Site-Specific Electronic Properties of [Ag[subscript 25](SR)[subscript 18]][superscript ;#8722;] Nan
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Ferredoxins (Fds) are small proteins which shuttle electrons to pathways like biological nitrogen fixation. Physical properties tune the reactivity of Fds with different pathways, but knowledge on how these properties can be manipulated to engineer new electron transfer pathways is lacking. Recently, we showed that an evolved strain of Rhodopseudomonas palustris uses a new electron transfer pathway for nitrogen fixation. This pathway involves a variant of the primary Fd of nitrogen fixation in R. palustris, Fer1, in which threonine at position 11 is substituted for isoleucine (Fer1 T11I ). To understand why this substitution in Fer1 enables more efficient electron transfer, we used in vivo and in vitro methods to characterize Fer1 and Fer1 T11I . Electrochemical characterization revealed both Fer1 and Fer1 T11I have similar redox transitions (–480 mV and – 550 mV), indicating the reduction potential was unaffected despite the proximity of T11 to an iron-sulfur (Fe—S) cluster of Fer1. Additionally, disruption of hydrogen bonding around an Fe—S cluster in Fer1 by substituting threonine with alanine (T11A) or valine (T11V) did not increase nitrogenase activity, indicating that disruption of hydrogen bonding does not explain the difference in activity observed for Fer1 T11I . Electron paramagnetic resonance spectroscopy studies revealed key differences in the electronic structure of Fer1 and Fer1 T11I , which indicate changes to the high spin states and/or spin-spin coupling between the Fe—S clusters of Fer1. Finally, our data implicates these electronic structure differences in facilitating electron flow and sets a foundation for further investigations to understand the connection between these properties and intermolecular electron transfer.
2D Janus Transition Metal Dichalcogenides (TMDs) have attracted much interest due to their exciting quantum properties arising from their unique two-faced structure, broken-mirror symmetry, and consequent colossal polarization field within the monolayer. While efforts are made to achieve high-quality Janus monolayers, the existing methods rely on highly energetic processes that introduce unwanted grain-boundary and point defects with still unexplored effects on the material's structural and excitonic properties Through high-resolution scanning transmission electron microscopy (HRSTEM), density functional theory (DFT), and optical spectroscopy measurements; this work introduces the most encountered and energetically stable point defects. It establishes their impact on the material's optical properties. HRSTEM studies show that the most energetically stable point defects are single (V S and V Se ) and double chalcogen vacancy (V S −V Se ), interstitial defects (Mi), and metal impurities (MW) and establish their structural characteristics. DFT further establishes their formation energies and related localized bands within the forbidden band. Cryogenic excitonic studies on h-BN-encapsulated Janus monolayers offer a clear correlation between these structural defects and observed emission features, which closely align with the results of the theory. Finally, the overall results introduce the defect genome of Janus TMDs as an essential guideline for assessing their structural quality and device properties.
Herein, we report the synthesis and characterization of two manganese tricarbonyl complexes, Mn I (HL)(CO) 3 Br (1 a-Br) and Mn I (MeL)(CO) 3 Br (1 b-Br) (where HL=2-(2’-pyridyl)benzimidazole; MeL=1-methyl-2-(2’-pyridy)benzimidazole) and assayed their electrocatalytic properties for CO 2 reduction. A redox-active pyridine benzimidazole ancillary ligand in complex 1 a-Br displayed unique hydrogen atom transfer ability to facilitate electrocatalytic CO 2 conversion at a markedly lower reduction potential than that observed for 1 b-Br. Notably, a one-electron reduction of 1 a-Br yields a structurally characterized H-bonded binuclear Mn(I) adduct (2 a’) rather than the typically observed Mn(0)-Mn(0) dimer, suggesting a novel method for CO 2 activation. Furthermore, combining advanced electrochemical, spectroscopic, and single crystal X-ray diffraction techniques, we demonstrate the use of an H-atom responsive ligand may reveal an alternative, low-energy pathway for CO 2 activation by an earth-abundant metal complex catalyst.
