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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

OAO 2 observations of the Alpha Persei cluster

UV photometry of the Alpha Persei cluster obtained with the OAO 2 satellite is analyzed. The data are used together with ground-based coude spectra to examine 20 stars ranging in spectral type from B3 to A3 for rotation effects as predicted by the models of Collins (1974) and of Collins and Sonneborn (1977). Values of v sin i are estimated on the recalibrated system of Slettebak et al. (1975), and an H-R diagram is plotted for the program stars. The distribution of the stars on the diagram is compared with the ZAMS for a chemical composition of X = 0.7 and Y = 0.02 as well as with two isochrones for logarithmic nuclear ages (in years) of 7.5 and 7.7. It is found that there is a seemingly narrow band of early-type stars lying a magnitude or so above the ZAMS on the plot, while the later types form a looser collection straddling the ZAMS. The data for Alpha Persei itself yield an upper limit of 7.4 to the logarithmic age. The analysis for rotation effects reveals one late-type star that does not conform to the model predictions and one 'weak helium' star. Evidence is discussed that these stars are not pole-on rapid rotators. It is concluded that there is a photometric effect for stars earlier than about B6 whereby these stars are displaced redward in color-magnitude diagrams.

Molnar, M. R.↗
DOE OSTI2024

Systematic exploration of the thermochemistry for a set of peroxy hydroperoxy-alkyl radicals

Here, the thermochemistry of peroxy hydroperoxy-alkyl ($\overline{O}$OQOOH) radicals has a significant influence on the reactivity of fuels and on the formation of highly oxygenated molecules (HOMs) in the atmosphere. Theoretical characterization of these radicals can be arduous due to their molecular size and complex fundamental interactions, such as hydrogen-bonding and torsional anharmonicity, and difficult to validate in the absence of any direct experimental thermochemical data. In this work, we systematically explore the thermochemistry of a set of $\overline{O}$OQOOH radicals with five increasingly affordable approaches with considerations for these interactions. In doing so, we present a novel conformer selection approach suited to predict properties at combustion temperatures. As a corollary, we also highlight the shortcomings in the standard choice of the ground conformer as the reference. The set of molecules is comprised of 149 C 2 -C 8 $\overline{O}$OQOOH radicals, selected to encompass a wide variety of branching and substitution patterns. Comparisons amongst the approaches help quantify the errors arising from various simplifying assumptions. For the largest of these radicals, and with the most affordable approach, final predictions of Gibbs energies are assigned a 2 σ uncertainty of 4 kcal mol -1 in the negative temperature coefficient region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

GPU acceleration of rank-reduced coupled-cluster singles and doubles

Here, we have developed a graphical processing unit (GPU) accelerated implementation of our recently introduced rank-reduced coupled-cluster singles and doubles (RR-CCSD) method. RR-CCSD introduces a low-rank approximation of the doubles amplitudes. This is combined with a low-rank approximation of the electron repulsion integrals via Cholesky decomposition. The result of these two low-rank approximations is the replacement of the usual fourth-order CCSD tensors with products of second- and third-order tensors. In our implementation, only a single fourth-order tensor must be constructed as an intermediate during the solution of the amplitude equations. Owing in large part to the compression of the doubles amplitudes, the GPU-accelerated implementation shows excellent parallel efficiency (95% on eight GPUs). Our implementation can solve the RR-CCSD equations for up to 400 electrons and 1550 basis functions—roughly 50% larger than the largest canonical CCSD computations that have been performed on any hardware. In addition to increased scalability, the RR-CCSD computations are faster than the corresponding CCSD computations for all but the smallest molecules. We test the accuracy of RR-CCSD for a variety of chemical systems including up to 1000 basis functions and determine that accuracy to better than 0.1% error in the correlation energy can be achieved with roughly 95% compression of the ov space for the largest systems considered. We also demonstrate that conformational energies can be predicted to be within 0.1 kcal mol -1 with efficient compression applied to the wavefunction. Finally, we find that low-rank approximations of the CCSD doubles amplitudes used in the similarity transformation of the Hamiltonian prior to a conventional equation-of-motion CCSD computation will not introduce significant errors (on the order of a few hundredths of an electronvolt) into the resulting excitation energies.

