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Quantum algorithms for topological data analysis (TDA) seem to provide an exponential advantage over the best classical approach while remaining immune to dequantization procedures and the data-loading problem. In this paper, we give complexity-theoretic evidence that the central task of TDA—estimating Betti numbers—is intractable even for quantum computers. Specifically, we prove that the problem of computing Betti numbers exactly is #P-hard, while the problem of approximating Betti numbers up to multiplicative error is NP-hard. Moreover, both problems retain their hardness if restricted to the regime where quantum algorithms for TDA perform best. Because quantum computers are not expected to solve #P-hard or NP-hard problems in subexponential time, our results imply that quantum algorithms for TDA offer only a polynomial advantage in the worst case. We support our claim by showing that the seminal quantum algorithm for TDA developed by Lloyd, Garnerone, and Zanardi achieves a quadratic speedup over the best-known classical approach in asymptotically almost all cases. Finally, we argue that an exponential quantum advantage can be recovered if the input data is given as a specification of simplices rather than as a list of vertices and edges.

Lithium–sulfur (Li–S) batteries can potentially outperform state-of-the-art lithium-ion batteries, but their further development is hindered by challenges, such as poor electrical conductivity of sulfur and lithium sulfide, shuttle phenomena of lithium polysulfides, and uneven distribution of solid reaction products. Herein, free-standing carbon nanofibers embedded with oxygen-deficient titanium dioxide nanoparticles (TiO2-x/CNFs) has been fabricated by a facile electrospinning method, which can support active electrode materials without the need for conductive carbon and binders. By carefully controlling the calcination temperature, a mixed phase of rutile and anatase was achieved in the TiO2-x nanoparticles. The hybridization of anatase/rutile TiO2-x and the oxygen vacancy in TiO2-x play a crucial role in enhancing the conversion kinetics of lithium polysulfides (LiPSs), mitigating the shuttle effect of LiPSs, and enhancing the overall efficiency of the Li–S battery system. Additionally, the free-standing TiO2-x/CNFs facilitate uniform deposition of reaction products during cycling, as confirmed by synchrotron X-ray imaging. As a result of these advantageous features, the TiO2-x/CNFs-based cathode demonstrates an initial specific discharge capacity of 787.4 mAh g−1 at 0.5 C in the Li–S coin cells, and a final specific discharge capacity of 584.0 mAh g−1 after 300 cycles. Furthermore, soft-packaged Li–S pouch cells were constructed using the TiO2-x/CNFs-based cathode, exhibiting excellent mechanical properties at different bending states. This study presents an innovative approach to developing free-standing sulfur host materials that are well suited for flexible Li–S batteries as well as for various other energy applications. Graphical Abstract

Remote epitaxial growth via a graphene interlayer and subsequent mechanical exfoliation of a free-standing membrane is a recently developed technique used to transfer complex oxide thin films onto non-native substrates to form heterogeneously integrated structures for various device applications. One such oxide is Yttrium Iron Garnet (YIG), a material of choice for a wide range of magnetoelectric and spintronic devices owing to its ferromagnetism with high Curie temperature as well as high quality factor and low losses in microwave frequencies. YIG is predominantly grown on lattice matched Gadolinium Gallium Garnet (GGG) substrates, but by utilizing the remote epitaxy technique, high quality YIG films can be transferred from GGG onto another substrate such as piezoelectric Lithium Niobate (LN). Mechanical strain coupling between the layers and magnetostrictive nature of YIG would allow for the investigation of the interplay in YIG/LN structures leading to the design of novel frequency agile magneto-acoustic devices. In this study functional properties of a YIG film grown using PLD on graphene-coated GGG substrate were investigated and compared to traditional YIG on GGG. Both materials were characterized in terms of crystal structure, surface morphology, FMR and Gilbert damping, and Raman and XAS spectroscopy. It was found that YIG on graphene-coated GGG exhibits significantly higher microwave losses than standard YIG on GGG (FMR linewidth 30.9 vs 2.1 Oe at 10 GHz, and Gilbert damping coefficient 15.4 × 10-4 vs 3.4 × 10-4 respectively), which was attributed to increased concentration of Fe2+ cations in YIG/Graphene/GGG. While the damping is higher in these studied films compared to YIG grown directly on GGG, the resulting properties are still very favorable compared to many other competing materials which can be grown without the need for lattice matched substrates, such as metallic ferromagnets.

