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Abstract Investigating the length scales of granules could help understand the dynamics of granules in the photosphere. In this work, we detected and identified granules in an active region near disk center observed at wavelength of TiO (7057 Å) by the 1.6 m Goode Solar Telescope (GST). By a detailed analysis of the size distribution and flatness of granules, we found a critical size that divides the granules in motions into two regimes: convection and turbulence. The length scales of granules with sizes larger than 600 km follow Gauss function and demonstrate “flat” in flatness, which reveal that these granules are dominated by convection. Those with sizes smaller than 600 km follow power-law function and behave power-law tendency in flatness, which indicate that the small granules are dominated by turbulence. Hence, for the granules in active regions, they are originally convective in large length scale, and directly become turbulent once their sizes turn to small, likely below the critical size of 600 km. Comparing with the granules in quiet regions, they evolve with the absence of the mixing motions of convection and turbulence. Such a difference is probably caused by the interaction between fluid motions and strong magnetic fields in active regions. The strong magnetic fields make high magnetic pressure which creates pressure walls and slows down the evolution of convective granules. Such walls cause convective granules extending to smaller sizes on one hand, and cause wide intergranular lanes on the other hand. The small granules isolated in such wide intergranular lanes are continually sheared, rotated by strong downflows in surroundings and hereby become turbulent. 

Abstract Seventy percent of global electricity is generated by steam-cycle power plants. A hydrophobic condenser surface within these plants could boost overall cycle efficiency by 2%. In 2022, this enhancement equates to an additional electrical power generation of 1000 TWh annually, or 83% of the global solar electricity production. Furthermore, this efficiency increase reduces CO₂emissions by 460 million tons /year with a decreased use of 2 trillion gallons of cooling water per year. However, the main challenge with hydrophobic surfaces is their poor durability. Here, we show that solid microscale-thick fluorinated diamond-like carbon (F-DLC) possesses mechanical and thermal properties that ensure durability in moist, abrasive, and thermally harsh conditions. The F-DLC coating achieves this without relying on atmospheric interactions, infused lubricants, self-healing strategies, or sacrificial surface designs. Through tailored substrate adhesion and multilayer deposition, we develop a pinhole-free F-DLC coating with low surface energy and comparable Young’s modulus to metals. In a three-year steam condensation experiment, the F-DLC coating maintains hydrophobicity, resulting in sustained and improved dropwise condensation on multiple metallic substrates. Our findings provide a promising solution to hydrophobic material fragility and can enhance the sustainability of renewable and non-renewable energy sources. 

We report herein that dendron-shaped macromolecules AB n crystallize into well-ordered pyramid-like structures from mixed solvents, instead of spherical motifs with curved structures, as found in the bulk. The design of the asymmetric molecular architecture and the choice of mixed solvents are applied as strategies to manipulate the crystallization process. In mixed solvents, the solvent selection for the Janus macromolecule and the existence of dominant crystalline clusters contribute to the formation of flat nanosheets. Whereas during solvent evaporation, the bulkiness of the asymmetric macromolecules easily creates defects within 2D nanosheets which lead to their spiral growth through screw dislocation. The size of the nanosheets and the growth into 2D nanosheets or 3D pyramidal structures can be regulated by the solvent ratio and solvent compositions. Moreover, macromolecules of higher asymmetry generate polycrystals of lower orderliness, probably due to higher localized stress. 

Abstract Qudit entanglement is an indispensable resource for quantum information processing since increasing dimensionality provides a pathway to higher capacity and increased noise resilience in quantum communications, and cluster-state quantum computations. In continuous-variable time–frequency entanglement, encoding multiple qubits per photon is only limited by the frequency correlation bandwidth and detection timing jitter. Here, we focus on the discrete-variable time–frequency entanglement in a biphoton frequency comb (BFC), generating by filtering the signal and idler outputs with a fiber Fabry–Pérot cavity with 45.32 GHz free-spectral range (FSR) and 1.56 GHz full-width-at-half-maximum (FWHM) from a continuous-wave (cw)-pumped type-II spontaneous parametric downconverter (SPDC). We generate a BFC whose time-binned/frequency-binned Hilbert space dimensionality is at least 324, based on the assumption of a pure state. Such BFC’s dimensionality doubles up to 648, after combining with its post-selected polarization entanglement, indicating a potential 6.28 bits/photon classical-information capacity. The BFC exhibits recurring Hong–Ou–Mandel (HOM) dips over 61 time bins with a maximum visibility of 98.4% without correction for accidental coincidences. In a post-selected measurement, it violates the Clauser–Horne–Shimony–Holt (CHSH) inequality for polarization entanglement by up to 18.5 standard deviations with anS-parameter of up to 2.771. It has Franson interference recurrences in 16 time bins with a maximum visibility of 96.1% without correction for accidental coincidences. From the zeroth- to the third-order Franson interference, we infer an entanglement of formation (E_of) up to 1.89 ± 0.03 ebits—where 2 ebits is the maximal entanglement for a 4 × 4 dimensional biphoton—as a lower bound on the 61 time-bin BFC’s high-dimensional entanglement. To further characterize time-binned/frequency-binned BFCs we obtain Schmidt mode decompositions of BFCs generated using cavities with 45.32, 15.15, and 5.03 GHz FSRs. These decompositions confirm the time–frequency scaling from Fourier-transform duality. Moreover, we present the theory of conjugate Franson interferometry—because it is characterized by the state’s joint-temporal intensity (JTI)—which can further help to distinguish between pure-state BFC and mixed state entangled frequency pairs, although the experimental implementation is challenging and not yet available. In summary, our BFC serves as a platform for high-dimensional quantum information processing and high-dimensional quantum key distribution (QKD). 

Abstract Condensation is ubiquitous in nature and industry. Heterogeneous condensation on surfaces is typified by the continuous cycle of droplet nucleation, growth, and departure. Central to the mechanistic understanding of the thermofluidic processes governing condensation is the rapid and high‐fidelity extraction of interpretable physical descriptors from the highly transient droplet population. However, extracting quantifiable measures out of dynamic objects with conventional imaging technologies poses a challenge to researchers. Here, an intelligent vision‐based framework is demonstrated that unites classical thermofluidic imaging techniques with deep learning to fundamentally address this challenge. The deep learning framework can autonomously harness physical descriptors and quantify thermal performance at extreme spatio‐temporal resolutions of 300 nm and 200 ms, respectively. The data‐centric analysis conclusively shows that contrary to classical understanding, the overall condensation performance is governed by a key tradeoff between heat transfer rate per individual droplet and droplet population density. The vision‐based approach presents a powerful tool for the study of not only phase‐change processes but also any nucleation‐based process within and beyond the thermal science community through the harnessing of big data. 

Abstract Self‐assembled nanostructures of rod‐like molecules are commonly limited to nematic or layered smectic structures dominated by the parallel arrangement of the rod‐like components. Distinct self‐assembly behavior of four categories of dendritic rods constructed by placing a tri(hydroxy) group at the apex of dendritic oligo‐fluorenes is observed. Designed hydrogen bonding and dendritic architecture break the parallel arrangement of the rods, resulting in molecules with specific (fan‐like or cone‐like) shapes. While the fan‐shaped molecules tend to form hexagonal packing cylindrical phases, the cone‐shaped molecules could form spherical motifs to pack into various ordered structures, including the Frank–Kasper A15 phase and dodecagonal quasicrystal. This study provides a model system to engineer diverse supramolecular structures by rod‐like molecules and sheds new light into the mechanisms of the formation of unconventional spherical packing structures in soft matter.