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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physical layer authentication of electronic communication networks

A network authentication system can be configured for sampling a plurality of signal samples from a device on a network, providing the plurality of signal samples to a first machine-learned model that is configured to determine a device fingerprint based at least in part on the plurality of signal samples, and providing the device fingerprint to a second machine-learned model that is configured to classify the device based at least in part on the device fingerprint.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bloom filters for molecules

Abstract Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions of molecules in just a few GB of memory and check membership in sub milliseconds. We found string representations can have a false positive rate below 1% and require significantly less storage than using fingerprints. Canonical SMILES with Bloom filters with the simple FNV (Fowler-Noll-Voll) hashing function provide fast and accurate membership tests with small memory requirements. We provide a general implementation and specific filters for detecting if a molecule is purchasable, patented, or a natural product according to existing databases at https://github.com/whitead/molbloom .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Estimating Fault Friction From Seismic Signals in the Laboratory

Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing but in general cannot be directly measured. We show that fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. Further analysis of this fingerprint leads to a simple equation of state quantitatively relating the seismic signal power and the friction on the fault. Finally, these results show that fault zone frictional characteristics and the state of stress in the surroundings of the fault can be inferred from seismic waves, at least in the laboratory.

58 GEOSCIENCES↗
DOE OSTI2019

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed. Finally, the proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes.

42 ENGINEERING↗
DOE OSTI2019

Ultra-High Resolution Elemental/Isotopic Mass Spectrometry (m/?m> 1,000,000): Coupling of the Liquid Sampling-Atmospheric Pressure Glow Discharge with an Orbitrap Mass Spectrometer for Applications in Biological Chemistry and Environmental Analysis

Many fundamental questions of astrophysics, biochemistry, and geology rely on the ability to accurately and precisely measure the mass and abundance of isotopes. Taken a step further, the capacity to perform such measurements on intact molecules provides insights into processes in diverse biological systems. Described here is the coupling of a combined atomic and molecular (CAM) ionization source, the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma, with a commercially available ThermoScientific Fusion Lumos mass spectrometer. Demonstrated for the first time is the ionization and isotopically-resolved fingerprinting of a long-postulated, but never mass-spectrometrically observed, bi-metallic complex Hg:Se-cysteine. Such a complex has been implicated as having a role in observations of Hg detoxification by selenoproteins/amino acids. Demonstrated as well is the ability to mass spectrometrically-resolve the geochronologically important isobaric 87Sr and 87Rb species (?m ~ 0.3 mDa, mass resolution m/?m ? 1,700,000). The mass difference in this case reflects the beta-decay of the 87Rb to the stable Sr isotope. These two demonstrations highlight what may be a significant change in bioinorganic and atomic mass spectrometry, with impact expected across a broad spectrum of the physical, biological and geological sciences.

Hoegg, Edward D.↗
DOE OSTI2013

Acoustical sensing of cardiomyocyte cluster beating

Highlights: •An example of the application of QCM-D to live cell studies. •Detection of human pluripotent stem cell-derived cardiomyocyte cluster beating. •Clusters were studied in a thin liquid film and in a large liquid volume. •The QCM-D beating profile provides an individual fingerprint of the hPS-CMCs. -- Abstract: Spontaneously beating human pluripotent stem cell-derived cardiomyocytes clusters (CMCs) represent an excellent in vitro tool for studies of human cardiomyocyte function and for pharmacological cardiac safety assessment. Such testing typically requires highly trained operators, precision plating, or large cell quantities, and there is a demand for real-time, label-free monitoring of small cell quantities, especially rare cells and tissue-like structures. Array formats based on sensing of electrical or optical properties of cells are being developed and in use by the pharmaceutical industry. A potential alternative to these techniques is represented by the quartz crystal microbalance with dissipation monitoring (QCM-D) technique, which is an acoustic surface sensitive technique that measures changes in mass and viscoelastic properties close to the sensor surface (from nm to μm). There is an increasing number of studies where QCM-D has successfully been applied to monitor properties of cells and cellular processes. In the present study, we show that spontaneous beating of CMCs on QCM-D sensors can be clearly detected, both in the frequency and the dissipation signals. Beating rates in the range of 66–168 bpm for CMCs were detected and confirmed by simultaneous light microscopy. The QCM-D beating profile was found to provide individual fingerprints of the hPS-CMCs. The presented results point towards acoustical assays for evaluation cardiotoxicity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Utilizing rare earth elements as tracers in high TDS reservoir brines in CCS applications

