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This study investigated combustion characteristics of composite fuel grains designed based on a modular fuel unit strategy. The modular fuel unit comprised a periodical helical structure with nine acrylonitrile–butadiene–styrene helical blades. A paraffin-based fuel was embedded between adjacent blades. Two modifications of the helical structure framework were researched. One mirrored the helical blades, and the other periodically extended the helical blades by perforation. A laboratory-scale hybrid rocket engine was used to investigate combustion characteristics of the fuel grains at an oxygen mass flux of 2.1–6.0 g/(s·cm2). Compared with the composite fuel grain with periodically extended helical blades, the modified composite fuel grains exhibited higher regression rates and a faster rise of regression rates as the oxygen mass flux increased. At an oxygen mass flux of 6.0 g/(s·cm2), the regression rate of the composite fuel grains with perforation and mirrored helical blades increased by 8.0% and 14.1%, respectively. The oxygen-to-fuel distribution of the composite fuel grain with mirrored helical blades was more concentrated, and its combustion efficiency was stable. Flame structure characteristics in the combustion chamber were visualized using a radiation imaging technique. A rapid increase in flame thickness of the composite fuel grains based on the modular unit was observed, which was consistent with their high regression rates. A simplified numerical simulation was carried out to elucidate the mechanism of the modified modular units on performance enhancement of the composite hybrid rocket grains.

Recent advancements in membrane-assisted seawater electrolysis powered by renewable energy offer a sustainable path to green hydrogen production. However, its large-scale implementation faces challenges due to slow power-to-hydrogen (P2H) conversion rates. Here we report a modular forward osmosis-water splitting (FOWS) system that integrates a thin-film composite FO membrane for water extraction with alkaline water electrolysis (AWE), denoted as FOWS AWE . This system generates high-purity hydrogen directly from wastewater at a rate of 448 Nm 3  day −1  m − 2 of membrane area, over 14 times faster than the state-of-the-art practice, with specific energy consumption as low as 3.96 kWh Nm −3 . The rapid hydrogen production rate results from the utilisation of 1 M potassium hydroxide as a draw solution to extract water from wastewater, and as the electrolyte of AWE to split water and produce hydrogen. The current system enables this through the use of a potassium hydroxide-tolerant and hydrophilic FO membrane. The established water-hydrogen balance model can be applied to design modular FO and AWE units to meet demands at various scales, from households to cities, and from different water sources. The FOWS AWE system is a sustainable and an economical approach for producing hydrogen at a record-high rate directly from wastewater, marking a significant leap in P2H practice. Green hydrogen production faces increased water risks due to scarce supplies of water. Here, authors develop a modular forward osmosis-water splitting system that utilises wastewater effluent to generate high-purity hydrogen, providing a sustainable solution for water and energy security.

The recent development of modular origami structures has ushered in an era for active metamaterials with multiple degrees of freedom (multi-DOF). Notably, no systematic inverse design approach for 3D curvilinear modular origami structures has been reported. Moreover, very few modular origami topologies have been studied to design active metamaterials with multi-DOF. Herein, we develop an inverse design method for constructing 3D reconfigurable architected structures — we synthesize modular origami structures whose unit cells can be volumetrically mapped into a prescribed 3D curvilinear shape followed by volumetric shrinkage to construct modules. After modification of the tubular geometry, we search through all the possible geometric and topological combinations of the modular origami structures to attain the target mobility using a topological reconstruction of modules. Our inverse design using geometric and topological reconstructions can provide an effective solution to construct 3D curvilinear reconfigurable structures with multi-DOF. Our work opens a path toward 3D reconfigurable systems based on volumetric inverse design, such as 3D active metamaterials and 3D morphing devices for automotive, aerospace, and biomedical engineering applications. Systematic inverse design for 3D curvilinear modular origami structures has not yet been reported. Here, the authors develop an inverse design method for 3D reconfigurable architected structures by geometric and topological reconstruction.

Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses revealed learned computational strategies mirroring the modular subtask structure of the training task set. Dynamical motifs, which are recurring patterns of neural activity that implement specific computations through dynamics, such as attractors, decision boundaries and rotations, were reused across tasks. For example, tasks requiring memory of a continuous circular variable repurposed the same ring attractor. We showed that dynamical motifs were implemented by clusters of units when the unit activation function was restricted to be positive. Cluster lesions caused modular performance deficits. Motifs were reconfigured for fast transfer learning after an initial phase of learning. This work establishes dynamical motifs as a fundamental unit of compositional computation, intermediate between neuron and network. As whole-brain studies simultaneously record activity from multiple specialized systems, the dynamical motif framework will guide questions about specialization and generalization. The authors identify reusable ‘dynamical motifs’ in artificial neural networks. These motifs enable flexible recombination of previously learned capabilities, promoting modular, compositional computation and rapid transfer learning. This discovery sheds light on the fundamental building blocks of intelligent behavior.

Chemists charged with manufacturing pharmaceuticals have recently been exploring the efficiency advantages of continuous flow techniques. Perera et al. now show that a flow apparatus can also accelerate reaction optimization earlier in the drug discovery process. They modified a high-performance liquid chromatography system to screen a wide variety of solvent, ligand, and base combinations to optimize carbon-carbon bond formation. Injecting stock solution aliquots of the catalyst and reactants into a carrier solvent stream let the authors vary the main solvent efficiently and scale up the optimal conditions for product isolation. Science , this issue p. 429 Chromatographic, flow-based screening of reaction conditions is demonstrated for Suzuki coupling in pharmaceutical research. The scarcity of complex intermediates in pharmaceutical research motivates the pursuit of reaction optimization protocols on submilligram scales. We report here the development of an automated flow-based synthesis platform, designed from commercially available components, that integrates both rapid nanomole-scale reaction screening and micromole-scale synthesis into a single modular unit. This system was validated by exploring a diverse range of reaction variables in a Suzuki-Miyaura coupling on nanomole scale at elevated temperatures, generating liquid chromatography–mass spectrometry data points for 5760 reactions at a rate of >1500 reactions per 24 hours. Through multiple injections of the same segment, the system directly produced micromole quantities of desired material. The optimal conditions were also replicated in traditional flow and batch mode at 50- to 200-milligram scale to provide good to excellent yields.

Modular construction refers to the use of factory prefabricated integrated module units. The modular steel construction unit SHS (Square Hollow Section) group column is a structure composed of four independent steel column units. Due to its compositional characteristics with voids, the fire resistance performance differs from ordinary steel columns, necessitating specific study. This paper employed a sequentially coupled thermal–mechanical analysis to investigate this. The effectiveness of the simulation model was first validated by comparing the simulated time–temperature curves and fire resistance limits with experimental results. A parametric analysis was then conducted to evaluate the influence of various factors, including the load ratio, cavity spacing, insulation type, gypsum board thickness, slenderness ratio, steel yield strength, and inner panel type, on the fire resistance limit. The results show that when the gypsum board thickness increased from 10 mm to 30 mm, the fire resistance limit correspondingly increased by 126%, 120%, 130%, and 130% for load ratios of 0.4, 0.5, 0.6, and 0.7, respectively. When the steel yield strength increased from 235 MPa to 690 MPa, the fire resistance limit increased by 20%, 21%, 24%, and 43% for load ratios ranging from 0.4 to 0.7. For inner panels of Glass Fiber, Rock Wool, Mineral Wool, and Plasterboard, the corresponding fire resistance limit ratios for load ratios of 0.4 to 0.7 were 1:1.13:1.24:1.45, 1:1.14:1.23:1.46, 1:1.11:1.2:1.42, and 1:1.08:1.18:1.41, respectively. It can be found that the best way to increase the fire resistance of the modular column is to increase the thickness of the gypsum board. A simplified calculation formula for the fire resistance limit of SHS group columns was derived through regression analysis, and recommendations for fire protection design were proposed, providing valuable insights for the future design and application of SHS group columns in steel modular construction.

