement technique. Compositional and temperature-dependent density estimates were calculated with a multidimensional Redlich–Kister model. The results of the new experimental measurements agree within 2% of the modeled density of molten eutectic LiF-NaF-KF. The Redlich–Kister model’s prediction shows a near-ideal density behavior for the LiF-NaF-KF system and is promising for the estimation of off-eutectic LiF-NaK-KF densities. Finally, through review of the existing literature and comparison to new measurements, recommendations are made for the density of LiF-NaF-KF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool

In this paper, we report the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package to process data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step, such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data stack, including DataShader and Param, to create an interactive visualization of mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS, and the visualization methods could be useful for other open-source mass spectrometry tools.

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

DONKEY: A Flexible and Accurate Algorithm for Clustering

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point is then assigned to one of the maxima by employing a maximization procedure. Finally, clusters artificially separated by minor fluctuations in the probability density are merged. The algorithm does not require parameter tuning, which ensures flexibility and reduces the risk of bias. It is tested on several synthetic data sets, where it consistently outperforms conventional clustering algorithms. As a final example, the algorithm is applied to the excited dynamics of the norbornadiene ⇌ quadricyclane (C 7 H 8 ) molecular photoswitch, demonstrating how distinct reaction pathways can be identified.

algorithms↗