In this paper we report the result of research associated with the testing of a procedures necessary for utilizing natural occurring trace elements, specifically the Rare Earth Elements (REE) as geochemical tracers in Carbon Capture and Storage (CCS) applications. Trace elements, particularly REE may be well suited to serve as in situ tracers for monitoring geochemical conditions and the migration of CO₂-charged waters within CCS storage systems. We have been conducting studies to determine the efficacy of using REE as a tracer and characterization tool in the laboratory, at a CCS analogue site in Soda Springs, Idaho, and at a proposed CCS reservoir at the Rock Springs Uplift, Wyoming. Results from field and laboratory studies have been encouraging and show that REE may be an effective tracer in CCS systems and overlying aquifers. In recent years, a series of studies using REE as a natural groundwater tracer have been conducted successfully at various locations around the globe. Additionally, REE and other trace elements have been successfully used as in situ tracers to describe the evolution of deep sedimentary Basins. Our goal has been to establish naturally occurring REE as a useful monitoring measuring and verification (MMV) tool in CCS research because formation brine chemistry will be particularly sensitive to changes in local equilibrium caused by the addition of large volumes of CO₂. Because brine within CCS target formations will have been in chemical equilibrium with the host rocks for millions of years, the addition of large volumes of CO₂ will cause reactions in the formation that will drive changes to the brine chemistry due to the pH change caused by the formation of carbonic acid. This CO₂ driven change in formation fluid chemistry will have a major impact on water rock reaction equilibrium in the formation, which will impart a change in the REE fingerprint of the brine that can measured and be used to monitor in situ reservoir conditions. Our research has shown that the REE signature imparted to the formation fluid by the introduction of CO₂ to the formation, can be measured and tracked as part of an MMV program. Additionally, this REE fingerprint may serve as an ideal tracer for fluid migration, both within the CCS target formation, and should formation fluids migrate into overlying aquifers. However application of REE and other trace elements to CCS system is complicated by the high salt content of the brines contained within the target formations. In the United States by regulation, in order for a geologic reservoir to be considered suitable for carbon storage, it must contain formation brine with total dissolved solids (TDS) > 10,000 ppm, and in most cases formation brines have TDS well in excess of that threshold. The high salinity of these brines creates analytical problems for elemental analysis, including element interference with trace metals in Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) (i.e. element mass overlap due to oxide or plasma phenomenon). Additionally, instruments like the ICP-MS that are sensitive enough to measure trace elements down to the parts per trillion level are quickly oversaturated when water TDS exceeds much more than 1,000 ppm. Normally this problem is dealt with through dilution of the sample, bringing the water chemistry into the instruments working range. However, dilution is not an option when analyzing these formation brines for trace metals, because trace elements, specifically the REE, which occur in aqueous solutions at the parts per trillion levels. Any dilution of the sample would make REE detection impossible. Therefore, the ability to use trace metals as in situ natural tracers in high TDS brines environments requires the development of methods for pre-concentrating trace elements, while reducing the salinity and associated elemental interference such that the brines can be routinely analyzed by standard ICP-MS methods. As part of the Big Sky Carbon Sequestration Project the INL-CAES has developed a rapid, easy to use process that pre-concentrates trace metals, including REE, up to 100x while eliminating interfering ions (e.g. Ba, Cl). The process is straightforward, inexpensive, and requires little infrastructure, using only a single chromatography column with inexpensive, reusable, commercially available resins and wash chemicals. The procedure has been tested with synthetic brines (215,000 ppm or less TDS) and field water samples (up to 5,000 ppm TDS). Testing has produced data of high quality with REE capture efficiency exceeding 95%, while reducing interfering elements by > 99%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Evidence of irreversible CO2 intercalation in montmorillonite