Single‐crystalline inorganic semiconductor nanomembranes (NMs) have attracted great attention over the last decade, which poses great advantages to complex device integration. Applications in heterogeneous electronics and flexible electronics have been demonstrated with various semiconductor nanomembranes. Single‐crystalline aluminum nitride (AlN), as an ultrawide‐bandgap semiconductor with great potential in applications such as high‐power electronics has not been demonstrated in its NM forms. This very first report demonstrates the creation, transfer‐printing, and characteristics of the high‐quality single‐crystalline AlN NMs. This work successfully transfers the AlN NMs onto various foreign substrates. The crystalline quality of the NMs has been characterized by a broad range of techniques before and after the transfer‐printing and no degradation in crystal quality has been observed. Interestingly, a partial relaxation of the tensile stress has been observed when comparing the original as‐grown AlN epi and the transferred AlN NMs. In addition, the transferred AlN NMs exhibits the presence of piezoelectricity at the nanoscale, as confirmed by piezoelectric force microscopy. This work also comments on the advantages and the challenges of the approach. Potentially, the novel approach opens a viable path for the development of the AlN‐based heterogeneous integration and future novel electronics and optoelectronics.

We propose a new class of error-correcting dynamic codes in two and three dimensions that has no explicit connection to any parent subsystem code. The two-dimensional code, which we call the CSS (Calderbank-Shor-Steane) honeycomb code, is geometrically similar to that of the honeycomb code by Hastings and Haah and also dynamically embeds an instantaneous toric code. However, unlike the honeycomb code, it possesses an explicit CSS structure and its gauge checks do not form a subsystem code. Nevertheless, we show that our dynamic protocol conserves logical information and possesses a threshold for error correction. We generalize this construction to three dimensions and obtain a code that fault tolerantly alternates between realizing two type-I fracton models, the checkerboard and the X-cube model. Finally, we show the compatibility of our CSS honeycomb-code protocol and the honeycomb code by showing the possibility of randomly switching between the two protocols without information loss while still measuring error syndromes. We call this more general aperiodic structure “dynamic tree codes,” which we also generalize to three dimensions. We construct a probabilistic finite automaton prescription that generates dynamic tree codes correcting any single-qubit Pauli errors and can be viewed as a step toward the development of practical fault-tolerant random codes.

Although architected materials based on truss networks have been shown to possess advantageous or extreme mechanical properties, those can be highly affected by tolerances and uncertainties in the manufacturing process, which are usually neglected during the design phase. Deterministic computational tools typically design structures with the assumption of perfect, defect-free architectures, while experiments have confirmed the inevitable presence of imperfections and their possibly detrimental impact on the effective properties. Information about the nature and expected magnitude of geometric defects that emerge from the additive manufacturing processes would allow for new designs that aim to mitigate (or at least account for) the effects of defects and to reduce the uncertainty in the effective properties. To this end, we here investigate the effects of four most commonly found types of geometric imperfections in trusses, applied to eleven representative truss topologies in two and three dimensions. Through our study, we (i) quantify the impact of imperfections on the effective stiffness through computational homogenization, (ii) examine the sensitivity of the various truss topologies with respect to those imperfections, (iii) demonstrate the applicability of the model through experiments on 3D-printed trusses, and (iv) present a machine learning framework to predict the presence of defects in a given truss architecture based merely on its mechanical response.

