The generation and manipulation of entanglement with low error are essential in quantum information systems. In practice, two-qubit entangling gates constitute a dominant error source, limiting circuit depths and performance in fault-tolerant architectures. Using a neutral-atom quantum processor, we realized entangling controlled-Z gates with a high-Rabi-frequency smooth-amplitude pulse, employing state-selective readout and qubit reuse for fast calibration, and achieved a fidelity of 99.854(4)%, which improved to 99.941(3)% upon loss postselection, with stable performance for 10 hours. We then used these low-error gates in quantum circuits with coherent atom rearrangement. Performance was benchmarked by creating and disentangling cluster states, and subsequently, we studied nonlocally entangled states with scrambling circuits featuring longer-range connectivity. Our approach provides a route toward deep-circuit, efficient fault-tolerant quantum computation.

Experimental reactor needed much more uranium and heavy water than the country could muster.

Precisely regulated metal clusters are crucial for maximizing the utilization efficiency of active metal atoms. In this study, we controllably introduced oxygen vacancy (Ov) into TiO2 via H2 reduction to regulate the assembly of Pd atoms for furfural decarbonylation. The optimized Pd/TiO2 catalyst with smaller Pd clusters and a suitable oxygen vacancy concentration achieved a 99.9% furfural conversion and a 92.4% furan selectivity at 200 °C and 0.3 MPa N2 pressure, showing high catalytic performance under relatively mild conditions. Structural characterizations revealed that oxygen vacancies acted as anchoring sites for Pd species, and an appropriate vacancy concentration favored the formation of highly dispersed Pd clusters rather than larger Pd nanoparticles. Comprehensive structural and electronic analyses revealed strong interactions between Pd species and oxygen-vacant TiO2, leading to the formation of Pd2+-Ov-Ti interfacial sites. These interfacial sites promote carbonyl activation and CO elimination, while adjacent Pd0 sites facilitated H transfer to produce furan. This work not only develops a highly efficient and tunable Pd-based catalyst for the furfural decarbonylation, but also provides a versatile strategy for optimizing catalytic interface via oxygen vacancies.

Interfacial nanopatterning is central to nanotechnology, with applications ranging from electronics to sensing and soft nanofabrication. Conventional approaches often rely on top-down methodologies that require high energy inputs and sophisticated equipment, and offer limited scalability. Bottom-up strategies, particularly block copolymer (BCP) self-assembly, have emerged as attractive alternatives, yet they generally yield static structures whose dimensions are dictated by BCP macromolecular parameters. In this work, we introduce an additional level of tunability by exploiting the thermoresponsiveness of one block within an amphiphilic BCP. Poly(N-isopropylacrylamide)-block-(2-acetoacetoxyethyl methacrylate), P(NIPAM-b-AEMA), was synthesized and self-assembled at the air-water interface using a Langmuir-Pockels trough. Surface compression and temperature guide the evolution of the monolayer from continuous films to spherical nanodomains. Atomic force microscopy (AFM) and grazing-incidence small-angle x-ray scattering (GISAXS) show that compression mainly controls the spacing between nanodomains, whereas temperature changes their size, spacing, and order through thermoresponsive reorganization of the interfacial film. Above the P(NIPAM) lower critical solution temperature, the nanodomain array becomes more regular, consistent with temperature-induced reorganization of the copolymer at the interface. These results identify thermoresponsive BCPs as useful platforms for tunable interfacial self-assembly and provide a simple route to stimuli-responsive nanostructures at fluid interfaces.