Zircaloy-4 cladding tubes were hydrogen-charged using a static pressure hydrogen charging method to experimentally produce a zirconium-hydride rim structure similar to that observed in Zircaloy-4 cladding tubes after operation in commercial light-water nuclear reactors. Detailed characterization was performed using electron energy loss spectroscopy (EELS) and four-dimensional scanning transmission electron microscopy (4D-STEM). By mapping the position of the plasmon peak in the low-loss EELS spectrum, it was determined that the zirconium hydrides formed were predominantly solid δ-phase zirconium hydrides. 4D-STEM was used to generate phase and strain maps at nanoscale. The interactions between two zirconium hydride platelets less than 200 nm apart leads to the localized lattice rotations and possible phase change, revealing a potential hydride growth mechanism.
Compressed multistate pair-density functional theory (CMS-PDFT) is a multistate version of multiconfiguration pair-density functional theory that can capture the correct topology of coupled potential energy surfaces (PESs) around conical intersections. Here, in this work, we develop interstate coupling vectors (ISCs) for CMS-PDFT in the OpenMolcas and PySCF/mrh electronic structure packages. Yet, the main focus of this work is using ISCs to calculate minimum-energy conical intersections (MECIs) by CMS-PDFT. This is performed using the projected constrained optimization method in OpenMolcas, which uses ISCs to restrain the iterations to the conical intersection seam. We optimize the S 1 /S 0 MECIs for ethylene, butadiene, and benzene and show that CMS-PDFT gives smooth PESs in the vicinities of the MECIs. Furthermore, the CMS-PDFT MECIs are in good agreement with the MECI calculated by the more expensive XMS-CASPT2 method.
Wide band gap AInSe 2 (A = K, Rb, Cs) is an important interlayer material for improving the efficiency of Cu(In,Ga)(S,Se) 2 (CIGS) solar cells. Compared to high-vacuum deposition and solid-state synthesis, a less energyintensive method is of interest for its fabrication. Herein, we present the rapid, low-temperature colloidal synthesis of AInSe 2 nanocrystals that opens a pathway for convenient solution processing. The crystal structures and electronic band structures of the nanocrystals were studied, and their particle morphology was found to be dependent on the choice of alkali metal and selenium precursors. Homogeneous solid solution (K,Rb,Cs)InSe 2 nanocrystals were synthesized using a mixture of alkali metal precursors. Their compositions, lattice parameters, and band gaps were easily tuned based on the K:Rb:Cs precursor ratio, providing potential for interface engineering of CIGS nanocrystal-based solar cells.
Dry buffer layer deposition techniques for chalcopyrite (CIGSSe)-based thin-film solar cells lack the surface-cleaning characteristics of the commonly used CdS or Zn(O,S) wet-chemical bath deposition. Here, a UV-induced ozone and/or a low-energy Ar + -ion treatment could provide dry CIGSSe surface cleaning steps. To study the impact of these treatments, the chemical surface structure of a CIGSSe absorber is investigated. For this purpose, a set of surface-sensitive spectroscopic methods, i.e., laboratory-based x-ray photoelectron spectroscopy and x-ray-excited Auger electron spectroscopy, is combined with synchrotron-based soft x-ray emission spectroscopy. After treatment times as short as 15 s, the UV-induced ozone treatment decreases the amount of carbon adsorbates at the CIGSSe surface significantly, while the oxygen content increases. This is accompanied by the oxidation of all absorber surface elements, i.e., indium, selenium, sulfur, and copper. Short (60 s) low-energy Ar + -ion treatments, in contrast, primarily remove oxygen from the surface. Longer treatment times also lead to a removal of carbon, while extremely long treatment times can also lead to additional (likely metallic) Cu phases at the absorber surface as well.