Heart failure with preserved ejection fraction (HFpEF) is a major challenge in cardiovascular medicine, accounting for ≈50% of all cases of heart failure. Despite the ongoing efforts, no medical device has yet received FDA approval. This is largely due to the lack of an in vivo model of the HFpEF hemodynamics, resulting in the inability to evaluate device effectiveness in vivo prior to clinical trials. Here, the development of a highly tunable porcine model of HFpEF hemodynamics is described using implantable soft robotic sleeves, where controlled actuation of a left ventricular and an aortic sleeve can recapitulate changes in ventricular compliance and afterload associated with a broad spectrum of HFpEF hemodynamic phenotypes. The feasibility of the proposed model in preclinical testing is demonstrated by evaluating the hemodynamic response of the model post-implantation of an interatrial shunt device, which is found to be consistent with findings from in silico studies and clinical trials. This work overcomes limitations of prior HFpEF models, such as low hemodynamic accuracy, high costs, and long development phases. The versatile and adjustable platform introduced can transform HFpEF device development, aiming to enhance the lives of the 32 million people affected globally.

We used coarse-grain molecular dynamics simulations to screen all possible histidine-bearing tetrapeptide sequences, finding novel peptide sequences with pH-tunable assembly properties. These tetrapeptides could be used for various biological applications, such as triggered delivery of bioactive molecules., Division of Civil, Mechanical and Manufacturing Innovation; Office of Naval Research

Field-emission-based vacuum transistors have been proposed as promising candidates for future high-power and harsh-environment electronic devices. However, the lack of an integrated anode is still an issue for vertical field-emission vacuum transistors for some applications such as radiation-hard vacuum electronic-based circuits. In this work, an anode-integration technology enabled by tilted metal deposition is proposed and experimentally demonstrated on GaN gated field emitter arrays (FEAs). Full transistor prototypes with a 103 on-off ratio in anode current are demonstrated. This process is compatible with gated FEAs of various materials, the vacuum channel can be sealed during fabrication, and the vacuum channel length can be controlled via multiple process parameters., Department of Defense (DoD), Department of Energy (DOE)

Materials capable of dynamically and reversibly altering their emission are relevant for numerous optical applications. Here, the anisotropic morphology‐directed light emission from fluorescent Janus emulsion droplets, an intrinsically chemo‐responsive material platform, is investigated. Informed by experimental observations of morphology‐dependent optical confinement of internally emitted light within the higher refractive index phases, ray‐tracing is used to predict and fine‐tune the droplets’ optical properties and their ability to concentrate light. Theoretical prediction and closely matching experimental results show that the collection of incident light and the confinement of emitted light in the internal droplet phase due to total internal reflection both contribute to the droplets’ anisotropic light emission profile. A novel ratiometric dual‐angle fluorescence detection approach that exploits the gravitational alignment of the droplets is implemented to quantify the morphology‐dependent large‐scale chemically‐induced modulation of the anisotropic emission of droplet layers. Relevant emulsion design parameters are systematically examined to enhance the signal‐to‐noise ratio, and a second emitter is co‐compartmentalized inside the droplets to amplify the anisotropic light confinement via an absorption–emission cascade. Preferential excitation of dyes in proximity to the internal droplet interface enhances the collected light intensity, demonstrating that dye‐loaded Janus emulsion droplets function as stimuli‐responsive, tunable, fluorescent optical elements.

Propane dehydrogenation (PDH) is an on-purpose catalytic technology to produce propylene from propane that operates at high temperatures, 773-973 K. Several key industry players have been active in developing new catalysts and processes with improved carbon footprint and economics, where Pt-based catalysts have played a central role. The optimization of these catalytic systems through computational and atomistic simulations requires large-scale models that account for their reactivity and dynamic properties. To address this challenge, we developed a new reactive ReaxFF force field (2023-Pt/C/H) that enables large-scale simulations of PDH reactions catalyzed on Pt surfaces. The optimization of force-field parameters relies on a large training set of density functional theory (DFT) calculations of Pt-catalyzed PDH mechanism, including geometries, adsorption and relative energies of reaction intermediates, and key C-H and C-C bond-breaking/forming reaction steps on the Pt(111) surface. The internal validation supports the accuracy of the developed 2023-Pt/C/H force-field parameters, resulting in mean absolute errors (MAE) against DFT data of 14 and 12 kJ mol-1 for relative energies of intermediates and energy barriers, respectively. We demonstrated the applicability of the 2023-Pt/C/H force field with reactive molecular dynamics simulations of propane on different Pt surface topologies and temperatures. The simulations successfully model the formation of propene in the gas phase as well as competitive, unproductive reactions such as deep dehydrogenation and C-C bond cleavage that produce H, C1 and C2 adsorbed species responsible of catalytic deactivation of Pt surface. Results show the following reactivity order: Pt(111) < Pt(100) < Pt(211), and that for the stepped Pt(211

