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Search for dark matter production in association with a single top quark in proton-proton collisions at $\sqrt{s} = 13$ TeV
A search for the production of a single top quark in association with invisible particles is performed using proton-proton collision data collected with the CMS detector at the LHC at $\sqrt{s}=13$ TeV, corresponding to an integrated luminosity of 138 fb −1 . In this search, a flavor-changing neutral current produces a single top quark or antiquark and an invisible state nonresonantly. The invisible state consists of a hypothetical spin-1 particle acting as a new mediator and decaying to two spin-1/2 dark matter candidates. The analysis searches for events in which the top quark or antiquark decays hadronically. No significant excess of events compatible with that signature is observed. Exclusion limits at 95% confidence level are placed on the masses of the spin-1 mediator and the dark matter candidates, and are compared to constraints from the dark matter relic density measurements. In a vector (axial-vector) coupling scenario, masses of the spin-1 mediator are excluded up to 1.85 (1.85) TeV with an expectation of 2.0 (2.0) TeV, whereas masses of the dark matter candidates are excluded up to 0.75 (0.55) TeV with an expectation of 0.85 (0.65) TeV.
Plasma decay in hydrocarbons and hydrocarbon- and H 2 O-containing mixtures excited by high-voltage nanosecond discharge at elevated gas temperatures
Plasma decay after a high-voltage nanosecond discharge was experimentally and numerically studied in pure hydrocarbons (C 2 H 6 and C 3 H 8 ), and H 2 O:N 2 and C 3 H 8 :O 2 mixtures for pressures in the range 2–4 Torr and gas temperatures from 300 to 600 K. In a stoichiometric C 3 H 8 :O 2 mixture, plasma decay was also studied in a repetitively pulsed discharge for varing numbers of discharge pulses (varying degrees of fuel oxidation). The rate of plasma decay was determined from the temporal evolution of electron density measured using the microwave interferometer. It was observed that gas heating to 600 K leads to a decrease in the rate of plasma decay in all cases. The effect of heating on plasma decay was most profound in the H 2 O:N 2 mixture (after a single discharge pulse) and in the C 3 H 8 :O 2 mixture for high degrees of fuel oxidation. A kinetic scheme was developed to numerically simulate the plasma decay in hydrocarbons and combustible mixtures. Numerical analysis showed that, under the conditions studied, plasma decay was controlled by dissociative electron recombination with simple molecular and cluster ions. Gas heating led to a decrease in the rate of the electron-ion recombination and the rate of conversion of molecular ions to cluster ions. As a result, the gas temperature increase caused a decrease in the fraction of cluster ions for which the recombination coefficients are an order of magnitude higher than the recombination coefficients for molecular ions. The influence of gas heating on the decrease of the amount of cluster ions was more important when the ion composition was dominated by hydrated H 3 O + (H 2 O) k ions. The rates of the formation of these ions are extremely sensitive to any variations in gas temperature. A result, in agreement with our observations, gas heating led to an anomalous decrease in the rates of plasma decay in the H 2 O:N 2 mixture, as well as in the C 3 H 8 :O 2 mixture when H 2 O molecules were produced due to fuel oxidation.
Green synthesis and characterization of nontoxic L-methionine capped silver and gold nanoparticles
The simple green method for synthesis of stable L-Methionine (L-Met) capped silver (Ag@LM NPs) and gold (Au@LM NPs) nanoparticles (NPs) without adding any additional reduction agent or stabilizer was developed. Colloidal dispersions were characterized by UV–Vis spectrophotometry. The size and spherical shape of NPs were evaluated by transmission electron microscopy. Their surface covering was confirmed by atomic force microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, and zeta potential measurements. Density functional theory calculations pointed that the preferential adsorption mode of L-Met on both Ag and Au surfaces was a vertical binding geometry via –NH2 group, while horizontal binding mode via [sbnd]S[sbnd] and –NH2 groups is also possible. The genotoxicity (evaluated by the micronucleus assay) of NPs, as well as their effects on some oxidative stress parameters (catalase activity, malondialdehyde level), were assessed in vitro using human peripheral blood cells as a model system. The influence of NPs on the morphology of lymphocyte cells studied using atomic force microscopy revealed that the membrane of cells remained unaffected after the treatment with NPs. When considering the effects of NPs on catalase activity and malondialdehyde level, neither particle type promoted oxidative stress. However, the treatment of lymphocytes with Ag@LM NPs induced a concentration-dependent enhancement of the micronuclei incidence and suppression of the cell proliferation while Au@LM NPs promoted cell proliferation, with no significant effects on micronuclei formation. The Ag@LM NPs were more prone to induce DNA damage than Au@LM NPs, which makes the latter type more suitable for further studies in nano-medicine. © 2019.
Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing
Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).
Control of solvent adsorption in porous liquids through solvent-solvent interactions
Type 3 Porous Liquids (PLs) are a new class of materials that offer transformative potential for gas capture and utilization. These PLs are comprised of a bulky solvent with a suspended empty nanoporous material that enables selective and high-capacity capture from dilute and complex gas steams. The relationship between the nanoporous material, the structure of the solvent molecules, and the time dependence of solvent adsorption into the nanoporous material governs long term adsorption properties. Herein, molecular dynamics (MD) calculations evaluated the solvent dynamics of nine neat solvents with a broad range of chemical identities and experimental density measurements probed the time-dependent adsorption of these solvents into the ZIF-8 metal-organic frameworks (MOFs) to form PL dispersions over three weeks. Two of the nine dispersion, using glyceryl triacetate and 2’-hydroxyacetopheneone as solvents, presented sufficiently low solvent infiltration—characterized by solvent adsorption less than 40% of the ZIF-8 pore volume—to be viable as PLs. The experimental solvent adsorption data for ZIF-8 dispersed in the four aromatic solvents (acetophenone, methylbenzoate, 2-isopropylphenol, and 2’-hydroxyacetophenone) was fit to a kinetic model to quantify rate of solvent adsorption into ZIF-8. The rates of adsorption of these four similar-sized solvents demonstrated that solvent adsorption is slowed and limited by solvent clustering dynamics that are, in turn, driven by intermolecular hydrogen bonding. Ultimately, experimental solvent sorption measurements indicated that glyceryl triacetate and 2’-hydroxyacetophenone with ZIF-8 formed PLs with the most stable porosity and the greatest potential for gas capture. In conclusion, the combined experimental-computational materials exploration framework used here reveals a novel relationship between molecular scale solvent dynamics and macroscale solvent adsorption kinetics critical for the discovery and rapid evaluation of Type 3 PL compositions.
Tuning gas separation performance of polyimide membranes with macrocyclic crown ether units
Membrane-based gas separation is an energy-efficient alternative to conventional thermally-driven separation processes. However, polymer membranes face the permeability-selectivity trade-off challenge, which stems from the broad size distribution of free volume voids. Here, this study reports a molecular design strategy to address this challenge through incorporating macrocyclic crown ether (CE) moieties into the backbone of Matrimid® polyimide, a commercial gas separation membrane. A series of CE-containing Matrimid®-like copolyimides were synthesized with systematically varied CE molar contents ranging from 3 to 20%. These copolyimides formed ductile, defect-free thin films suitable for membrane fabrication. Gas permeation tests revealed a non-monotonic relationship between permeability/selectivity and CE content. Notably, the copolyimide with only 5% CE demonstrated a 61% increase in CO 2 /CH 4 selectivity and a 13% increase in CO 2 permeability relative to pristine Matrimid®. Higher CE contents did not yield further performance improvements, which is likely due to the competing effects of chain packing disruption and π–π interactions among CE moieties at high content. This hypothesis was supported by wide-angle X-ray scattering (WAXS) analysis, density measurements, and fractional free volume calculations. These findings highlight the potential of macrocyclic crown ether incorporation strategies in fine tuning the microstructure of commercial polyimide gas separation membranes to surpass the traditional permeability-selectivity trade-off.
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks
Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.
Phase Stability and Electrochemical Performance of La-Site-Doped Li6La3Zr0.5Nb0.5Ta0.5Hf0.5O12 High-Entropy Garnets
We investigate La-site substitution in the high-entropy garnet Li6La3Zr0.5Nb0.5Ta0.5Hf0.5O12 (LLZNTH) using Ba2+, Sr2+, and Sm3+ to elucidate how dopant governs phase stability, Li-site distribution, and electrochemical behavior. X-ray diffraction shows that Sr2+ is incorporated homogeneously into the garnet lattice, whereas the larger Ba2+ and smaller Sm3+ ions partially exceed the structural tolerance, generating secondary phases. Nevertheless, the Sm-doped composition (x = 0.05) exhibits the highest room-temperature ionic conductivity (2.7 × 10–4 S cm–1). Neutron powder diffraction reveals that Sm substitution drives a redistribution of Li+ from the tetrahedral 24 d sites into the higher-mobility 96 h positions, enhancing the connectivity of the three-dimensional Li-ion migration network. A Sm-doping series (x = 0.01–0.05) further shows that only sufficiently high Sm levels induce this redistribution, whereas lower concentrations retain Li arrangements similar to the undoped garnet. Critical current density measurements demonstrate that La-site dopants also influence interfacial stability against Li metal, underscoring a trade-off between bulk transport enhancement and mechanical robustness. Collectively, these findings reveal that in high-entropy garnets improved ionic conductivity can originate not only from phase-pure structures but also from targeted modification of the Li sublattice, even when accompanied by secondary phases, offering a compositional design principle for garnet electrolytes.