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

A modular and extensible CHARMM-compatible model for all-atom simulation of polypeptoids

Peptoids (N-substituted glycines) are a class of sequence-defined synthetic peptidomimetic polymers with applications including drug delivery, catalysis, and biomimicry. Classical molecular simulations have been used to predict and understand the conformational dynamics of single chains and their self-assembly into morphologies including sheets, tubes, spheres, and fibrils. The CGenFF-NTOID model based on the CHARMM General Force Field has demonstrated success in accurate all-atom molecular modeling of peptoid structure and thermodynamics. Extension of this force field to new peptoid side chains has historically required reparameterization of side chain bonded interactions against ab initio data. This fitting protocol improves the accuracy of the force field but is also burdensome and precludes modular extensibility of the model to arbitrary peptoid sequences. In this work, we develop and demonstrate a Modular Side Chain CGenFF-NTOID (MoSiC-CGenFF-NTOID) as an extension of CGenFF-NTOID employing a modular decomposition of the peptoid backbone and side chain parameterizations, wherein arbitrary side chains within the large family of substituted methyl groups (i.e., –CH 3 , –CH 2 R, –CHRR', and –CRR'R") are directly ported from CGenFF. We validate this approach against ab initio calculations and experimental data to develop a MoSiC-CGenFF-NTOID model for all 20 natural amino acid side chains along with 13 commonly used synthetic side chains and present an extensible paradigm to efficiently determine whether a novel side chain can be directly incorporated into the model or whether refitting of the CGenFF parameters is warranted. We make the model freely available to the community along with a tool to perform automated initial structure generation.

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

Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection

Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662–C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662–C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662–C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A high transmission tender X-ray monochromator employing a matched pair of multilayer grating and mirror

It is now well established that multilayer coated gratings may offer high diffraction efficiencies over the tender X-ray range, from about 1 keV to 5 keV, covering the gap between single layer coated grating monochromators and crystal monochromators. Nevertheless, few beamlines in the world are using such gratings in their monochromator. The successful implementation of a multilayer grating monochromator requires producing a matched pair of a multilayer grating and a multilayer mirror, and this matching is not straightforward as it must account for different incidence angles and refraction effects on each element. Here we review the realization of the multilayer grating monochromator of the SIRIUS beamline which has been successfully in service for several years. We show how, by alternating computer simulation with our diffraction code and measurements, we could optimize the monochromator transmission on a very wide energy range. After the grating was coated, it was found that the angle of optimal efficiency versus photon energy was significantly different from what was predicted by a simple conformal model of binary layers. Layer interdiffusion and profile smoothing during the deposition process must be added to the multilayer model to reproduce the measured data. The critical adjustment of the mirror multilayer period is achieved by the lateral translation of the mirror, which was given a small transverse period gradient. The monochromator is thus providing high transmission efficiency in the 1 to 5 keV energy range, more than 30% over 2.5 keV and up to 46% at 4.6 keV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1971

Absolute transition probabilities of phosphorus.

Use of a gas-driven shock tube to measure the absolute strengths of 21 P I lines and 126 P II lines (from 3300 to 6900 A). Accuracy for prominent, isolated neutral and ionic lines is estimated to be 28 to 40% and 18 to 30%, respectively. The data and the corresponding theoretical predictions are examined for conformity with the sum rules.-

Miller, M. H.↗
NASA NTRS1994

Carbon, nitrogen, magnesium, silicon, and titanium isotopic compositions of single interstellar silicon carbide grains from the Murchison carbonaceous chondrite

