The most striking claim in the study is also the easiest to dismiss at first glance: the droplets never actually touch the crease that stops them. Instead, they respond from a distance. When a soft elastic surface is compressed past a critical strain, it buckles into a crease—a sharp, downward fold in which the material folds against itself. Around this fold, the surface deforms and curves in a characteristic way. For a liquid droplet resting on that surface, the local curvature changes the energetic landscape of wetting. The crease effectively raises an energy barrier, an elastic-cum-capillary hill that the droplet must climb to continue its journey. Small droplets, which carry less gravitational and inertial momentum relative to their surface energy, slow down as they approach this barrier and stop short of it. Larger droplets simply plow over the fold and continue on their way. The result is a sharply defined size threshold, a gate that admits droplets above a cutoff and blocks everything below it.

That threshold is not fixed. Because the crease exists only while the surface is compressed, the amount of compression becomes a single mechanical dial that reprograms the gate on the fly. The researchers found that the effect is dramatically nonlinear: squeezing the surface just 15 percent harder quadruples the critical droplet size that can pass. In practical terms, an operator can tune the same physical fold to block droplets of one size in the morning and admit them in the afternoon, without ever patterning the surface or swapping out components. ‘What we did not expect is that drops of different sizes are not sensing the same thing,’ Pandey says. According to the team, large droplets respond primarily to how steep the fold is—the overall geometric sharpness of the deformation—while small droplets respond to how rapidly that steepness changes, a subtler gradient effect tied to the spatial variation of the surface curvature around the crease. This size-dependent asymmetry is what gives the crease its ability to discriminate between droplets with such precision.

The physics behind the phenomenon sits at the intersection of elasticity and capillarity, a field researchers call elastocapillarity. Soft materials deform easily under the pull of surface tension, and liquids in turn feel the geometry of the elastic substrate they rest on. A crease concentrates both effects into a tiny region: the fold itself is a self-contact singularity in the elastic body, and the surrounding deformation field extends outward over a distance set by the elastic modulus and the droplet’s own length scales. A droplet approaching the crease deforms the soft surface beneath it, and that deformation feeds back into the droplet’s motion. Near a crease, this coupled elastocapillary interaction becomes repulsive for droplets below the critical size—the paper’s authors describe the mechanism as elastocapillary repulsion—so the droplet decelerates as if it were rolling up an invisible incline. Nothing in the traditional wetting literature required the barrier to be felt remotely, which is why the distance-sensing behavior caught the team’s attention.

Once a single gate is understood, the natural question is what a network of gates can do. The researchers went well beyond the one-fold demonstration. By arranging creases in deliberate geometries on the compressed surface, they guided droplets along prescribed paths, sorted them by size, and even sorted them by surface tension, since the barrier that repels a droplet depends on its capillary characteristics as well as its dimensions. The creases can store what the team describes as a kind of droplet memory, in which the configuration of the surface keeps track of past inputs—a sequence of droplets leaves a mechanical record in how the gates have been used or the state of the flow. They also showed that a continuous stream of droplets arriving at a crease can be reshaped into fewer, larger pulses, a form of flow coalescence that could be valuable wherever fluid needs to be delivered in discrete, metered doses rather than a dribble of small drops.

The most ambitious demonstration pushes the analogy between droplet gates and electronic circuitry to its logical conclusion: computation. The team routed two separate streams of droplets into a single crease junction to build a half adder, the elementary arithmetic unit that combines two binary inputs into a sum and a carry. In an electronic processor, a half adder is built from transistors; here, it was built from nothing more than the mechanical state of a compressed elastic surface and the trajectories of liquid droplets. The logic is embodied in geometry: whether a droplet from one stream arrives at the junction and crosses, stops, or merges with a droplet from the other stream depends on the gate state, and the outputs can be read from the flow emerging on the far side. Because the creases appear and vanish with compression, the entire circuit is rewritable. ‘We can switch a gate off, let everything through, and switch it back on,’ Pandey explains. ‘Nothing is permanently patterned into the surface, and the control comes down to a single mechanical variable.’ That last point matters enormously for anyone who has tried to reconfigure a microfluidic chip: conventional devices encode their channels and valves in lithographically etched structures, so changing the routing means fabricating a new chip. A crease circuit changes its mind instantly.