Local Order in Liquid Gallium–Indium Alloys
Liquid metals such as eutectic Ga–In alloys have low melting points and low toxicity and are used in catalysis and micro-robotics. This study investigates the local atomic structure of liquid gallium-indium alloys by a combination of density measurements, diffraction data, and Monte-Carlo simulation via the empirical potential structure refinement approach. A high-Q shoulder observed in liquid Ga is related to structural rearrangements in the second coordination shell. Structure analysis found coordination environments close to a random distribution for eutectic Ga–In alloy, while electronic effects appear to dominate the mixing enthalpy.
Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential
A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.
Millimeter-Scale Exfoliation of hBN with Tunable Flake Thickness for Scalable Encapsulation
As a two-dimensional (2D) dielectric material, hexagonal boron nitride (hBN) is in high demand for applications in photonics, nonlinear optics, and nanoelectronics. Unfortunately, the high-throughput preparation of macroscopic-scale, high-quality hBN flakes with controlled thickness is an ongoing challenge, limiting device fabrication and technological integration. In this report we present a metal thin-film exfoliation method to prepare hBN flakes with millimeter-scale dimension, near-unity yields, and tunable flake thickness distribution from 1-7 layers, a substantial improvement over scotch tape exfoliation. The single crystallinity and high quality of the exfoliated hBN are demonstrated with optical microscopy, atomic force microscopy, Raman spectroscopy, and second harmonic generation. We further explore a possible mechanism for the effectiveness and selectivity based on thin-film residual stress measurements, density functional theory calculations, and transmission electron microscopy imaging of the deposited metal films. We find that the magnitude of the residual tensile stress induced by thin film deposition plays a key role in determining exfoliated flake thickness in a manner which closely resembles 3D semiconductor spalling. Lastly, we demonstrate that our exfoliated, large-area hBN flakes can be readily incorporated as encapsulating layers for other 2D monolayers. Altogether, this method brings us one step closer to the high throughput, mass production of hBN-based 2D photonic, optoelectronic, and quantum devices.
Characterizing Variability in Lignocellulosic Biomass: A Review
Feedstock variability is a significant barrier to the scale-up and commercialization of lignocellulosic biofuel technologies. Variability in feedstock characteristics and behavior creates numerous challenges to the biorefining industry by affecting continuous operation and biofuels yields. Recently, feedstock variability is understood and explained largely on the basis of chemical composition. Physical and mechanical properties and behavior of lignocellulosic feedstock in various unit operations, studied through advanced analytical methods, can further explain variability. Such studies will enable us in developing processes and designing equipment to improve operation and conversion performance. In this perspective, we review several advanced analytical methods that measure density, moisture content, thermal properties, flowability, grindability, rheology properties, and micromorphological characteristics. We also discuss the correlations and interactions among these properties that reflect the complexity of lignocellulosic biomass as a feedstock and the associated quality metrics and logistics of supplying consistent quality feedstock to a biorefinery. We further examine methods that have not traditionally been used to characterize lignocellulosic feedstocks but have the potential to bridge the gap in our explanation of feedstock variability.
A Nanocavitation Approach to Understanding Water Capture, Water Release, and Framework Physical Stability in Hierarchically Porous MOFs
Chemically stable metal–organic frameworks (MOFs) featuring interconnected hierarchical pores have proven to be promising for a remarkable variety of applications. Nevertheless, the framework’s susceptibility to capillary-force-induced pore collapse, especially during water evacuation, has often limited practical applications. Methodologies capable of predicting the relative magnitudes of these forces as functions of the pore size, chemical composition of the pore walls, and fluid loading would be valuable for resolution of the pore collapse problem. Here, we report that a molecular simulation approach centered on evacuation-induced nanocavitation within fluids occupying MOF pores can yield the desired physical-force information. Here, the computations can spatially pinpoint evacuation elements responsible for collapse and the chemical basis for mitigation of the collapse of modified pores. Experimental isotherms and difference-electron density measurements of the MOF NU-1000 and four chemical variants validate the computational approach and corroborate predictions regarding relative stability, anomalous sequence of pore-filling, and chemical basis for mitigation of destructive forces.