Seven hundred and twenty SiC grains from the Murchison CM2 chondrite, ranging in size from 1 to 10 micrometers, were analyzed by ion microprobe mass spectrometry for their C-isotopic compositions. Subsets of the grains were also analyzed for N (450 grains), Si (183 grains), Mg (179 grains), and Ti (28 grains) isotopes. These results are compared with previous measurements on 41 larger SiC grains (up to 15 x 26 micrometers) from a different sample of Murchison analyzed by Virag et al. (1992) and Ireland, Zinner, & Amari (1991a). All grains of the present study are isotopically anomalous with C-12/C-13 ratios ranging from 0.022 to 28.4 x solar, N-14/N-15 ratios from 0.046 to 30 x solar, Si-29/Si-28 from 0.54 to 1.20 x solar, Si-30/Si-28 from 0.42 to 1.14 x solar, Ti-49/Ti-48 from 0.96 to 1.95 x solar, and Ti-50/Ti-48 from 0.94 to 1.39 x solar. Many grains have large Mg-26 excesses from the decay of Al-26 with inferred Al-26/Al-27 ratios ranging up to 0.61, or 12,200 x the ratio of 5 x 10(exp -5) inferred for the early solar system. Several groups can be distinguished among the SiC grains. Most of the grains have C-13 and N-14 excesses, and their Si isotopic compositions (mostly excesses in Si-29 and Si-30) plot close to a slope 1.34 line on a Delta Si-29/Si-28 versus Delta Si-30/Si-28 three-isotope plot. Grains with small C-12/C-13 ratios (less than 10) tend to have smaller or no N-14 excesses and high Al-26/Al-27 ratios (up to 0.01). Grains with C-12/C-13 greater than 150 fall into two groups: grains X have N-15 excesses and Si-29 and Si-30 deficits and the highest (0.1 to 0.6) Al-26/Al-27 ratios; grains Y have N-14 excesses and plot on a slope 0.35 line on a Si three-isotope plot. In addition, large SiC grains of the Virag et al. (1992) study fall into three-distinct clusters according to their C-, Si-, and Ti-isotopic compositions. The isotopic diversity of the grains and the clustering of their isotopic compositions imply distinct and multiple stellar sources. The C- and N-isotopic compositions of most grains are consistent with H-burning in the CNO cycle. These and s-process Kr, Xe, Ba, and Nd suggest asymptotic giant branch (AGB) or Wolf-Rayet stars as likely sources for the grains, but existing models of nucleosynthesis in these stellar sites fail to account in detail for all the observed isotopic compositions. Special problems are posed by grains with C-12/C-13 less than 10 and almost normal and heavy N-isotopic compositions. Also the Si- and Ti-isotopic compositions, with excesses in Si-29 and Si-30 relative to Si-28 and excesses in all Ti isotopes relative to Ti-48, do not precisely conform with the compositions predicted for slow neutron capture. Additional theoretical efforts are needed to achieve an understanding of the isotopic composition of the SiC grains and their stellar sources.

Hoppe, Peter↗
NASA NTRS2001

Processing bottlenecks in dual-task performance: structural limitation or strategic postponement?

Recent evidence indicates that a central bottleneck causes much of the slowing that occurs when two tasks are performed at the same time. This bottleneck might reflect a structural limitation inherent in the cognitive architecture. Alternatively, the bottleneck might reflect strategic (i.e., voluntary) postponement, induced by instructions to emphasize one task over the other. To distinguish structural limitations from strategic postponement, we examine a new paradigm in which subjects are told to place equal emphasis on both tasks and to emit both responses at about the same time. An experiment using this paradigm demonstrated patterns of interference that cannot easily be attributed to strategic postponement, preparation effects, or conflicts in response production. The data conform closely to the predictions of structural central bottleneck models.

Attention↗
DOE OSTI2025

Equivariant Graph Attention Network - 3D Conformers & Feature Fusion

EGAN-3F (Equivariant Graph Attention Network - 3D Conformers & Feature Fusion) presents an innovative approach for predicting binding affinity between small molecules and protein targets, a fundamental task in drug discovery. Traditional structure-based methods often depend on protein-ligand complex structures obtained from crystallography or molecular docking. In contrast, ligand-only machine learning models using 1D or 2D representations such as SMILES have been developed to predict binding affinity without structural information about the target; however, their accuracy is often limited due to the lack of 3D ligand information. EGAN-3F addresses this limitation by integrating spatially aware graph learning with traditional descriptor-based features. We systematically investigate how combining 2D and 3D molecular representations enhances binding affinity prediction from SMILES strings. This approach underscores the importance of modeling conformational diversity and incorporating chemically meaningful descriptors to improve predictive accuracy. The key innovation of EGAN-3F lies in its ability to achieve robust ligand-based binding affinity predictions without requiring protein-ligand complex structures, effectively bridging the gap between purely structural and ligand-only modeling paradigms.

Shim, Heesung [Lawrence Livermore National Laborat↗
NASA NTRS1980

Nonbonded interactions in membrane active cyclic biopolymers. IV - Cation dependence

Interactions of valinomycin and form of its analogs in several conformations with the central ions Li(+), Na(+), K(+), Rb(+) and Cs(+) are investigated as part of a study of the specific preference of valinomycin for potassium and the mechanisms of carrier-mediated ion transport across membranes. Ion binding energies and conformational potential energies are calculated taking into account polarization energy formulas and repulsive energy between the central ion and the ligand atoms for conformations representing various stages in ion capture and release for each of the two ring chiralities of valinomycin and its analogs. Results allow the prediction of the chirality and conformation most likely to be observed for a given analog, and may be used to synthesize analogs with a desired rigidity or flexibility. The binding energies with the alkali metal cations are found to decrease with increasing ion size, and to be smaller than the corresponding ion hydration energies. It is pointed out that the observed potassium preference may be explainable in terms of differences between binding and hydration energies. Binding energies are also noted to depend on ligand conformation.