Mitigation of the global climate change via sequestration of anthropogenic carbon dioxide (CO2) in geologic formations requires assessment of the reservoir storage capacity and cap rock seal integrity. The typical cap rock is shale or mudstone rich in clay minerals that may significantly affect the effectiveness of the CO2 trapping. Specific objectives of this study were to conduct experimental investigation into the processes associated with CO2 and H2O trapped in swelling clay, namely, Wyoming and Texas montmorillonite powder. Combined (same-sample) multi-technique data ? manometric sorption isotherm hysteresis, diffuse reflectance infrared spectroscopy ?trapped CO2? fingerprints, irreversible X-ray diffraction patterns for the clay interlayer in intermediate hydration state, and HF acid digestion resulting in formation of non-extractable F:CO2 adducts ? corroborate a hypothesis that carbon dioxide molecules can be irreversibly trapped via anomalous extreme confinement in the galleries associated with montmorillonite interlayer, which may result in formation of carbonates in the longer term. Validation on Arizona montmorillonite lumps substantiated the evidence that such processes may occur in natural clay deposits but possibly on a different scale and at a different rate.

Romanov, V↗
DOE OSTI2023

Tangent functional connectomes uncover more unique phenotypic traits

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the “fingerprint gradient” (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond

The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here, we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. As a result, by scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Identification and characterization of naturally occurring inhibitors against UDP-glucuronosyltransferase 1A1 in Fructus Psoraleae (Bu-gu-zhi)

As an edible traditional Chinese herb, Fructus psoraleae (FP) has been widely used in Asia for the treatment of vitiligo, bone fracture and osteoporosis. Several cases on markedly elevated bilirubin and acute liver injury following administration of FP and its related proprietary medicine have been reported, but the mechanism in FP-associated toxicity has not been well investigated yet. This study aimed to investigate the inhibitory effects of FP extract and its major constituents against human UDP-glucuronosyltransferase 1A1 (UGT1A1), the key enzyme responsible for metabolic elimination of bilirubin. To this end, N-(3-carboxy propyl)-4-hydroxy-1,8-naphthalimide (NCHN), a newly developed specific fluorescent probe for UGT1A1, was used to evaluate the inhibitory effects of FP extract or its fractions in human liver microsomes (HLM), while LC-UV fingerprint and UGT1A1 inhibition profile were combined to identity and characterize the naturally occurring inhibitors of UGT1A1 in FP. Our results demonstrated that both the extract of FP and five major components of FP displayed evident inhibitory effects on UGT1A1 in HLM. Among these five identified naturally occurring inhibitors, bavachin and corylifol A were found to be strong inhibitors of UGT1A1 with the inhibition kinetic parameters (K{sub i}) values lower than 1 μM, while neobavaisoflavone, isobavachalcone, and bavachinin displayed moderate inhibitory effects against UGT1A1 in HLM, with the K{sub i} values ranging from 1.61 to 9.86 μM. These findings suggested that FP contains natural compounds with potent inhibitory effects against human UGT1A1, which may be one of the important reasons for triggering FP-associated toxicity, including elevated bilirubin levels and liver injury. - Graphical abstract: LC-UV fingerprint and UGT1A1 inhibition profiles were combined to identity and characterize the natural inhibitors of UGT1A1 in F. psoraleae for the first time. Five major components in F. psoraleae were identified as strong inhibitors against UGT1A1 in human liver microsomes, with the K{sub i} values ranging from 1.18–9.86 μM. - Highlights: • A method for rapid identification of UGT1A1 inhibitors from herbals was developed. • Fructus psoraleae (FP) extract displayed strong inhibitory effects on UGT1A1. • Five UGT1A1 inhibitory constituents in FP were identified for the first time. • The inhibition mechanism and the inhibition constant were well-characterized.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Determining the Oxygen Stoichiometry of Cobaltite Thin Films