The ability to manipulate droplets without batteries, motors, valves, or embedded circuitry is what gives the technique its practical appeal. Portable diagnostic devices—lateral-flow tests and the growing family of microfluidic assays designed to analyze tiny samples of blood, saliva, or other fluids—depend on moving, metering, and merging microliter volumes reliably and cheaply. A surface that sorts droplets by size or composition, merges them on demand, and holds a record of what has passed through could perform some of that fluid management passively, driven purely by the mechanics of the substrate. The same qualities suit the approach to water harvesting: systems that collect drinking water from fog or humid air rely on capturing, coalescing, and transporting droplets, and a creased surface that passively gates and reshapes droplet flow could improve how efficiently collected water is channeled and consolidated.

The research also carries implications for anyone working with soft materials, where creases are usually regarded as a nuisance. Compressible gels, elastomers, and biological tissues develop creases under load, and engineers typically design against them, treating the folds as failure modes or sources of unwanted friction and adhesion. This study reframes the fold as a functional element—a component rather than a defect. In that sense the work belongs to a broader movement in soft-matter engineering that treats mechanical instabilities as programmable resources: buckling plates that morph into target shapes, wrinkles that encode strain history, and now creases that compute with droplets. The underlying physics, elastocapillary repulsion mediated by substrate deformation, is likely to operate in any system where a liquid moves across a surface soft enough to respond to it, from lab-on-chip devices built from hydrogels to condensation on flexible coatings.

The study, titled ‘Creases gate and steer droplets via elastocapillary repulsion,’ was published in the Proceedings of the National Academy of Sciences on September 22, 2026. Alongside Pandey, the co-authors are Zixuan Wu, a postdoctoral researcher in Pandey’s group at Syracuse University; Gavin Linton, an undergraduate in mechanical and aerospace engineering; and Stefan Karpitschka, a professor of physics at the University of Konstanz in Germany. The collaboration brought together expertise in soft-matter mechanics at Syracuse and in capillary dynamics at Konstanz, a pairing well suited to a phenomenon that lives precisely at the boundary between the two disciplines.

For now, the demonstration remains at the level of laboratory surfaces and dyed droplets lining up along a fold, smaller drops halted before the crease while larger ones sail across. But the conceptual payoff is immediate. It establishes that a single mechanical variable—compression—can program a hierarchy of fluidic behaviors: gating by size, sorting by surface tension, path guidance, pulse formation, memory, and even arithmetic. In a field where adding functionality usually means adding components, a technology that achieves all of this by simply squeezing a sheet of rubber, and reverses it by letting go, offers an unusually elegant answer to the question of how small, cheap, and simple fluid control can become. The traffic lights of the microfluidic world, it turns out, may need nothing more than a wrinkle in the road.

Subject of Research: Elastocapillary gating of droplets by compression-induced creases in soft elastic surfaces

Article Title: New research shows how engineers turn tiny creases into droplet traffic controllers

Article References: New research shows how engineers turn tiny creases into droplet traffic controllers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: droplets, creases, soft matter, elastocapillarity, microfluidics, surface tension, elastic surfaces, droplet sorting, logic operations, water harvesting, diagnostics, Syracuse University

The central achievement of the work is reconfigurability. Non-Abelian braiding, in which the outcome of swapping two objects depends on the order in which the swaps are performed, has previously been demonstrated in superconducting processors, trapped-ion systems, photonic chips and acoustic metamaterials. Those platforms are powerful but require cryogenic temperatures, intricate nanofabrication, or fixed on-chip geometries. The new experiment brings the same mathematical structure into an ordinary liquid crystal cell sitting at ambient conditions, where the relevant objects are micron-scale defect lines entangled around colloidal particles, and where the control knob is simply light.

Disclination lines are the skeletons of disorder within an ordered medium. In a nematic liquid crystal, rod-like molecules align with a local direction called the director; a disclination line marks a seam where that alignment field cannot be smoothly defined, much like the seam on a tennis ball marks a place where the covering cannot lie flat. When colloidal particles are dispersed in the nematic, their surfaces impose orientation constraints on the surrounding director, and defect lines become entangled among the particles, forming stable, topologically protected structures. The team exploited this entanglement by photonically manipulating the colloids, using patterned light to reorient the director field at the cell surface and drive the particles through cooperative molecular reorientations.

By sweeping this optical control, the researchers wove disclination lines into chiral double-helix entanglements, structures in which pairs of defect lines wind around one another in either a left-handed or right-handed twist. The handedness of the helix is not merely decorative; it serves as a binary degree of freedom that the team calls a nematic bit, or nbit. Depending on whether the surface director is rotated counterclockwise or clockwise by the incident light, the entanglement settles into one or the other chiral state, effectively writing a bit into the topology of the defect network. Supplementary videos accompanying the paper show the process unfolding in real time, with double-helix entanglements forming around assemblies of up to nine colloids and even coexisting regions of opposite chirality within a single four-particle structure.