Biological organisms achieve robust high-level behaviours by combining and coordinating stochastic low-level components 1 – 3 . By contrast, most current robotic systems comprise either monolithic mechanisms 4 , 5 or modular units with coordinated motions 6 , 7 . Such robots require explicit control of individual components to perform specific functions, and the failure of one component typically renders the entire robot inoperable. Here we demonstrate a robotic system whose overall behaviour can be successfully controlled by exploiting statistical mechanics phenomena. We achieve this by incorporating many loosely coupled ‘particles’, which are incapable of independent locomotion and do not possess individual identity or addressable position. In the proposed system, each particle is permitted to perform only uniform volumetric oscillations that are phase-modulated by a global signal. Despite the stochastic motion of the robot and lack of direct control of its individual components, we demonstrate physical robots composed of up to two dozen particles and simulated robots with up to 100,000 particles capable of robust locomotion, object transport and phototaxis (movement towards a light stimulus). Locomotion is maintained even when 20 per cent of the particles malfunction. These findings indicate that stochastic systems may offer an alternative approach to more complex and exacting robots via large-scale robust amorphous robotic systems that exhibit deterministic behaviour. A stochastic robotic system shows deterministic behaviour—such as locomotion, object transport and phototaxis—from the collective motion of many loosely coupled disk-shaped ‘particles’ that perform only volumetric oscillations.

Shape-morphing systems, which can perform complex tasks through morphological transformations, are of great interest for future applications in minimally invasive medicine 1 , 2 , soft robotics 3 – 6 , active metamaterials 7 and smart surfaces 8 . With current fabrication methods, shape-morphing configurations have been embedded into structural design by, for example, spatial distribution of heterogeneous materials 9 – 14 , which cannot be altered once fabricated. The systems are therefore restricted to a single type of transformation that is predetermined by their geometry. Here we develop a strategy to encode multiple shape-morphing instructions into a micromachine by programming the magnetic configurations of arrays of single-domain nanomagnets on connected panels. This programming is achieved by applying a specific sequence of magnetic fields to nanomagnets with suitably tailored switching fields, and results in specific shape transformations of the customized micromachines under an applied magnetic field. Using this concept, we have built an assembly of modular units that can be programmed to morph into letters of the alphabet, and we have constructed a microscale ‘bird’ capable of complex behaviours, including ‘flapping’, ‘hovering’, ‘turning’ and ‘side-slipping’. This establishes a route for the creation of future intelligent microsystems that are reconfigurable and reprogrammable in situ, and that can therefore adapt to complex situations. A micromachine less than 100 micrometres across, made of arrays of nanomagnets on hinged panels, is encoded with multiple shape transformations  and actuated with a magnetic field.

Information processing typically occurs via the composition of modular units, such as the universal logic gates found in discrete computation circuits. The benefit of modular information processing, in contrast to globally integrated information processing, is that complex computations are more easily and flexibly implemented via a series of simpler, localized information processing operations that only control and change local degrees of freedom. We show that, despite these benefits, there are unavoidable thermodynamic costs to modularity—costs that arise directly from the operation of localized processing and that go beyond Landauer’s bound on the work required to erase information. Localized operations are unable to leverage global correlations, which are a thermodynamic fuel. We quantify the minimum irretrievable dissipation of modular computations in terms of the difference between the change in global nonequilibrium free energy, which captures these global correlations, and the local (marginal) change in nonequilibrium free energy, which bounds modular work production. This modularity dissipation is proportional to the amount of additional work required to perform a computational task modularly, measuring a structural energy cost. It determines the thermodynamic efficiency of different modular implementations of the same computation, and so it has immediate consequences for the architecture of physically embedded transducers, known as information ratchets. Constructively, we show how to circumvent modularity dissipation by designing internal ratchet states that capture the information reservoir’s global correlations and patterns. Thus, there are routes to thermodynamic efficiency that circumvent globally integrated protocols and instead reduce modularity dissipation to optimize the architecture of computations composed of a series of localized operations.

To address the future demand for on-orbit modular construction of large space telescopes, this paper proposes a cooperative assembly method for modular sub-mirror units based on visual measurement and trajectory optimization. A modular sub-mirror assembly system is established, incorporating an AprilTag-assisted pose estimation framework. On this basis, a 3-5-3 polynomial trajectory planning approach and an improved Dung Beetle Optimizer (DBO) are introduced to achieve multi-objective optimization of the assembly path. Experimental validation is conducted on a dual-arm UR robotic platform, demonstrating the effectiveness of the proposed method in terms of assembly accuracy, execution stability, and trajectory tracking performance. The results indicate that the proposed approach satisfies the requirements for efficient, robust, and compliant assembly, and exhibits strong feasibility and potential for practical application in space-based modular optical systems.