In Very Low Earth Orbit (VLEO), spacecraft encounter residual atmosphere primarily composed of atomic oxygen (AO). The drag and operational lifetime of these spacecraft are determined by the interaction between AO and the surface, which for aluminum is a passivation layer of Al2O3. We present a blueprint for developing machine-learned interatomic potentials for reactive gas-surface systems. Gaussian-Moment Neural Networks, implemented in apax, are trained using an active learning workflow that incorporates a spin-state switching scheme. This approach accounts for the triplet-to-singlet transition that AO undergoes upon adsorption, a process usually neglected in machine-learned potential studies. This methodology enables the simulation of thousands of hyperthermal impacts on (0001) Al2O3 at orbital velocity with near-first-principles accuracy for systems four times larger and four orders of magnitude cheaper than ab initio molecular dynamics. Alumina is not an inert surface. Most impacting atoms adsorb rather than reflect, and with continuous exposure, the surface reaches a dynamic steady state where ∼14% of the binding sites are occupied. The surface remains intact. Atoms desorb primarily as O2 formed by recombination, indicating that surface chemistry, rather than simple scattering, governs the energy transfer during impact. Conventional spacecraft drag models assume that impacting atoms desorb thermally in equilibrium with the surface. Our findings demonstrate that this assumption is invalid for alumina. We determine an energy accommodation coefficient of 0.61, which is lower than the 0.82 to 1.0 range measured on actual spacecraft surfaces. This discrepancy quantifies the effect of technical surface roughness. When scaling the simulated momentum transfer to the European Space Agency's GOCE satellite, the measured reentry drag force is reproduced within a factor of 1.8. Since an atomically smooth surface still exhibits strong energy accommodation through chemical bond formation, effective drag reduction in VLEO requires materials that are both atomically smooth and chemically inert.

Malignant catarrhal fever (MCF) is a severe lymphoproliferative disease of domestic and wild ruminants caused by macaviruses, mainly ovine gammaherpesvirus 2 (OvHV-2) and alcelaphine gammaherpesvirus 1 (AlHV-1). Although relevant in sub-Saharan Africa, molecular epidemiological data remain limited, particularly in Namibia. This study investigated the occurrence and diversity of MCF viruses in Namibian livestock and contextualised local strains globally.Fifteen samples from cattle and sheep suspected of MCF were collected across five Namibian regions in 2023. Viral DNA was screened by duplex nested PCR, followed by amplification and sequencing of selected OvHV-2 (ORF50, ORF75, Ov9.5) and AlHV-1 (ORF50, A9.5) genomic regions. Phylogenetic and genetic distance analyses assessed viral diversity.OvHV-2 was the most commonly reported, being detected in 13/15 samples from both symptomatic cattle and asymptomatic sheep, whereas AlHV-1 was identified in two bovine cases. OvHV-2 sequences showed limited diversity and clustered into two main clades, without clear geographic structuring and with intermixing between host species. Several Namibian strains were closely related to sequences from Africa, Europe, Asia, and North America, depending on the genomic region analysed. AlHV-1 sequences were related to strains from southern and eastern Africa.These findings support widespread OvHV-2 circulation and indicate sheep as a likely infection source for cattle. The lack of spatial structure and presence of multiple clades suggest complex transmission dynamics, potentially influenced by animal movements, although the role of livestock trade and wildlife-mediated transmission remains unclear. Enhanced molecular surveillance, biosecurity, and control of animal translocations are needed to mitigate MCF impact in the region.

In this paper, the adsorption properties of K atoms on the surface of two-dimensional MoSi2N4 were investigated via density functional theory. It was found that the adsorption energies of K atom at the Mo-top, N-top and Si-top sites of pristine MoSi2N4 were - 0.03 eV, - 0.05 eV and - 0.08 eV, respectively. The diffusion barrier of K atom on the MoSi2N4 surface was calculated to be only 0.052 eV, indicating the excellent diffusion ability of K atom on MoSi2N4. With the increasing of adsorbed K atoms, stable adsorption of K atoms on the MoSi2N4 surface was no longer achievable. By introducing Si-vacancy defects, the adsorption energy of K atoms at the most favourable adsorption site on the MoSi2N4 surface reached - 3.92 eV. Compared with pristine MoSi2N4, the adsorption strength was enhanced by approximately 50 times. Meanwhile, the number of stably adsorbed K atoms increased from 1 to 6. Accordingly, a maximum theoretical K storage capacity of 773 mAh/g and an open-circuit voltage of 1.66 V were obtained. Moreover, the diffusion barrier of K atom on the VSi-MoSi2N4 surface was calculated to be 0.053 eV. The modified two-dimensional MoSi2N4 combined excellent K atom transport capability with moderate adsorption strength, demonstrating its potential application as an anode material for potassium-ion batteries.

