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The production of hydrogen using electrical energy and thermal energy generated from renewable energy in combination with solid oxide electrolysis cells (SOECs) is one of the most important ways to reduce the cost of hydrogen production. However, fluctuations can lead to damage to SOEC. Therefore, this study develops a dynamic SOEC model to systematically analyze the transient behavior of solid oxide electrolysis cells under various operating conditions. the synergistic variation of electrical energy and thermal energy. This study finds that the synergistic variation of electrical and thermal energy does not significantly reduce the safety of SOEC compared to electrical or thermal energy inputs. In addition, this study optimizes the operating conditions of the SOEC. Comparative analysis reveals an 83.5% reduction in maximum temperature gradient for co-flow operation relative to counter-flow configuration, and the co-flow mode reaches the steady state more rapidly.

Classical thermal theory of steady counterflow flame propagation is extended by allowing for the finite-rate of the heat release in the flame edge and by introducing the dependence of the flame edge temperature on the opposed flow velocity. The experimentally observed non-monotonic dependence of the flame spread rate on the velocity of opposed gas flow is replicated. The relation between the flame edge temperature and the opposed gas velocity is derived. Similar to the experimental observations, the proposed approach predicts existence of two extinction limits corresponding to flame quenching at low velocities (either due to insufficient reactant diffusion rate or excessive radiative losses) and blow-off at high velocities (due to the insufficient residence time).

A moiré pattern is formed when two copies of a periodic pattern are overlaid with a relative twist. We address the electronic structure of a twisted two-layer graphene system, showing that in its continuum Dirac model the moiré pattern periodicity leads to moiré Bloch bands. The two layers become more strongly coupled and the Dirac velocity crosses zero several times as the twist angle is reduced. For a discrete set of magic angles the velocity vanishes, the lowest moiré band flattens, and the Dirac-point density-of-states and the counterflow conductivity are strongly enhanced.

Acoustic tweezers can control target movement through the momentum interaction between an acoustic wave and an object. This technology has advantages over optical tweezers for in-vivo cell manipulation due to its high tissue penetrability and strong acoustic radiation force. However, normal cells are difficult to acoustically manipulate because of their small size and the similarity between their acoustic impedance and that of the medium. In this study, we use the heterologous expression of gene clusters to generate genetically engineered bacteria that can produce numerous sub-micron gas vesicles in the bacterial cytoplasm. We show that the presence of the gas vesicles significantly enhances the acoustic sensitivity of the engineering bacteria, which can be manipulated by ultrasound. We find that by employing phased-array-based acoustic tweezers, the engineering bacteria can be trapped into clusters and manipulated in vitro and in vivo via electronically steered acoustic beams, enabling the counter flow or on-demand flow of these bacteria in the vasculature of live mice. Furthermore, we demonstrate that the aggregation efficiency of engineering bacteria in a tumour is improved by utilizing this technology. This study provides a platform for the in-vivo manipulation of live cells, which will promote the progress of cell-based biomedical applications. In vivo manipulation of cells has applications in cell-based therapy, tissue engineering and targeted drug delivery. Here the authors demonstrate in vivo programmable acoustic manipulation of genetically engineered bacteria using holographic acoustic tweezers.

Strongly interacting bosons have been predicted to display a transition into a superfluid ground state, similar to Bose–Einstein condensation. This effect is now observed in a double bilayer graphene structure, with excitons as the bosonic particles. A spatially indirect exciton is created when an electron and a hole, confined to separate layers of a double quantum well system, bind to form a composite boson 1 , 2 . Such excitons are long-lived, and in the limit of strong interactions are predicted to undergo a Bose–Einstein condensate-like phase transition into a superfluid ground state 1 , 2 , 3 . Here, we report evidence of an exciton condensate in the quantum Hall effect regime of double-layer structures of bilayer graphene. Interlayer correlation is identified by quantized Hall drag at matched layer densities, and the dissipationless nature of the phase is confirmed in the counterflow geometry 4 , 5 . A selection rule for the condensate phase is observed involving both the orbital and valley indices of bilayer graphene. Our results establish double bilayer graphene as an ideal system for studying the rich phase diagram of strongly interacting bosonic particles in the solid state.

