Structures of the [sigma subscript 2] receptor enable docking for bioactive ligand discovery
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The complex, [{[Mn(bpy)(CO) 3 ] 2 }(μ-CN)] + (Mn 2 CN + ), has previously been shown to photochemically reduce CO 2 to CO. The detailed mechanism behind its reactivity was not elucidated. In this work, the photoevolution of this reaction is studied in acetonitrile (MeCN) using IR and UV-vis spectroscopy. Samples were excited into the Mn I → π* bpy metal-to-ligand charge transfer (MLCT) absorption band triggering CO loss, and rapid MeCN solvent ligation at the open coordination site. It is concluded that this process occurs selectively at the Mn axial ligation site that is trans to the C-end of the bridging cyanide. Upon further photolysis, the metal–metal bonded dimeric species, [(CO) 3 (bpy)Mn–Mn(bpy)(CO) 3 ] (Mn–Mn) is observed to form under anaerobic conditions. The presence of this dimeric species coincides with the observation of CO production. When oxygen is present, CO 2 photoreduction does not occur, which is attributed to the inability of Mn 2 CN + to convert to the metal–metal bonded dimer. Photolysis experiments, where the Mn–Mn dimer is formed photochemically under argon first and then exposed to CO 2 , reveal that it is the radical species, [Mn(bpy)(CO) 3 ˙] (Mn˙), that interacts with the CO 2 . Since the presence of Mn–Mn and light is required for CO production, [Mn(bpy)(CO) 3 ˙] is proposed to be a photochemical reagent for the transformation of CO 2 to CO.
The room temperature growth of two-dimensional van der Waals (2D-vdW) materials is indispensable for state-of-the-art nanotechnology. Low temperature growth supersedes the requirement of elevated growth temperatures accompanied with high thermal budgets. Moreover, for electronic applications, low or room temperature growth reduces the possibility of intrinsic film-substrate interfacial thermal diffusion related deterioration of the functional properties and the consequent deterioration of the device performance. Here, we demonstrated the growth of ultrawide-bandgap boron nitride (BN) at room temperature by using the pulsed laser deposition (PLD) process, which exhibited various functional properties for potential applications. Comprehensive chemical, spectroscopic and microscopic characterizations confirmed the growth of ordered nanosheet-like hexagonal BN (h-BN). Functionally, the nanosheets show hydrophobicity, high lubricity (low coefficient of friction), and a low refractive index within the visible to near-infrared wavelength range, and room temperature single-photon quantum emission. In conclusion, our work unveils an important step that brings a plethora of potential applications for these room temperature grown h-BN nanosheets as the synthesis can be feasible on any given substrate, thus creating a scenario for “h-BN on demand” under a frugal thermal budget.
We used temperature-dependent spark plasma sintering to induce phase transformations of metastable 3D c-BN to mixed-phase 3D/2D c-BN/h-BN and ultimately to the stable 2D h-BN phase at high temperature, useful for extreme-temperature technology.
Wide and ultrawide-bandgap semiconductors lie at the heart of next-generation high-power, high-frequency electronics. Here, in this paper, we report the growth of ultrawide-bandgap boron nitride (BN) thin films on wide-bandgap gallium nitride (GaN) by pulsed laser deposition. Comprehensive spectroscopic (core level and valence band x-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, and Raman) and microscopic (atomic force microscopy and scanning transmission electron microscopy) characterizations confirm the growth of BN thin films on GaN. Optically, we observed that the BN/GaN heterostructure is second-harmonic generation active. Moreover, we fabricated the BN/GaN heterostructure-based Schottky diode that demonstrates rectifying characteristics, lower turn-on voltage, and an improved breakdown capability (~234 V) as compared to GaN (~168 V), owing to the higher breakdown electrical field of BN. Our approach is an early step toward bridging the gap between wide and ultrawide-bandgap materials for potential optoelectronics as well as next-generation high-power electronics.
In May 2023, the LIGO-Virgo-KAGRA (LVK) Collaboration reported the likely black hole-neutron star (BHNS) merger GW230529_181500. The signal was observed with high significance in only one detector, limiting constraints on the black hole spin and motivating our study of disruption in this mass regime. That event is expected to be the merger of a 2.5–4.5 M ⊙ primary with a secondary compact object of mass between 1.2–2.0 M ⊙ . This makes it the first BHNS merger with a significant potential for the production of electromagnetic (EM) counterparts, and provides further evidence for compact objects existing within the suspected lower mass gap. To produce post-merger EM transients, the component of the black hole spin aligned with the orbital angular momentum must be sufficiently high, allowing the neutron star to be tidally disrupted. The disrupting BHNS binary may then eject a few percent of a solar mass of matter, leading to an observable kilonova driven by radioactive decays in ejecta, and/or a compact-binary gamma-ray burst (cbGRB) resulting from the formation of an accretion disk and relativistic jet. Determining which mergers lead to disruption of the neutron star is necessary to predict the prevalence of EM signals from BHNS mergers, yet most BHNS simulations so far have been performed far from the minimum spin required for tidal disruption. Here, we use the Spectral Einstein Code to explore the behavior of BHNS mergers in a mass range consistent with GW230529_181500 close to that critical spin, and compare our results against the mass remnant model currently used by the LVK Collaboration to predict the probability of tidal disruption. Furthermore, our numerical results reveal the emergence of non-zero accretion disks even below the predicted NS disruption limit, of low mass but capable of powering cbGRBs. Our results also demonstrate that the remnant mass model underpredicts the disk mass for the DD2 equation of state, while they are within expected modeling errors for SFHo. The disruption limit itself, however, is not found to significantly differ from the predictions of the analytical model, unless remnant masses M rem ≲ 0.001 M ⊙ prove interesting observationally. In all of our simulations, any kilonova signal would be dim and most likely dominated by post-merger disk outflows.