Understanding how the uranyl ion (UO22+) exploits native metal-binding sites in human proteins is essential for elucidating uranium transport, accumulation, and toxicity. In this computational study, we investigated how uranyl is accommodated within the protein pockets of human serum transferrin and human serum albumin, and compared these interactions with those in an engineered super uranyl-binding protein (SUP). QM/MM geometry optimizations and harmonic vibrational frequency calculations were performed starting from available X-ray crystal structures. The intrinsic strengths of individual uranyl-protein interactions were quantified using Local Vibrational Mode Analysis (LMA), complementary with Quantum Theory of Atoms in Molecules (QTAIM) and Natural Bond Orbital (NBO) analyses. Histidine residues form the strongest individual U-N interactions, whereas aspartate and glutamate provided equally strong or stronger capture through bidentate U-O coordination. In human serum transferrin, the native carbonate cofactor forms an important component of the uranyl coordination sphere, whereas hydrogen bonds involving the uranyl oxo atoms contributed only weakly to binding. Comparison with the high-affinity SUP revealed that human serum proteins bind uranyl nearly as strongly as the engineered protein, highlighting their potential role in uranium transport and fate following human exposure.

First-principles calculations play a pivotal role in contemporary chemical studies. The most commonly employed algorithms are based on density functional theory (DFT), and in certain instances, the Hartree-Fock method is also utilized. Although these two methods can efficiently yield accurate results for small-to-medium-sized systems, they incur prohibitively high computational costs for systems containing 1000-100 000 atoms. However, capturing such scales is indispensable for replicating realistic complex reactions. Methods based on orbital-free density functional theory (OFDFT), which are expected to significantly reduce computational costs compared to DFT while maintaining quantum-scale accuracy, are thus garnering increasing attention. In this review, the independent-electron methods and the Hohenberg-Kohn theorem are briefly introduced initially. Subsequently, the most crucial component of OFDFT methods, namely, the TF model and its derivatives, is elaborated. A unique extended electron model (EEM) that incorporates geometric algebra into the wavefunction is also reviewed. The latest advancements in this field, particularly those driven by deep learning (DL), are presented in the final part of this review to offer an overview of the trends in OFDFT. This review aims to provide a fundamental understanding of OFDFT to audiences interested in adopting or developing these appealing methods in their research.

High-resolution crystal structure determination using X-ray, electron or neutron diffraction is often limited by poor crystallinity, small crystallite size and instrumental factors, which cause significant diffraction peak broadening and a consequent loss of information. This challenge is particularly severe for nanocrystalline materials, whose X-ray powder diffraction (XRPD) profiles exhibit extensive peak overlap, thereby hindering the reliable extraction of structural information through conventional crystallographic methods. We present here a novel deep-learning-based approach to increase the probability of determining the structure of nanocrystals. A deep-learning model is trained to transform simulated diffraction patterns, degraded by reduced crystalline order, into optimal profiles representative of ideal perfectly crystalline materials. Instead of attempting direct structure inference, this approach improves data by making them better suited for standard crystallographic analyses. The resulting pipeline is both robust and versatile, demonstrating consistent performance across varying crystallite sizes on simulated profiles. The procedure has also been successfully applied to experimental data. To our knowledge, this is the first application of deep learning to enhance experimental XRPD data, representing a breakthrough in the structural characterization of nanocrystalline materials, specifically by enabling progress in difficult cases where atomic-level structure determination is hindered by poor diffraction data.

Future fusion reactors require robust monitoring of plasma equilibrium and fluctuations to sustain and optimize energy gain, despite severely restricted machine access. We demonstrate a multifunctional interferometer-polarimeter on the DIII-D tokamak that utilizes only four radial chords to simultaneously measure: (a) the interferometric phase shift, (b) the Faraday effect, (c) the Cotton-Mouton effect, (d) density and magnetic fluctuations up to 10 MHz, and (e) fluctuation toroidal mode numbers and vertical wavenumbers. This is achieved via a three-wave, dual-sensor scheme incorporating four vertically and toroidally displaced chords. The Faraday effect measurements constrain the core magnetic equilibrium, directly determining the magnetic axis vertical position. The Cotton-Mouton effect measurement yields a robust line-integrated density measurement free of fringe jumps. Furthermore, fluctuation and wavenumber measurements enable mode localization, facilitating a spatiotemporal survey of multiscale fluctuations from the plasma core to the edge. These results validate a compact interferometry-polarimetry approach capable of delivering comprehensive equilibrium and fluctuation measurements under limited access, supporting the demands of fusion reactors.

