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This page aims to highlight the most interesting papers published in Nature Communications in the interdisciplinary areas where diverse approaches at the boundaries of physics, mathematics, materials science and engineering take place to create new research opportunities.
So far, attempts to prevent droplet rebound rely on augmenting energy dissipation. Here, the authors present that the rebound of hollow droplets is suppressed even on super-repellent surfaces, reminiscent of zero-surface-tension liquid droplets.
Current droplet manipulation techniques have limitations such as applying to a large scale of volume or of on-demand droplet release. Here using a magnetic actuated Janus origami robot, Jiang et al. present a strategy to achieve omni-manipulation of micro and nanoliter droplets.
To date the performance of molecular electronics compared to silicon limits their applications. Yang et al. develop the first mechano-optoelectronic switch based on mechanically controlled aggregation-induced emission of the self-assembled molecules, which can be reversibly switched at high speed.
Collective motion arises from the coordination of individuals and entails the adjustment of their respective velocities. Yet, how individuals achieve this coordination is often not understood. For migrating cells and motorized agents, Riedl et al. show that the synchronization of the intrinsic oscillator through nearest neighbour coupling establishes the necessary feedback leading to a uniform speed within the collective.
Optical properties of organic semiconductors enable various optoelectronic applications. Müller et al. report a large exciton bandwidth in a crystalline organic material and attribute it to the strong Coulomb interaction in directed exciton pathways induced by the donor–acceptor type molecular structure.
Fano varieties are mathematical shapes that are basic units in geometry, they are challenging to classify in high dimensions. The authors introduce a machine learning approach that picks out geometric structure from complex mathematical data where rigorous analytical methods are lacking.
Transfer learning can be applied in computer vision and natural language processing to utilize knowledge from a source task to improve performance on a target task. The authors propose a framework for transfer learning with kernel methods for improved image classification and virtual drug screening.
Inspired by human analogical reasoning in cognitive science, the authors propose an approach combining deep learning systems with an analogical reasoning mechanism, to detect abstract similarity in real-world images without intensive training in reasoning tasks.
Personal communication networks through mobile phones and online platforms can be characterized by patterns of tie strengths. The authors propose a model to explain driving mechanisms of emerging tie strength heterogeneity in social networks, observing similarity of patterns across various datasets.
Hydrodynamics and correlated motion of colloids in the near-field interactions are not fully understood. The authors report the motion of particles in particle-pairs is direction-dependent in the near-field and that the Stokes-Einstein relation is not applicable in this case.
The slightest deformation in the colloidal gels made by smooth particles causes them to transition from a solid to a liquid state. The authors develop a surface grafting technique using click-like chemistry to functionalize particles and show that the rough particle gel is much tougher.
Understanding glass transition would rely on the knowledge of the structural ordering upon slow cooling in the absence of crystallization or phase separation. The authors identify exotic compositional order, not accompanied by any thermodynamic signature, directly impacts the structural relaxation dynamics.
The coalescence of nuclei plays an important role in phase transitions, but it remains challenging to monitor the process in real time. Here, Peng et al. image the coalescence of two post-critical nuclei in the crystal-crystal transition with single-particle resolution using a colloidal system.
Conservation laws are crucial for analyzing and modeling nonlinear dynamical systems; however, identification of conserved quantities is often quite challenging. The authors propose here a geometric approach to discovering conservation laws directly from trajectory data that does not require an explicit dynamical model of the system or detailed time information.
The photophysics of 2D layered Ruddlesden-Popper perovskites is still lively debated. Here, authors address the exciton stability of perovskites in form of film and single crystal by resonant injection of cold excitons and probe the exciton dissociation with femtosecond differential transmission.
Droplets and sharp interfaces at supercritical pressures are interpreted as evidence of surface tension due to phase equilibria in mixtures, given the lack of a supercritical liquid-vapor phase equilibrium in pure fluids. Authors show from first principles and simulations that, unlike in gases or liquids, stable droplets, bubbles, and planar interfaces can exist without surface tension.
The formation of soliton macromolecules or metamaterial analogues of polymers with inter-soliton binding resembling strong covalent-like chemical bonds has not been considered so far. Zhao et al. experimentally create and theoretically, model soliton macromolecules, called “polyskyrmionomers”, introducing polymer-mimicking designs of topological chiral meta matter that promise technological utility in data storage and electro-optics.