With this encoding in hand, the team implemented a complete set of braid operations, the elementary moves in which defect lines pass over and under one another, and demonstrated their non-commutativity in networks of three lines. In an Abelian world, performing operation A and then operation B yields the same final configuration as performing B and then A. In the nematic platform, the order matters: two braid sequences that differ only in their ordering leave the network in topologically distinct states. This order-dependence is the defining signature of non-Abelian behaviour and the property that makes braided anyons attractive as a basis for fault-tolerant information processing, because the encoded state depends on the global history of exchanges rather than on any local measurement.

A crucial advantage of the soft-matter setting is that the braid gates themselves can be moved. The colloidal particles act as physical gates that pin and route the disclination lines, and by repositioning these particles with optical tweezers and light-driven transport, the researchers reprogrammed the braiding sequence in situ without rebuilding the sample. Small variations in colloid position or local line curvature leave the topological state unchanged, a robustness the team verified directly by perturbing the networks and observing that the encoded configuration survived. The method also extends beyond three-line demonstrations to multiline architectures, suggesting a path toward larger braiding networks assembled from the same elementary components.

Perhaps the most forward-looking contribution is the establishment of an inverse-design framework. Rather than working forward from operations to outcomes, the team developed an algebraic procedure that runs in reverse: given a desired topological transformation, the framework compiles it into prescribed spatial routing of the defect lines together with layer-by-layer phase corrections. This compiler-like capability mirrors how electronic design automation tools translate logic descriptions into circuit layouts, and it transforms the platform from a demonstration apparatus into a programmable one. The researchers note that the scalability of light-driven manipulation makes this design loop practical, since the same optical interface that writes a single bit can in principle address many.

The significance of the result lies in its bridging role. Topological information processing has been dominated by quantum proposals, where non-Abelian anyons would protect quantum states against local noise. Realizing the same braiding algebra in a classical, room-temperature material does not produce a quantum computer, but it provides a tangible, inexpensive laboratory in which non-Abelian logic can be studied, visualized and engineered. The authors position the system as a programmable classical platform for robust topological transformations, one that connects the soft-matter physics of liquid crystals with the emerging field of topological information processing. Because liquid crystals are already the workhorse of display technology, there is also a plausible engineering pathway: the optical and electro-optical toolkits for addressing nematic cells at high resolution are mature and commercially available.

The work also builds on a rich lineage. Knot-theoretic descriptions of nematic defects date back more than a decade, when theorists showed that disclination networks carry invariants analogous to braids and that rewiring operations among them can be classified. Experimentalists later demonstrated reconfigurable knots and links in chiral nematic colloids, and recent theoretical work proposed nematic bits and universal logic gates based on defect topology. What the new study adds is dynamics and control: the ability to actively drive the system through braid operations on demand, to verify non-commutativity experimentally, and to reprogram the network at will. Earlier light-driven studies from the same group had shown collective transport and reconfigurable assembly of nematic colloids and active transformations of disclination networks, providing the technical foundation for the present braiding results.

Looking ahead, the researchers suggest that the platform could serve as a testing ground for algorithms and error models relevant to topological computing, while also raising its own questions about how complex braid networks relax, hold information and fail. The combination of a mathematical structure once confined to abstract group theory with something as tangible as soap-like molecules and glass beads, manipulated by nothing more exotic than patterned light, is a reminder that some of the deepest ideas in physics can be made to run, quite literally, at room temperature. For now, the nematic bits weave their helices quietly under a microscope, but they weave them on command, in any order the operator chooses, and undo nothing by accident.

Subject of Research: Light-driven reconfigurable non-Abelian braiding of disclination lines in nematic liquid crystals

Article Title: Reconfigurable non-Abelian braiding of nematic bits

Article References: Lei, Z., Zheng, X., Zhang, J., Tang, W., Tian, K., Song, G., Asilehan, Z., Chen, Z., Vergara, F., Guan, Y., Zhang, R., Jiang, J., & Peng, C. (2026). Reconfigurable non-Abelian braiding of nematic bits. Nature Materials. https://doi.org/10.1038/s41563-026-02728-x

Image Credits: AI Generated

DOI: 10.1038/s41563-026-02728-x

Keywords: non-Abelian braiding, nematic liquid crystals, topological defects, disclination lines, nematic bits, colloids, topological information processing, soft matter, inverse design, liquid crystal colloids, chiral double helix, optical control