Heterostructures based on ultrawide-bandgap (UWBG) semiconductors (bandgap > 4.0 eV), such as BN and diamond, hold significant importance for the development of high-power electronics in the next generation. However, achieving in situ heteroepitaxy of BN/diamond or vice versa remains exceptionally challenging due to the complex growth kinetics involved. In this work, we grew BN thin film on (100) single-crystal diamonds using pulsed laser deposition and investigated its structural, magnetic, optical, and thermal properties. Throughout this study, the structural analyses confirmed the growth of BN films, which exhibited diamagnetic behavior at room temperature. Notably, the film demonstrated anisotropic refractive index characteristics within the visible-to-near-infrared wavelength range. The room-temperature cross-plane thermal conductivity of BN is 1.53 ± 0.77 W/mK, while the thermal conductance of the BN/diamond interface is 20 ± 2 MW/m 2 K. These findings have significant implications for a range of device applications based on UWBG BN/diamond heterostructures.
We present the initiation of a community-driven framework for the integration of accelerator digital twins into control systems: Twinac. Few facilities have fully integrated accelerator digital twins like at Cornell’s CHESS. Many facilities have active research to employ surrogate models to aid in operational decisions like at Argonne’s ALS, MSU’s FRIB, SLAC’s LCLS-II, and Fermilab’s FAST/IOTA, PIP-II, and main complex. To lower the barrier to entry for all accelerator facilities to build and benefit from a digital twin of their own accelerators, we propose the following software framework. Twinac will provide the capability to compose one’s own digital twin using reusable components engineered at other facilities. With this model in place, Twinac will also support tools for (1) predictive maintenance systems; (2) discovery of correlated but uncontrolled environmental factors, like seasonal temperature variations causing performance changes on power supplies, magnets, etc.; and (3) prototyping and updating sophisticated optimization and controls algorithms. The Twinac framework will enable sharing and simplified deployment of modeled components and control algorithms at all facilities. With an inter-facility team to build and support the Twinac framework, it will be easy to publish and try out the latest advancements at one’s own facility.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴ speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously previously impossible with conventional tools while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.
Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex.
Digital twins (DTs) are predictive models of a physical system that dynamically update to reflect any changes. This concept was first conceived as early as 1993 by David Gelernter in his speculative non-fiction Mirror Worlds. DTs were coined in 2002 by Michael Grieves and applied to Product Lifecycle Management for manufacturing. Since then, industry has been developing tools to simplify the creation, deployment, and use of digital twins for manufacturing, fleet management, and biological systems. We will recommend industry standard technology and interfaces that we should adopt in the accelerator community. We further identify gaps in this technology to which we can add new capabilities and solutions, which can be expanded for our use cases.
Abstract The recent detections of the ∼10 s longγ-ray bursts (GRBs) 211211A and 230307A followed by softer temporally extended emission (EE) and kilonovae point to a new GRB class. Using state-of-the-art first-principles simulations, we introduce a unifying theoretical framework that connects binary neutron star (BNS) and black hole–NS (BH–NS) merger populations with the fundamental physics governing compact binary GRBs (cbGRBs). For binaries with large total masses,M tot ≳ 2.8M ⊙ , the compact remnant created by the merger promptly collapses into a BH surrounded by an accretion disk. The duration of the pre-magnetically arrested disk (MAD) phase sets the duration of the roughly constant power cbGRB and could be influenced by the disk mass,M d . We show that massive disks (M d ≳ 0.1M ⊙ ), which form for large binary mass ratiosq≳ 1.2 in BNS orq≲ 3 in BH–NS mergers, inevitably produce 211211A-like long cbGRBs. Once the disk becomes MAD, the jet power drops with the mass accretion rate as M ̇ ∼ t − 2 , establishing the EE decay. Two scenarios are plausible for short cbGRBs. They can be powered by BHs with less massive disks, which form for otherqvalues. Alternatively, for binaries withM tot ≲ 2.8M ⊙ , mergers should go through a hypermassive NS (HMNS) phase, as inferred for GW170817. Magnetized outflows from such HMNSs, which typically live for ≲1 s, offer an alternative progenitor for short cbGRBs. The first scenario is challenged by the bimodal GRB duration distribution and the fact that the Galactic BNS population peaks at sufficiently low masses that most mergers should go through an HMNS phase.
We propose a universal framework for a system of virtual accelerator controls (Twinac), a standard toolkit for research institutions to design, maintain, and use a real-time, end-to-end "digital twin" of their particle accelerator facility. This virtual counterpart will mirror any physical accelerator to provide (1) predictive maintenance; (2) surveillance of hidden environmental factors, such as seasonal temperature variations, which could impact performance of power supplies, magnets, and other instruments; and (3) a capability to model novel ways to operate the accelerator without risking equipment damage. Twinac is envisioned as accelerator facility agnostic, allowing institutions to share and reuse myriad simulation approaches (analytics-based, A.I.-driven, or combinations of the like) across facilities. The Twinac system lays the groundwork for a collaborative network of institutions to maintain and update this shared virtual accelerator technology.
The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴× speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously—previously impossible with conventional tools—while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.