The Central Solenoid (CS) is at the core of the EU DEMO tokamak, and has a strong impact on the tokamak design and the overall machine size. By ramping its current, the CS generates a change of magnetic flux, which initiates the plasma, and induces and controls the plasma current. In the context of the conceptual design studies for DEMO coordinated by EUROfusion, the Swiss Plasma Center has developed a simple pre-dimensioning methodology, assuming uniform current density in the solenoid winding pack, and considering the use of Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn and REBCO. The effects of grading of the superconductor and structural steel are also discussed. Since the CS for the EU DEMO will experience tens of thousands of charge/discharge cycles during its lifetime, mechanical fatigue considerations are taken into account. The proposed methodology has been used for the preliminary design of the CS winding pack, and for a number of parametric analyses in the context of the integrated physics and engineering studies for the EU DEMO machine (identifying the relationship between key performance parameters of the solenoid, such as the generated magnetic flux and the fatigue lifetime).
Wide-bandgap perovskite solar cells (PSCs) are attracting increasing attention because they play an irreplaceable role in tandem solar cells. Nevertheless, wide-bandgap PSCs suffer large open-circuit voltage (VOC ) loss and instability due to photoinduced halide segregation, significantly limiting their application. Herein, a bile salt (sodium glycochenodeoxycholate, GCDC, a natural product), is used to construct an ultrathin self-assembled ionic insulating layer firmly coating the perovskite film, which suppresses halide phase separation, reduces VOC loss, and improves device stability. As a result, 1.68 eV wide-bandgap devices with an inverted structure deliver a VOC of 1.20 V with an efficiency of 20.38%. The unencapsulated GCDC-treated devices are considerably more stable than the control devices, retaining 92% of their initial efficiency after 1392 h storage under ambient conditions and retaining 93% after heating at 65 °C for 1128 h in an N2 atmosphere. This strategy of mitigating ion migration via anchoring a nonconductive layer provides a simple approach to achieving efficient and stable wide-bandgap PSCs.
Birds of the crow family adapt food-caching strategies to anticipated needs at the time of cache recovery and rely on memory of the what, where and when of previous caching events to recover their hidden food. It is unclear if this behavior can be explained by simple associative learning or if it relies on higher cognitive processes like mental time-travel. We present a computational model and propose a neural implementation of food-caching behavior. The model has hunger variables for motivational control, reward-modulated update of retrieval and caching policies and an associative neural network for remembering caching events with a memory consolidation mechanism for flexible decoding of the age of a memory. Our methodology of formalizing experimental protocols is transferable to other domains and facilitates model evaluation and experiment design. Here, we show that memory-augmented, associative reinforcement learning without mental time-travel is sufficient to explain the results of 28 behavioral experiments with food-caching birds.
Laser powder bed fusion (LPBF) has great potential for the fabricating complex geometries with improved functionality. In combination with nickel alloys such as Hastelloy X, parts produced with this technology find usage in high-temperature applications. Many studies have focused on the microstructure of Hastelloy X fabricated via LPBF technology, but most have been performed on simple cubic geometries. Considering the aforementioned applications, the parts are often characterized by fine features, like very thin-walled structured, often in the sub-1 mm range. However, little is known about the LPBF fabrication of such structures. The study presented in this paper shows that wall thickness plays an important role in microstructure formation. In situ temperature measurements and thermal simulations showed a difference in thermal history. Local heat accumulation in the vicinity of the scanned tracks was observed for the sample consisting of multiple tracks. Moreover, this effect was enhanced with the number of tracks, leading to different melt pool morphologies. Significantly, coarser solidification cells were found near the sample edges. In addition, larger grains oriented parallel to the build direction were observed for the samples consisting of 3 and more tracks, while for the thinner samples, a very fine microstructure with random orientation was found.
Amino acids and peptides generally exhibit zwitterionic forms with salt bridge (SB) structures in solution but charge-solvated (CS) motifs in the gas phase. Here, we report a study of non-covalent complexes of the protonated amino acid arginine, ArgH+(H2O)n (n = 1-5), produced in the gas phase from an aqueous solution with a controlled number of retained water molecules. These complexes were probed by cold ion spectroscopy and treated by quantum chemistry. The spectroscopic changes induced upon gradual dehydration of arginine were assigned by structural calculations to the transition from SB to CS geometries. SB conformers appear to be present for the complexes with as few as 3 retained water molecules, although energetically CS structures should become prevailing already for ArgH+ with 7-8 water molecules. We attribute the revealed kinetic trapping of arginine in native-like zwitterionic forms to evaporative cooling of the hydrated complexes to as low as below 200 K.