Nerve agents are highly toxic chemical compounds that pose significant threats to human health and safety. Detecting and identifying these agents quickly is crucial for preventing their exposure and providing timely treatment. This study proposes the use of nanohybrids comprising crystalline octahedral molybdenum iodide cluster material supported on graphene (Mo6@Graphene) as a novel sensing material for the detection of dimethyl methylphosphonate (DMMP), a simulant of sarin nerve agent. The electrical sensing performance towards different concentrations of DMMP and at room temperature demonstrated fast and sensitive responses to DMMP, with almost full recovery of the sensor baseline in a few minutes. The calibration curve obtained showed a linear relationship between the sensor response and DMMP concentration, enabling the estimation of the limit of detection (LOD) of 270 ppb. The robustness of the Mo6@Graphene nanomaterial towards DMMP was assessed using various techniques, including Raman spectroscopy, X-ray diffraction and photoelectron spectroscopy. The binding energies (BEs) between DMMP and the supported Mo6 clusters determined by DFT calculations revealed H-bonding interactions involved in the sensing mechanism. The excellent sensing performance of the Mo6@Graphene film, combined with its low power consumption and high stability, makes it a promising candidate for the detection of nerve agents and their simulants.

Nerve agents are highly toxic chemical compounds that pose significant threats to human health and safety. Detecting and identifying these agents quickly is crucial for preventing their exposure and providing timely treatment. This study proposes the use of nanohybrids comprising crystalline octahedral molybdenum iodide cluster material supported on graphene (Mo6@Graphene) as a novel sensing material for the detection of dimethyl methylphosphonate (DMMP), a simulant of sarin nerve agent. The electrical sensing performance towards different concentrations of DMMP and at room temperature demonstrated fast and sensitive responses to DMMP, with almost full recovery of the sensor baseline in a few minutes. The calibration curve obtained showed a linear relationship between the sensor response and DMMP concentration, enabling the estimation of the limit of detection (LOD) of 270 ppb. The robustness of the Mo6@Graphene nanomaterial towards DMMP was assessed using various techniques, including Raman spectroscopy, X-ray diffraction and photoelectron spectroscopy. The binding energies (BEs) between DMMP and the supported Mo6 clusters determined by DFT calculations revealed H-bonding interactions involved in the sensing mechanism. The excellent sensing performance of the Mo6@Graphene film, combined with its low power consumption and high stability, makes it a promising candidate for the detection of nerve agents and their simulants.

The development and optimization of a paper-based ion-selective organic electrochemical transistor (IS-OECT) with an outstanding analytical performance is presented. The combination of thick-film transistor technology with a suitable optimization of the ion-selective membrane composition and thickness allows reaching sensitivities of up to 2.50 mA/decade, which is more than one order of magnitude higher than other similar devices reported up to date. The system shows good selectivity, allowing the detection of low concentrations of potassium in high saline concentrations, and a linear range from 0.1 mM to 100 mM, which covers the relevant clinical range of K+ in blood. A calibration curve for K+ in artificial serum between 1 and 10 mM shows changes in concentrations down to 0.05 mM can be discriminated. Furthermore, the device can be made with a simple manufacturing process, such as drop casting, on low-cost substrate materials and can be operated with a gate voltage of 0 V. This IS-OCET offers promising avenues for developing ion-sensing platforms for the point of need.