Two-dimensional electronic-vibrational spectroscopy (2DEVS) is an emerging spectroscopic technique which exploits two different frequency ranges for the excitation (visible) and detection (infrared) axes of a 2D spectrum. In contrast to degenerate 2D techniques, such as 2D electronic or 2D infrared spectroscopy, the spectral features of a 2DEV spectrum report cross correlations between fluctuating electronic and vibrational energy gaps rather than autocorrelations as in the degenerate spectroscopies. The center line slope of the spectral features reports on this cross correlation function directly and can reveal specific electronic-vibrational couplings and rapid changes in the electronic structure, for example. The involvement of the two types of transition moments, visible and infrared, makes 2DEVS very sensitive to electronic and vibronic mixing. 2DEV spectra also feature improved spectral resolution, making the method valuable for unraveling the highly congested spectra of molecular complexes. The unique features of 2DEVS are illustrated in this paper with specific examples and their origin described at an intuitive level with references to formal derivations provided. Although early in its development and far from fully explored, 2DEVS has already proven to be a valuable addition to the tool box of ultrafast nonlinear optical spectroscopy and is of promising potential in future efforts to explore the intricate connection between electronic and vibrational nuclear degrees of freedom in energy and charge transport applications.
A dedicated simulator, Poisson_CCD, has been constructed which models astronomical CCDs by solving Poisson’s equation numerically and simulating charge transport within the CCD. The potentials and free carrier densities within the CCD are self-consistently solved for, giving realistic results for the charge distribution within the CCD storage wells. The simulator has been used to model the CCDs that are being used to construct the LSST digital camera. The simulator output has been validated by comparing its predictions with several different types of CCD measurements, including astrometric shifts, brighter-fatter induced pixel–pixel covariances, saturation effects, and diffusion spreading. The code is open source and freely available.
Recent work such as Omnijet-$\alpha$ has demonstrated that effective tokenization combined with transformer-based architectures can produce effective foundation models for jet physics. While tokenization may help models capture generalizable event characteristics, it also introduces discretization errors that may compromise the precision required for downstream physics analyses. As the number and complexity of the particle features grow, these errors are likely to grow proportionally. In this study, we investigate new tokenization strategies to improve the application of generative transformer models to \textsc{Pythia8} simulations of electron-proton scattering at the Electron-Ion Collider. Specifically, we propose a feature-based structured tokenization approach that utilizes multiple tokens per particle, improving expressivity, while reducing the total number of unique tokens needed. We evaluate this method against grid-based binning, K-means clustering, and vector-quantized variational auto-encoders on the event simulations. Our results show that feature-based structured tokenization reduces discretization error, leading to more accurate generative modeling of particle-level events.
Copper-based chalcogenides have emerged as promising thermoelectric materials due to their high thermoelectric performance, tunable transport properties, earth abundance and low toxicity. We have presented an overview of experimental results and first-principal calculations investigating the thermoelectric properties of various polymorphs of Cu2SnS3 (CTS), Cu2ZnSnS4 (CZTS), and Cu2ZnSnSe4 (CZTSe) synthesized by high-energy reactive mechanical alloying (ball milling). Of particular interest are the disordered polymorphs of these materials, which exhibit phonon-glass–electron-crystal behavior—a decoupling of electron and phonon transport properties. The interplay of cationic disorder and nanostructuring leads to ultra-low thermal conductivities while enhancing electronic transport. These beneficial transport properties are the consequence of a plethora of features, including trap states, anharmonicity, rattling, and conductive surface states, both topologically trivial and non-trivial. Based on experimental results and computational methods, this report aims to elucidate the details of the electronic and lattice transport properties, thereby confirming that the higher thermoelectric (TE) performance of disordered polymorphs is essentially due to their complex crystallographic structures. In addition, we have presented synchrotron X-ray diffraction (SR-XRD) measurements and ab initio molecular dynamics (AIMD) simulations of the root-mean-square displacement (RMSD) in these materials, confirming anharmonicity and bond inhomogeneity for disordered polymorphs.