Efficient valorization of lignin, a sustainable source of functionalized aromatic products, would reduce dependence on fossil-derived feedstocks. Oxidative depolymerization is frequently applied to lignin to generate phenolic monomers. However, due to the instability of phenolic intermediates, repolymerization and dearylation reactions lead to low selectivity and product yields. Here, a highly efficient strategy to extract the aromatic monomers from lignin affording functionalized diaryl ethers using oxidative cross-coupling reactions is described, which overcomes the limitations of oxidative methods and affords high-value specialty chemicals. Reaction of phenylboronic acids with lignin converts the reactive phenolic intermediates into stable diaryl ether products in near-theoretical maximum yields (92% for beech lignin and 95% for poplar lignin based on the content of β−O−4 linkages). This strategy suppresses side reactions typically encountered in oxidative depolymerization reactions of lignin and provides a new approach for the direct transformation of lignin into valuable functionalized diaryl ethers, including key intermediates in pharmaceutical and natural product synthesis.
The myelinated white matter tracts of the central nervous system (CNS) are essential for fast transmission of electrical impulses and are often differentially affected in human neurodegenerative diseases across CNS region, age and sex. We hypothesize that this selective vulnerability is underpinned by physiological variation in white matter glia. Using single nucleus RNA sequencing of human post-mortem white matter samples from the brain, cerebellum and spinal cord and subsequent tissue-based validation we found substantial glial heterogeneity with tissue region: we identified region-specific oligodendrocyte precursor cells (OPCs) that retain developmental origin markers into adulthood, distinguishing them from mouse OPCs. Region-specific OPCs give rise to similar oligodendrocyte populations, however spinal cord oligodendrocytes exhibit markers such as SKAP2 which are associated with increased myelin production and we found a spinal cord selective population particularly equipped for producing long and thick myelin sheaths based on the expression of genes/proteins such as HCN2. Spinal cord microglia exhibit a more activated phenotype compared to brain microglia, suggesting that the spinal cord is a more pro-inflammatory environment, a difference that intensifies with age. Astrocyte gene expression correlates strongly with CNS region, however, astrocytes do not show a more activated state with region or age. Across all glia, sex differences are subtle but the consistent increased expression of protein-folding genes in male donors hints at pathways that may contribute to sex differences in disease susceptibility. These findings are essential to consider for understanding selective CNS pathologies and developing tailored therapeutic strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s40478-023-01568-z.
The significant discrepancy observed between the predicted and experimental switching fields in correlated insulators under a DC electric field far-from-equilibrium necessitates a reevaluation of current microscopic understanding. Here we show that an electron avalanche can occur in the bulk limit of such insulators at arbitrarily small electric field by introducing a generic model of electrons coupled to an inelastic medium of phonons. The quantum avalanche arises by the generation of a ladder of in-gap states, created by a multi-phonon emission process. Hot-phonons in the avalanche trigger a premature and partial collapse of the correlated gap. The phonon spectrum dictates the existence of two-stage versus single-stage switching events which we associate with charge-density-wave and Mott resistive phase transitions, respectively. The behavior of electron and phonon temperatures, as well as the temperature dependence of the threshold fields, demonstrates how a crossover between the thermal and quantum switching scenarios emerges within a unified framework of the quantum avalanche.
The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.
In the quest for low power bio-inspired spiking sensors, functional oxides like vanadium dioxide are expected to enable future energy efficient sensing. Here, we report uncooled millimeter-wave spiking detectors based on the sensitivity of insulator-to-metal transition threshold voltage to the incident wave. The detection concept is demonstrated through actuation of biased VO2 switches encapsulated in a pair of coupled antennas by interrupting coplanar waveguides for broadband measurements, on silicon substrates. Ultimately, we propose an electromagnetic-wave-sensitive voltage-controlled spike generator based on VO2 switches in an astable spiking circuit. The fabricated sensors show responsivities of around 66.3 MHz.W⁻¹ at 1 μW, with a low noise equivalent power of 5 nW.Hz−0.5 at room temperature, for a footprint of 2.5 × 10⁻⁵ mm². The responsivity in static characterizations is 76 kV.W⁻¹. Based on experimental statistical data measured on robust fabricated devices, we discuss stochastic behavior and noise limits of VO2 -based spiking sensors applicable for wave power sensing in mm-wave and sub-terahertz range.