We develop a theory of strong anisotropy of the energy spectra in the thermally driven turbulent counterflow of superfluid 4 He. The key ingredients of the theory are the three-dimensional differential closure for the vector of the energy flux and the anisotropy of the mutual friction force. We suggest an approximate analytic solution of the resulting energy-rate equation, which is fully supported by our numerical solution. The two-dimensional energy spectrum is strongly confined in the direction of the counterflow velocity. In agreement with the experiments, the energy spectra in the direction orthogonal to the counterflow exhibit two scaling ranges: a near-classical non-universal cascade dominated range and a universal critical regime at large wavenumbers. The theory predicts the dependence of various details of the spectra and the transition to the universal critical regime on the flow parameters. This article is part of the theme issue ‘Scaling the turbulence edifice (part 2)’.

The term quantum turbulence denotes the turbulent motion of quantum fluids, systems such as superfluid helium and atomic Bose—Einstein condensates, which are characterized by quantized vorticity, superfluidity, and, at finite temperatures, two-fluid behavior. This article introduces their basic properties, describes types and regimes of turbulence that have been observed, and highlights similarities and differences between quantum turbulence and classical turbulence in ordinary fluids. Our aim is also to link together the articles of this special issue and to provide a perspective of the future development of a subject that contains aspects of fluid mechanics, atomic physics, condensed matter, and low-temperature physics.

Given a simply connected planar domain D, distinct points x, y ∈ ∂D, and κ > 0, the Schramm–Loewner evolution SLEκ is a random continuous non-self-crossing path in D̅ from x to y. The SLEκ(ρ1; ρ2) processes, defined for ρ1, ρ2 > −2, are in some sense the most natural generalizations of SLEκ. When κ ≤ 4, we prove that the law of the time-reversal of an SLEκ(ρ1; ρ2) from x to y is, up to parameterization, an SLEκ(ρ2; ρ1) from y to x. This assumes that the \"force points\" used to define SLEκ(ρ1; ρ2) are immediately to the left and right of the SLE seed. A generalization to arbitrary (and arbitrarily many) force points applies whenever the path does not (or is conditioned not to) hit ∂D \\ {x, y}. The proof of time-reversal symmetry makes use of the interpretation of SLEκ(ρ1; ρ2) as a ray of a random geometry associated to the Gaussian-free field. Within this framework, the time-reversal result allows us to couple two instances of the Gaussian-free field (with different boundary conditions) so that their difference is almost surely constant on either side of the path. In a fairly general sense, adding appropriate constants to the two sides of a ray reverses its orientation.

The reciprocal energy and enstrophy transfers between normal fluid and superfluid components dictate the overall dynamics of superfluid 4 He including the generation, evolution and coupling of coherent structures, the distribution of energy among lengthscales, and the decay of turbulence. To better understand the essential ingredients of this interaction, we employ a numerical two-way model which self-consistently accounts for the back-reaction of the superfluid vortex lines onto the normal fluid. Here we focus on a prototypical laminar (non-turbulent) vortex configuration which is simple enough to clearly relate the geometry of the vortex line to energy injection and dissipation to/from the normal fluid: a Kelvin wave excitation on two vortex anti-vortex pairs evolving in (a) an initially quiescent normal fluid, and (b) an imposed counterflow. In (a), the superfluid injects energy and vorticity in the normal fluid. In (b), the superfluid gains energy from the normal fluid via the Donnelly–Glaberson instability.

The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose a Triple-Scale Multi-Stage InceptionNeXt (TSMS-InceptionNeXt) combustion state recognition method based on feature extraction optimization. First, to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images, we introduce Triplet Attention, which applies attention to the width, height, and Red Green Blue (RGB) dimensions of the flame images to enhance its ability to model dynamic features. Secondly, to address the issue of key information loss in the Inception deep convolution layers, we propose a Similarity-based Feature Concentration (SimC) mechanism to enhance the model’s capability to concentrate on critical features. Next, to address the insufficient receptive field of the model, we propose a Multi-Scale Dilated Channel Parallel Integration (MDCPI) mechanism to enhance the model’s ability to extract multi-scale contextual information. Finally, to address the issue of the model’s Multi-Layer Perceptron Head (MlpHead)neglecting channel interactions, we propose a Channel Shuffle-Guided Channel-Spatial Attention (ShuffleCS) mechanism, which integrates information from different channels to further enhance the representational power of the input features. To validate the effectiveness of the method, experiments are conducted on the counterflow burner flame visible light image dataset. The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71% on the dataset, improving by 2.38% over the baseline model and outperforming the baseline model’s performance. It achieved accuracy improvements of 10.47%, 4.76%, 11.19%, and 9.28% compared to the Reparameterized Visual Geometry Group (RepVGG), Squeeze-erunhanced Axial Transoformer (SeaFormer), Simplified Graph Transformers (SGFormer), and VanillaNet models, respectively, effectively enhancing the recognition performance for combustion states in counterflow burners.