Calculations are presented for the thermodynamic functions and phase-separation boundaries of solid metallic hydrogen-helium alloys at temperatures between zero and 19,000 K and at pressures between 15 and 90 Mbar. Expressions for the band-structure energy of a randomly disordered alloy (including third order in the electron-ion interaction) are derived and evaluated. Short- and long-range orders are included by the quasi-chemical method, and lattice dynamics in the virtual-crystal harmonic approximation. It is concluded that at temperatures below 4000 K, there is essentially complete phase separation of hydrogen-helium alloys and that a miscibility gap remains at the highest temperatures and pressures considered. The relevance of these results to models of the deep interior of Jupiter is briefly discussed.
After a classification and analysis of electronic and optoelectronic switching devices, the design principles and structure of a matrix optical switch is described. The switching and pair-exclusion operations in this type of switch are examined, and a method for the optical switching of communication channels is elaborated. Finally, attention is given to the structural organization of a parallel computer system with a matrix optical switch.
The pure rotational spectrum of the magnesium monoacetylide radical, MgCCH, has been recorded in the laboratory using millimeter/sub-mm direct absorption spectroscopy. These measurements constitute the first time this molecule has been observed in the laboratory by any spectroscopic method. Seventeen rotational transitions were observed in the frequency range 210-370 GHz for MgCCH, which appears to be a linear molecule with a (sup 2)Sigma ground electronic state. Rotational and fine structure constants were determined for this radical from a nonlinear least squares fit to the data. The rotational rest frequencies measured here will enable astronomical observations to be carried out for MgCCH towards IRC +10216, where the magnesium compounds MgCN and MgNC, as well as many acetylide species, are present.
A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation table in the presence of various sources of laboratory noise is shown. The output of the neural network is called the degradable classification index. The curve was generated by a simultaneous comparison of means, and it shows a peak-to-peak sensitivity of about 100 nm. The following graph uses model generated data from a compressor blade to show that much higher sensitivities are possible when the environment can be controlled better. The peak-to-peak sensitivity here is about 20 nm. The training procedure was modified for the second graph, and the data were subjected to an intensity-dependent transformation called folding. All the measurements for this approach to calibration were optical. The peak-to-peak amplitudes of the vibration modes were measured using heterodyne interferometry, and the modes themselves were recorded using television (electronic) holography.
Real-time digital beamforming, combined with lightweight, large aperture reflectors, enable a new architecture, which is the baseline for the proposed DESDynI [Deformation, Ecosystem Structure, and Dynamics of Ice] SAR [Synthetic Aperture Radar] Instrument (or DSI). This new instrument concept requires new methods for calibrating multiple simultaneous channels. The calibration of current state-of-the-art Electronically Steered Arrays typically involves pre-flight TR (Transmit/Receive) module characterization over temperature, and in-flight correction based on measured temperatures. This method ignores the effects of element aging and any drifts unrelated to temperature. We are developing new digital calibration of digital beamforming arrays, which helps to reduce development time, risk and cost. Precision calibrated TR modules enable real-time beamforming architectures by accurately tracking modules' characteristics through closed-loop digital calibration, which tracks systematic changes regardless of temperature. The benefit of this effort is that it would enable a new, lightweight radar architecture, with on-board digital beamforming. This provides significantly larger swath coverage than conventional SAR architectures
Recent work such as Omnijet-$\alpha$ has demonstrated that effective tokenization combined with transformer-based architectures can produce effective foundation models for jet physics. While tokenization may help models capture generalizable event characteristics, it also introduces discretization errors that may compromise the precision required for downstream physics analyses. As the number and complexity of the particle features grow, these errors are likely to grow proportionally. In this study, we investigate new tokenization strategies to improve the application of generative transformer models to \textsc{Pythia8} simulations of electron-proton scattering at the Electron-Ion Collider. Specifically, we propose a feature-based structured tokenization approach that utilizes multiple tokens per particle, improving expressivity, while reducing the total number of unique tokens needed. We evaluate this method against grid-based binning, K-means clustering, and vector-quantized variational auto-encoders on the event simulations. Our results show that feature-based structured tokenization reduces discretization error, leading to more accurate generative modeling of particle-level events.