Radhakrishnan, R.↗
DOE OSTI2023

How Accurate Can Crystal Structure Predictions Be for High-Energy Molecular Crystals?

Molecular crystals have shallow potential energy landscapes, with multiple local minima separated by very small differences in total energy. Predicting molecular packing and molecular conformation in the crystal generally requires ab initio methods of high accuracy, especially when polymorphs are involved. We used dispersion-corrected density functional theory (DFT-D) to assess the capabilities of an evolutionary algorithm (EA) for the crystal structure prediction (CSP) of well-known but challenging high-energy molecular crystals (HMX, RDX, CL-20, and FOX-7). While providing the EA with the experimental conformation of the molecule quickly re-discovers the experimental packing, it is more realistic to start instead from a naïve, flat, or neutral initial conformation, which reflects the limited experimental knowledge we generally have in the computational design of molecular crystals. By doing so, and using fully flexible molecules in fully variable unit cells, we show that the experimental structures can be predicted in fewer than 20 generations. Nonetheless, one must be aware that some molecular crystals have naturally hindered evolutions, requiring as many attempts as there are space groups of interest to predict their structures, and some may require the accuracy of all-electron calculations to discriminate between closely ranked structures. To save resources in this computationally demanding process, we showed that a hybrid xTB/DFT-D approach could be considered in a subsequent study to push the limits of CSP beyond 200+ atoms and for cocrystals.

42 ENGINEERING↗
DOE OSTI2023

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Abstract Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l - and d -amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC 50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.

42 ENGINEERING↗
DOE OSTI2022

A separations and purification process for improving yields and meeting fuel contaminant specifications for high-octane gasoline produced from dimethyl-ether over a Cu/BEA catalyst

In this work, we have been developing a three-step conversion of biomass-derived syngas to methanol to dimethyl-ether (DME) to non-aromatic hydrocarbons for use as high-octane gasoline and sustainable aviation fuel. This process produces branched alkanes from DME using a Cu/BEA catalyst and is a promising alternative to other syngas conversion processes such as Fischer-Tropsch to linear alkanes and traditional ZSM-5 catalyzed methanol to aromatic gasoline. In this short article we describe some advances in our understanding related to separations and purification via the use of more detailed experimental speciation in an updated process model involving multiple phase equilibrium-based separation steps. Primary modeled reactor outlet constituents (and weight %) are: C3 and lighter hydrocarbon gases (11.1%), C4s (54.5%), H 2 (1.2%), CO 2 (2.9%), water (5.0%), unreacted DME (16.5%), methanol (2.3%), and C5+ hydrocarbons (6.4%). DME (the primary reactant) and H 2 recycle and reuse are important for the overall process efficiency, and the recycle of C4s is important to increase the C5+ yield via reactivation and homologation. Thus H 2 , C4s, and DME are targeted for recycle, while methanol and water need to be removed from the product to conform with fuel specifications. Model predictions from Aspen Plus using the NRTL-RK property method indicate a fuel composition with C5+ content of 97.1 wt%, with minor constituents: 2.4 wt% C4s, 0.3 wt% methanol, 0.1 wt% DME, 0.03 wt% water, and 0.01 wt% C3s. These ranges of minor components conform with fuel quality requirements, and the modeled product is amenable for unconstrained blending to boost gasoline octane ratings.

09 BIOMASS FUELS↗
DOE OSTI2023

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Elimination of QCD Renormalization Scale and Scheme Ambiguities

The setting of the renormalization scale (μ r ) in the perturbative QCD (pQCD) is one of the crucial problems for achieving precise fixed-order pQCD predictions. The conventional prescription is to take its value as the typical momentum transfer Q in a given process, and theoretical uncertainties are then evaluated by varying it over an arbitrary range. The conventional scale-setting procedure introduces arbitrary scheme-and-scale ambiguities in fixed-order pQCD predictions. The principle of maximum conformality (PMC) provides a systematic way to eliminate the renormalization scheme-and-scale ambiguities. The PMC method has rigorous theoretical foundations; it satisfies the renormalization group invariance (RGI) and all of the self-consistency conditions derived from the renormalization group. The PMC has now been successfully applied to many physical processes. In this paper, we summarize recent PMC applications, including event shape observables and heavy quark pair production near the threshold region in e + e – annihilation and top-quark decay at hadronic colliders. In addition, estimating the contributions related to the uncalculated higher-order terms is also summarized. These results show that the major theoretical uncertainties caused by different choices of μ r are eliminated, and the improved pQCD predictions are thus obtained, demonstrating the generality and applicability of the PMC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