Transition metal oxides (TMO) are promising materials to realize low-power neuromorphic devices. Their physical properties critically depend on their oxygen vacancy concentrations, whose experimental determination remains a challenging task. Here we focus on cobaltites, in particular La 1-x Sr x CoO 3-d (LSCO), and we present a strategy to identify fingerprints of oxygen vacancies in X-ray absorption (XA) spectra. Using a combination of experiment and theory, we show that the variation of the oxygen vacancy concentration in the perovskite phase of LSCO is correlated with the change of the relative peak positions of the O K-edge XA spectra. Furthermore, we also identify an additional geometrical fingerprint that captures both the changes of the Co-O bond length and Co-O-Co bond angle in the material due to the presence of oxygen vacancies. Finally, we predict the oxygen vacancy concentration of experimental samples and show how the resistivity of the oxide material may be tuned as a function of the defect concentration present in the system.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers

Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties—are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. Here, the burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature T g as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure–property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured T g values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified T g . While the present contribution is focused specifically on T g , the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

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

Soft X-ray Emission Spectroscopy of Phosphorus Compounds for Energy Conversion and Storage

Phosphorus (P) is a ubiquitous component of materials for energy conversion and storage. Despite the effectiveness of soft X-ray emission spectroscopy (XES) to characterize these complex materials, XES studies of P-containing compounds are still largely missing. In this investigation, the electronic structure and XES signatures from the P L 2,3 -edge are probed for nine solid-state P-containing compounds characterized by various oxidation states and chemical environments: GaP (3−) , InP (3−) , red-P (0) , H 3 P (3+) O 3 , Na 2 H 2 P 2 (4+) O 6 , H 3 P (5+) O 4 , KH 2 P (5+) O 4 , Na 2 HP (5+) O 4 , and InP (5+) O 4 . The XES spectrum of each material exhibits distinct spectral fingerprints, which provide a robust basis for the speciation of complex P-containing samples. Density functional theory calculations shed light on the electronic structure of selected materials and particularly on the effect of phosphorus-oxygen hybridization. This study provides a comprehensive understanding of P L 2,3 XES spectral fingerprints across a wide range of oxidation states and chemical environments. These data sets and insights pave the way for dedicated future work, such as in situ and operando investigations of P-containing compounds in energy-related applications, where speciation of P compounds is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Photoinduced Heterocyclic Ring Opening of Furfural: Distinct Open-Chain Product Identification by Ultrafast X-ray Transient Absorption Spectroscopy

The ultraviolet-induced photochemistry of five-membered heterocyclic rings often involves ring opening as a prominent excited-state relaxation pathway. The identification of this particular photoinduced mechanism, however, presents a challenge for many experimental methods. We show that femtosecond X-ray transient absorption spectroscopy at the carbon K-edge (~284 eV) provides core-to-valence spectral fingerprints that enable the unambiguous identification of ring-opened isomers of organic heterocycles. The unique differences in the electronic structure between a carbon atom bonded to the oxygen in the ring versus a carbon atom set free of the oxygen in the ring-opened product are readily apparent in the X-ray spectra. Ultrafast ring opening via C-O bond fission occurs within ~350 fs in 266-nm photoexcited furfural, as evidenced by fingerprint core (carbon 1s) electronic transitions into a nonbonding orbital of the open-chain carbene intermediate at 283.3 eV. The lack of recovery of the 1sπ* ground-state depletion in furfural at 286.4 eV indicates that internal conversion to the ground state is a minor channel. These experimental results, augmented by recent advances in the generation of isolated attosecond pulses at the carbon K-edge, will pave the way for probing ring-opened conical intersection dynamics in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