Cyclodextrin-based metal-organic frameworks (CD-MOFs) has attracted considerable attention owing to their high crystallinity, water solubility, and biocompatibility. However, integrating atomically precise metal nanoclusters (MNCs) into CD-MOFs remains largely unexplored. Only a few reports describe encapsulating MNCs within CD-MOFs, but none have crystallographically elucidated the structural aspects of MNCs-embedded CD-MOFs. Moreover, the site-specific positioning of MNCs in MOF-highly desirable for developing active catalysts and modulating properties-remains an unsolved challenge. Herein, we report the atomically precise hybrid assembly of Ag6 nanoclusters (NCs) within γ-cyclodextrin (γ-CD) MOFs. Ultrasmall Ag6 NCs are immobilized within the porous alkali metal ion-coordinated (K+, Rb+, Cs+) γ-CD framework, affording a well-defined metal nanocluster-γ-CD-MOF architecture at the nanoscale. Single-crystal X-ray diffraction unambiguously resolves a series of Ag6 NC-incorporated γ-CD MOFs, denoted as Ag6/M+/γ-CD (M = K, Rb, Cs). Incorporation of the Ag6 NCs profoundly modulates their intrinsic photophysical properties, induces supramolecular chirality, and enables second-harmonic generation (SHG). Furthermore, the Ag6/M+/γ-CD systems exhibit thermally activated delayed fluorescence, as corroborated by detailed theoretical calculations. Notably, these hybrid frameworks display pronounced circularly polarized luminescence, arising from chirality transfer from the γ-CD host and the spatially organized ligand shell surrounding the luminescent Ag6 NC assemblies.

We present WEST, an open-source plane-wave pseudopotential code for large-scale excited-state materials simulations, and describe its theoretical foundations, software architecture, and capabilities. WEST implements full-frequency GW, quantum defect embedding theory, the Bethe-Salpeter equation, and time-dependent density functional theory within a common algorithmic framework that avoids the explicit computation of virtual electronic states. By combining density functional and density matrix perturbation theory, low-rank representations of the dielectric screening and exact exchange, and localization techniques, WEST achieves favorable computational scaling with system size. The code supports the calculation of quasi-particle and neutral excitation energies, optical and photoluminescence spectra, excited-state forces, and non-adiabatic couplings, with interoperable workflows connecting to quantum chemistry, vibronic coupling, and quantum computing packages. A hierarchical parallelization strategy and graphics processing unit (GPU) acceleration deliver near-ideal strong scaling to thousands of GPUs, enabling accurate excited-state simulations of systems with more than a thousand atoms. Representative applications, spanning the full optical cycle of solid-state spin defects, self-trapped excitons in metal-halide perovskites, and the optical response of liquid water and ice, demonstrate the accuracy and versatility of the code across diverse material classes. The capabilities implemented in WEST establish the code as a scalable platform for predictive excited-state simulations, high-throughput materials discovery, and the generation of high-fidelity datasets for machine learning in computational materials science.

Fluorination has become a key molecular engineering strategy in medicinal chemistry and nanomedicine, enabling the fine tuning of the physicochemical and biological properties of therapeutic molecules and drug delivery systems. The unique characteristics of fluorine, including its small steric footprint, high electronegativity, and exceptional strength of the C-F bond, enhance metabolic stability, modulate lipophilicity and acidity, and improve pharmacokinetic profiles. Beyond these well-established effects, fluorination has emerged as a tool for controlling the assembly and biological behavior of nanocarriers. Fluorinated biomolecules, polymers, dendrimers, and lipid-based systems often display improved colloidal stability, reduced nonspecific protein adsorption, enhanced cellular uptake, and efficient endosomal escape, ultimately leading to superior intracellular delivery of nucleic acids, proteins, and other therapeutic cargoes. Here we summarize recent advances in the fluorination of both therapeutic cargoes and delivery carriers, emphasizing how different fluorination strategies, including single fluorine atoms, trifluoromethyl groups, linear perfluoroalkyl chains, and emerging short branched fluorinated motifs, can be exploited to optimize drug delivery performance. Attention is devoted to the relationship between fluorinated molecular architectures, self-assembly, and bio-nano interactions that govern biological activity. Furthermore, the advantages and limitations of current fluorination approaches are discussed. Finally, we highlight the need for a deeper mechanistic understanding of how fluorinated moieties interact with proteins and biological membranes. Advanced physicochemical techniques, including neutron and X-ray scattering and reflectometry, are expected to provide fundamental insights into these processes, enabling the rational design of more effective, safer, and environmentally sustainable fluorinated therapeutics and nanomedicines.