The Cold Neutral Medium (CNM) is an important part of the galactic gas cycle and a precondition for the formation of molecular and star forming gas, yet its distribution is still not fully understood. In this work we present extremely high resolution simulations of spiral galaxies with time-dependent chemistry such that we can track the formation of the CNM, its distribution within the galaxy, and its correlation with star formation. We find no strong radial dependence between the CNM fraction and total H i due to the decreasing interstellar radiation field counterbalancing the decreasing gas column density at larger galactic radii. However, the CNM fraction does increase in spiral arms where the CNM distribution is clumpy, rather than continuous, overlapping more closely with H2. The CNM doesn’t extend out radially as far as H i, and the vertical scale height is smaller in the outer galaxy compared to H i with no flaring. The CNM column density scales with total midplane pressure and disappears from the gas phase below values of PT/kB = 1000 K cm−3. We find that the star formation rate density follows a similar scaling law with CNM column density to the total gas Kennicutt-Schmidt law. In the outer galaxy we produce realistic vertical velocity dispersions in the H i purely from galactic dynamics but our models do not predict CNM at the extremely large radii observed in H i absorption studies. We suggest that grand design spiral arms might produce isolated clumps of CNM at these radii.
Flexible Al-air batteries have attracted widespread attention in the field of wearable power due to the high theoretical energy density of Al metal. However, the efficiency degradation and anodizing retardation caused by Al parasitic corrosion severely limit the performance breakthrough of the batteries. Herein, we propose a self-modulating Prussian-blue bifunctional interface membrane to defend and activate Al anode, thus greatly improving the discharge performance of hydrogel-based Al-air battery. When a rational 12 mg·cm-2 membrane is loaded on the anode surface, the effect of anticorrosion and activation is optimal thanks to the formation of a stable and breathable interface. The results demonstrate that a flexible Al-air battery using the interface membrane can output a high power density of 65.76 mW·cm-2. Besides, the battery can achieve a high capacity of 2377.43 mAh·g-1, anode efficiency of 79.78% and energy density of 3176.39 Wh·kg-1 at 10 mA·cm-2. Density functional theory calculations uncover the anticorrosion-activation mechanism of the membrane that Fe3+ with a large number of empty orbitals can accelerate electrons transfer, and nucleophilic reactant [FeⅡ(CN)6]4- promotes the diffusion of Al3+ into the electrolyte.These findings are beneficial to inhibition of interfacial parasitic corrosion and weakening of discharge hysteresis for flexible Al-air batteries.
Surface defects cause non‐radiative charge recombination and reduce the photovoltaic performance of perovskite solar cells (PSCs), thus effective passivation of defects has become a crucial method for achieving efficient and stable devices. Organic ammonium halides have been widely used for perovskite surface passivation, due to their simple preparation, lattice matching with perovskite, and high defects passivation ability. Herein, a surface passivator 2,4,6‐trimethylbenzenaminium iodide (TMBAI) is employed as the interfacial layer between the spiro‐OMeTAD and perovskite layer to modify the surface defect states. It is found that TMBAI treatment suppresses the nonradiative charge carrier recombination, resulting in a 60 mV increase of the open‐circuit voltage (Voc) (from 1.11 to 1.17 V) and raises the fill factor from 76.3% to 80.3%. As a result, the TMBAI‐based PSCs device demonstrates a power conversion efficiency (PCE) of 23.7%. Remarkably, PSCs with an aperture area of 1 square centimeter produce a PCE of 21.7% under standard AM1.5 G sunlight. The unencapsulated TMBAI‐modified device retains 92.6% and 90.1% of the initial values after 1000 and 550 h under ambient conditions (humidity 55%–65%) and one‐sun continuous illumination, respectively.
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy desired objectives under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a significant cost. Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learn- ing, as demonstrated in the Practical Molecular Optimization (PMO) benchmark. Here, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task and a drug discovery case study requiring both exploration and exploitation. Our method achieves a new state-of-the-art in the PMO benchmark which enforces a computational budget, and outperforms the previous best performing method on 19/23 tasks.
Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.
We introduce and analyze a discontinuous Galerkin method for the numerical modeling of the equations of Multiple-Network Poroelastic Theory (MPET) in the dynamic formulation. The MPET model can comprehensively describe functional changes in the brain considering multiple scales of fluids. Concerning the spatial discretization, we employ a high-order discontinuous Galerkin method on polygonal and polyhedral grids and we derive stability and a priori error estimates. The temporal discretization is based on a coupling between a Newmark [Formula: see text]-method for the momentum equation and a [Formula: see text]-method for the pressure equations. After the presentation of some verification numerical tests, we perform a convergence analysis using an agglomerated mesh of a geometry of a brain slice. Finally, we present a simulation in a three-dimensional patient-specific brain reconstructed from magnetic resonance images. The model presented in this paper can be regarded as a preliminary attempt to model the perfusion in the brain.
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