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Regulation systems for fluid-driven soft robots predominantly consist of inflexible and bulky components. These rigid structures considerably limit the adaptability and mobility of these robots. Soft valves in various forms for fluidic actuators have been developed, primarily fluidically or electrically driven. However, fluidic soft valves require external pressure sources that limit robot locomotion. State-of-the-art electrostatic valves are unable to modulate pressure beyond 3.5 kPa with a sufficient flow rate (>6 mL⋅min −1 ). In this work, we present an electrically powered soft valve for hydraulic actuators with mesoscale channels based on a different class of ultrahigh-power density dynamic dielectric elastomer actuators. The dynamic dielectric elastomer actuators (DEAs) are actuated at 500 Hz or above. These DEAs generate 300% higher blocked force compared with the dynamic DEAs in previous works and their loaded power density reaches 290 W⋅kg −1 at operating conditions. The soft valves are developed with compact (7 mm tall) and lightweight (0.35 g) dynamic DEAs, and they allow effective control of up to 51 kPa of pressure and a 40 mL⋅min −1 flow rate with a response time less than 0.1 s. The valves can also tune flow rates based on their driving voltages. Using the DEA soft valves, we demonstrate control of hydraulic actuators of different volumes and achieve independent control of multiple actuators powered by a single pressure source. This compact and lightweight DEA valve is capable of unprecedented electrical control of hydraulic actuators, showing the potential for future onboard motion control of soft fluid-driven robots. 

The Arctic is warming at almost four times the global rate. Cooling caused by anthropogenic aerosols has been estimated to offset sixty percent of greenhouse-gas-induced Arctic warming, but the contribution of aerosols to radiative forcing (RF) represents the largest uncertainty in estimating total RF, largely due to unknown preindustrial aerosol abundance. Here, sulfur isotope measurements in a Greenland ice core show that passive volcanic degassing contributes up to 66 ± 10% of preindustrial ice core sulfate in years without major eruptions. A state-of-the-art model indicates passive volcanic sulfur emissions influencing the Arctic are underestimated by up to a factor of three, possibly because many volcanic inventories do not include hydrogen sulfide emissions. Higher preindustrial volcanic sulfur emissions reduce modeled anthropogenic Arctic aerosol cooling by up to a factor of two (+0.11 to +0.29 W m-2 (watts per square meter)), suggesting that underestimating passive volcanic sulfur emissions has significant implications for anthropogenic-induced Arctic climate change. These data include sulfur isotopes of sulfate measurements from a Greenland ice core and volcanic gas measurements (CO2:S (carbon dioxide:sulfur) ratios) from various volcanoes and hot springs in Iceland. 

Today’s use of large-scale industrial robots is enabling extraordinary achievement on the assembly line, but these robots remain isolated from the humans on the factory floor because they are very powerful, and thus dangerous to be around. In contrast, the soft robotics research community has proposed soft robots that are safe for human environments. The current state of the art enables the creation of small-scale soft robotic devices. In this article we address the gap between small-scale soft robots and the need for human-sized safe robots by introducing a new soft robotic module and multiple human-scale robot configurations based on this module. We tackle large-scale soft robots by presenting a modular and reconfigurable soft robotic platform that can be used to build fully functional and untethered meter-scale soft robots. These findings indicate that a new wave of human-scale soft robots can be an alternative to classic rigid-bodied robots in tasks and environments where humans and machines can work side by side with capabilities that include, but are not limited to, autonomous legged locomotion and grasping. 

Abstract. Mixed-phase Southern Ocean clouds are challenging to simulate, and theirrepresentation in climate models is an important control on climatesensitivity. In particular, the amount of supercooled water and frozen massthat they contain in the present climate is a predictor of their planetaryfeedback in a warming climate. The recent Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) vastly increased theamount of in situ data available from mixed-phase Southern Ocean clouds usefulfor model evaluation. Bulk measurements distinguishing liquid and ice watercontent are not available from SOCRATES, so single-particle phaseclassifications from the Two-Dimensional Stereo (2D-S) probe are invaluablefor quantifying mixed-phase cloud properties. Motivated by the presence oflarge biases in existing phase discrimination algorithms, we develop a noveltechnique for single-particle phase classification of binary 2D-S images usinga random forest algorithm, which we refer to as the University of WashingtonIce–Liquid Discriminator (UWILD). UWILD uses 14 parameters computed frombinary image data, as well as particle inter-arrival time, to predict phase.We use liquid-only and ice-dominated time periods within the SOCRATES datasetas training and testing data. This novel approach to model training avoidsmajor pitfalls associated with using manually labeled data, including reducedmodel generalizability and high labor costs. We find that UWILD is wellcalibrated and has an overall accuracy of 95 % compared to72 % and 79 % for two existing phase classificationalgorithms that we compare it with. UWILD improves classifications of smallice crystals and large liquid drops in particular and has more flexibilitythan the other algorithms to identify both liquid-dominated and ice-dominatedregions within the SOCRATES dataset. UWILD misclassifies a small percentageof large liquid drops as ice. Such misclassified particles are typicallyassociated with model confidence below 75 % and can easily befiltered out of the dataset. UWILD phase classifications show that particleswith area-equivalent diameter (Deq)  < 0.17 mm are mostlyliquid at all temperatures sampled, down to −40 ∘C. Largerparticles (Deq>0.17 mm) are predominantly frozen at alltemperatures below 0 ∘C. Between 0 and 5 ∘C,there are roughly equal numbers of frozen and liquid mid-sized particles (0.170.33 mm) are mostly frozen. We also use UWILD's phaseclassifications to estimate sub-1 Hz phase heterogeneity, and we showexamples of meter-scale cloud phase heterogeneity in the SOCRATES dataset. 

Pyrosomes are tunicates in the phylum Chordata, which also contains vertebrates. Their gigantic blooms play important ecological and biogeochemical roles in oceans.Pyrosoma, meaning “fire-body”, derives from their brilliant bioluminescence. The biochemistry of this light production is unknown, but has been hypothesized to be bacterial in origin. We found that mixing coelenterazine—a eukaryote-specific luciferin—withPyrosoma atlanticumhomogenate produced light. To identify the bioluminescent machinery, we sequencedP. atlanticumtranscriptomes and found a sequence match to a cnidarian luciferase (RLuc). We expressed this novel luciferase (PyroLuc) and, combined with coelenterazine, it produced light. A similar gene was recently predicted from a bioluminescent brittle star, indicating that RLuc-like luciferases may have evolved convergently from homologous dehalogenases across phyla (Cnidaria, Echinodermata, and Chordata). This report indicates that a widespread gene may be able to functionally converge, resulting in bioluminescence across animal phyla, and describes and characterizes the first putative chordate luciferase.

 

The change in planetary albedo due to aerosol−cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth’s climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol−cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm −3 and 24 cm −3 . By extension, the radiative forcing since 1850 from aerosol−cloud interactions is constrained to be −1.2 W⋅m −2 to −0.6 W⋅m −2 . The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol−cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.