Soft grippers can emulate human hands, but it remains challenging to achieve multiple capability in manipulating various objects in one design. Hong et al. utilize a kirigami gripper with controllable and programmable trajectories to manipulate objects spanning from ultra-soft to ultra-strong with high precision.
Sliding of drops over solid surface is a common phenomenon, but it remains impossible to predict the sliding velocity due to numerous dissipation channels causing drop friction. Li et al. show that dynamic wetting is determined by a dimensionless friction coefficient, which is a material parameter.
Shape morphing surfaces demonstrate a wide variety of applications, yet the existing technologies lack high-fidelity, high-speed deformation and embedded state sensing. Johnson et al. integrate soft actuators and soft sensors for high-fidelity shape morphing with self-sensing and high-speed actuation.
The mechanical forces exerted by active fluids may provide an effective way of transporting microscopic objects, but the details remain elusive. Using space modulated activity, Pellicciotta et al. generate active pressure gradients capable of transporting passive particles in controlled directions.
Hydrodynamically coupled rotors can be used to describe interactions ranging from molecular machines to atmospheric dynamics. Modin et al. show that optically-driven rotors in a non-tweezing beam can freely diffuse while spinning asynchronously and develop an analytical hydrodynamic model to explain.
Rare quantum tunneling two-level systems are known to govern the glass physics at low temperatures, but it remains challenging to detect them in simulations. Ciarella et al. show a machine learning approach to efficiently identify the structural defects, allowing to predict the quantum splitting.
In biology, individuals are known to achieve higher navigation accuracy when moving in a group compared to single animals. The authors show that simple self-propelled robotic modules that are incapable of accurate motion as individuals can achieve accurate group navigation once coupled via deformable elastic links.
Turbulent pair dispersion is relevant for mixing processes such as microplastics transport in the ocean or dynamics of water droplets in clouds. The authors present a geometrical framework and empirical evidence that elucidate the universality of the process across scales, while forming a bridge with the classical Richardson theory.
To ensure the privacy of processed data, federated learning approaches involve local differential privacy techniques which however require communicating a large amount of data that needs protection. The authors propose here a framework that uses selected small data to transfer knowledge in federated learning with privacy guarantees.
The Debye interaction is defined as the attraction between a polar molecule and a nonpolar molecule, which governs many self-assembling processes in materials. Here, Lee et al. design a like-charged colloidal model at the water-oil interface to characterize the Debye interaction for the first time.
A common approach to design single-molecule switch is to use molecular backbones in response to external stimulus, but often requires complex organic synthesis. Here, Tong et al. show how to in situ control of the molecule-electrode contact using electrochemical gating to realize a reversible switch.
Liquid metals are widely used in flexible electronics and soft robotics applications, but their adhesion to underlying solid substrates is unwanted. Dai et al. show that liquid metal droplets can overcome adhesion forces and bounce off from the surface covered with a water film with sufficient thickness.
Increase of friction between two solid surfaces in stationary contact over time, known as frictional aging, has been widely observed. Farain and Bonn show that, regardless of surface roughness or degree of compression, the normalized stress relaxation of microcontacts is the same as that of bulk material.
Isotropization temperature determines the temperature at which Liquid Crystals Elastomer (LCE) material actuates. Here, the authors give a general strategy based on dynamic covalent bonds for tuning the isotropization temperature for LCEs.
Marangoni swimmers have high relative speed, considering the body length and absence of a mechanical system but the fabricating is complex. Song et al. transform simple pen strokes into dynamic, programmable robots that can ‘swim’ with striking versatility.
Ferroelectric Nematic Liquid Crystals (FNLCs) have potential in applications due to their unique combination of fluidity, spontaneous polarization, large dielectric permittivity, and second-order non-linear optical properties. Sebastián et al. show the patterning of electric polarization in FNLCs by photoalignment which exploit flexoelectric coupling between polarization and splay director deformations.
Insect-scale untethered micro aerial vehicles such as artificial dandelion devices suffer from high flight randomness and inadequate controllability. Chen et al. design and fabricate an untethered dandelion-inspired microflier, which is spatially and temporally controlled by an ultralight and supersensitive light-driven bimorph soft actuator.