The Complicated Link Between Material Properties and Microfracture Density for an Underground Explosion in Granite
Geophysical techniques are often implemented as quick and inexpensive ways to locate and characterize fractures in the subsurface, which is important for a number of geoscience fields. Seismic velocities are the most widely used proxies for identification of fractures, but the correlation is not always well-defined. In this study we present material property data: unconfined compressive strength (UCS), bulk density (ρ), Young's modulus (E), Poisson's ratio (ν), P wave velocity (V p ), and S wave velocity (V s ), in conjunction with microfracture densities measured on samples of granite collected before and after underground chemical explosions. Results indicate the relationship between fractures and material properties is complex, even in this single-lithology environment. We interpret that this complexity arises from varying fracture mechanisms (e.g. dilation-inducing fractures vs compression-inducing fractures) in different parts of the core, due to differences in stress conditions. Additional complexity may result from chemical interactions between the fresh fractures and the fluids in the area. Water content appears to have a significant, if not dominant, role in the unconfined compressive strength (UCS) of the samples. We suggest caution when using elastic property measurements as a proxy for fracturing in areas of explosion-induced damage, or in other areas where a variety of mechanisms induce fracturing.
Is Cs 2 TiBr 6 a promising Pb-free perovskite for solar energy applications?
In a quest for Pb-free perovskites suitable for solar energy applications, Cs 2 TiBr 6 has recently been reported as a promising compound, with appropriate optical and electrical properties as well as high stability under environmental stresses. In this study, we pursue investigation on this compound, demonstrating phase pure Cs 2 TiBr 6 powder formation using solution synthesis and providing complementary experimental characterization and theoretical calculations. An experimental absorption onset of around 2.0 eV is extracted and a weak broad photoluminescence is measured. Density functional theory calculations predict an indirect bandgap, parity-forbidden for both the direct and indirect transitions, which explains the weak and Stokes shifted luminescence. Additionally, we highlight the strong instability of Cs 2 TiBr 6 powder in ambient atmosphere. Therefore, our experimental results supported by theoretical calculations differ from previous results and raise doubts on the suitability of Cs 2 TiBr 6 in its pristine form for solar energy applications.
Doppler Backscattering Data Analysis and Integrated Modeling with OMFIT
One Modeling Framework for Integrated Tasks (OMFIT) is a widely used software tool in the magnetic fusion research community. OMFIT provides magnetic fusion energy researchers with a framework for the development of special-purpose physics modules. This paper describes an OMFIT physics module pertaining to the Doppler Backscattering (DBS) fusion plasma diagnostic. DBS measures density fluctuations and flow velocity through plasma scattering of electromagnetic waves. The OMFIT DBS module was developed to analyze experimental DBS data and facilitate modeling of DBS systems installed on multiple tokamak devices. The OMFIT DBS module is designed to support several analysis workflows: detailed analysis of experimental data, experimental planning, and theory-based synthetic diagnostic modeling. The DBS module uses integrated modeling by leveraging other OMFIT physics modules to perform tasks related to DBS, e.g. ray/beam–tracing simulations, edge-localized mode–synchronized data analysis, magnetic equilibrium reconstruction, and fitting kinetic profile data. Furthermore, this paper describes several supported workflows and serves a reference for the OMFIT DBS module.
High pressure phase transition and strength estimate in polycrystalline alumina during laser-driven shock compression
Alumina (Al 2 O 3 ) is an important ceramic material notable for its compressive strength and hardness. It represents one of the major oxide components of the Earth's mantle. Static compression experiments have reported evidence for phase transformations from the trigonal α-corundum phase to the orthorhombic Rh 2 O 3 (II)-type structure at ~90 GPa, and then to the post-perovskite structure at ~130 GPa, but these phases have yet to be directly observed under shock compression. In this work, we describe laser-driven shock compression experiments on polycrystalline alumina conducted at the Matter in Extreme Conditions endstation of the Linac Coherent Light Source. Ultrafast x-ray pulses (50 fs, 10 12 photons/pulse) were used to probe the atomic-level response at different times during shock propagation and subsequent pressure release. At 107 ± 8 GPa on the Hugoniot, we observe diffraction peaks that match the orthorhombic Rh 2 O 3 (II) phase with a density of 5.16 ± 0.03 g cm –3 . Upon unloading, the material transforms back to the α-corundum structure. Upon release to ambient pressure, densities are lower than predicted assuming isentropic release, indicating additional lattice expansion due to plastic work heating. Furthermore, using temperature values calculated from density measurements, we provide an estimate of alumina's strength on release from shock compression.