Fingering patterns form spontaneously when a non-wetting viscous liquid displaces a dry granular mixture in a confined flow cell. The authors show how these patterns are controlled by the balance between viscous, capillary, and frictional forces.
A liquid drop hovers over the surface when placed on a hot surface that is hotter than the Leidenfrost point because a vapor film will form beneath the drop. Here authors report how to manipulate drops by cutting the Leidenfrost film using chemically heterogeneous surfaces.
The colloidal composites of gel and solid inclusions are more commonly encountered in real life. Using simulations, authors identify two lengthscales whose interplay generically controls the gelation in composite gels.
Early warning of the airflow separation and monitoring of the stall status is critical for the safety of flying aircraft. Here authors introduce a lightweight and conformable system on the wing surface of aircraft that can sense and warn the pre-stall.
In classical and quantum thermodynamics, a trade-off between speed, precision and cost is of relevance for problems in open quantum dynamics and various biomolecular processes. By employing bulk-boundary correspondence, the authors uncover connection between thermodynamic uncertainty relations and speed limit relations.
In the variety of biological and social networks, the validation of experimental data is done by comparing an overlap with reference networks. The authors introduce a positive statistical benchmark corresponding to the best possible overlap between two networks to threshold and validate new experimental datasets.
Better understanding of a trade-off between the speed and accuracy of decision-making is relevant for mapping biological intelligence to machines. The authors introduce a brain-inspired learning algorithm to uncover dependencies in individual fMRI networks with features of neural activity and predict inter-individual differences in decision-making.
Identifying topological defects in disordered materials has a profound effect on predicting when and where the material will break. Matteo Baggioli comments a recent publication in Nature Communications, which confirms the existence of defects in glasses and their crucial role for plasticity.
It remains challenging to understand the relation between mechanical properties of glasses close to the yielding point and plastic behaviors at microscales. Wu et al. examine the plasticity using topological properties of the vibrational modes and identify a correlation between defects and plastic events.
A challenge in making a flexible mold stamp using roll-to-roll nanoimprint lithography is to increase area while minimizing perceptible seams. Here, based on Fourier spectral analysis of moiré patterns resulting from superposed identical patterns, a method that enables the fabrication of scalable, quasi-seamless functional surfaces without the use of alignment marks is proposed.
Understanding of diffusive and spreading processes in networks remains challenging when dynamics of the network is complex. The authors propose a quantity to reflect the potential of a network node to diffuse information, that may serve to develop interventions for improved network efficiency.
The biological plausibility of backpropagation and its relationship with synaptic plasticity remain open questions. The authors propose a meta-learning approach to discover interpretable plasticity rules to train neural networks under biological constraints. The meta-learned rules boost the learning efficiency via bio-inspired synaptic plasticity.
Carbon nanotubes are promising candidates for transport of ions and charges, but the response of carbon nanotubes under osmotic forcings is not well explored. Here the authors report enhanced ion-specific osmotic transport in individual double-walled carbon nanotubes.
Boiling crisis is a physical phenomenon limiting the operation of many technologies cooled by boiling. Zhang et al. reveal theoretically and experimentally the existence of a unifying criterion to explain and predict the boiling crisis.
Ionic-junction devices are difficult to integrate with fiber-shaped tissues like nerves and muscles for applications in implantable bioelectronics due to their large size and bulk structure. Authors realize here easy to implant fiber-shaped iontronics through an integrated opposite charge grafting process, enabling the construction of ionic logic gates and artificial neural pathways.
The lattice strain induced by surface ligands not only stabilizes black phase at room temperature but also enables full-range A-site tuning. Here, authors construct a detailed picture of temperature dependent behaviour of perovskite quantum dots by in situ spectroscopic and structural measurements.
Small-scale manipulation of liquids is being controlled with bulky pumps or power sources. Dradrach et al. show a light-driven pumping of micro-liter liquid using a centimeter-long liquid crystal elastomer strip.
The emission maximum of phosphorescence is normally larger than that of fluorescence. Here, authors report up-converted room-temperature phosphorescence materials that emit from higher-lying triplet states with 3n-π* characters and large phosphorescence decay rate constants, violating Kasha’s rule.
Supercritical fluids have local density inhomogeneities caused by molecular clusters. Authors show that the molecular interactions of supercritical fluids, associated with localized clusters, obey complex network dynamics that can be represented by a hidden-variable network model.