The central challenge the team set out to address concerns scalability and flexibility in ionic neuromorphic devices. Neuromorphic nanofluidics, which harness ionic rather than electronic transport to emulate neural dynamics, have generally depended on nanoconfinement—squeezing ionic motion into channels with dimensions comparable to the electrical double layers that form at solid-liquid interfaces. This confinement gives researchers exquisite control over how quickly ions accumulate, dissipate, and relax, which in turn determines whether a device exhibits the short-term and long-term memory effects characteristic of biological synapses. But the inherent scales of the spatial interactions at nanoconfined interfaces impose hard limits: fabricating angstrom-scale or nanometer-scale channels across large areas is difficult, integrating them into soft, stretchable, or biologically coupled systems is harder still, and the spatial scale of the governing physics cannot easily be tuned after fabrication.

The new system sidesteps nanoconfinement altogether by introducing what the researchers call cascaded-heterointerfacial ion traps within a multiphasic gel ion transporter, or GIT. Rather than a single confining channel, the device consists of multiple gel phases joined at heterointerfaces—boundaries between chemically distinct soft phases with differing ionic environments. When ions attempt to cross these interfaces, they encounter what the authors describe as interionic hierarchical cross-interface retardation and dissipation. In practical terms, the coupled dynamics of ion accumulation, crowding, and relaxation on either side of each boundary slow and scatter ionic flux in a hierarchy of timescales. Each interface therefore behaves like a trap: it can hold ionic charge transiently, release it gradually, and modulate the transmission of subsequent ionic spikes. By cascading several such interfaces in series, the system multiplies these trapping effects, creating rich temporal dynamics from macroscopically scalable soft materials.

One of the clearest signatures of this design is a pronounced ionic bipolar rectification effect, with rectification ratios exceeding one thousand. Rectification means that ionic current flows far more readily in one polarity than the other, analogous to the behavior of a diode in an electronic circuit. In biological terms, it resembles the one-way gating of signals at synapses and ion channels. Achieving ratios above 10^3 in an entirely soft, gel-based architecture indicates that the cascaded traps do not merely attenuate signals but actively sculpt their directionality. The rectification arises because ion enrichment and depletion at successive heterointerfaces depend strongly on the polarity of the applied bias, so the same physical structure can either promote or suppress transmission depending on which way the ionic spike travels through the system.

Beyond directionality, the device exhibits what the researchers characterize as spike-strength-dependent and timing-dependent dual-order plasticity. This is a cornerstone of neural computation. In the brain, synapses strengthen or weaken depending both on how strongly they are activated and on the precise relative timing of pre- and postsynaptic spikes—a phenomenon known as spike-timing-dependent plasticity, which is widely believed to underpin learning and memory formation. By reproducing both amplitude-dependent and timing-dependent forms of plasticity within a single ionic platform, the cascaded-heterointerface system captures two distinct but intertwined orders of synaptic adaptability. The interionic retardation and dissipation at each trap accumulate across the cascade, so the device’s response to any given spike depends on its own recent history—exactly the property that distinguishes a memristive, memory-bearing element from a passive conductor.

Particularly significant is the integration of both short-term and long-term memory effects within the same material system. Short-term plasticity, in which synaptic efficacy transiently changes over milliseconds to seconds, enables computational functions such as filtering, adaptation to stimulus statistics, and temporal differentiation. Long-term plasticity, persisting over much longer durations, provides the substrate for durable memory and learned associations. Biological synapses blend the two seamlessly, and neuromorphic engineers have long struggled to replicate that blend with adequate dynamic range. The hierarchical trapping timescales of the multiphasic gel naturally generate both regimes, allowing the team to demonstrate complex ion-based synaptic adaptability and multiple biologically grounded learning rules, including forms of spike-timing-dependent behavior implemented through purely ionic dynamics.

The energy figures reported are remarkable for a soft material system: multistate ionic neuromorphic processing at as little as 0.61 picojoules per spike. For context, individual synaptic transmission events in the human brain are often estimated to consume on the order of tens of femtojoules to picojoules, and modern semiconductor-based artificial synapses frequently require far more energy per operation, particularly when overheads of converting signals between electronic and ionic or chemical domains are counted. Operating in the picojoule regime means the gel system is not merely a conceptual demonstration but approaches the energy budgets at which practical, body-attached, or even body-implanted neuromorphic hardware becomes feasible. Soft matter also offers mechanical compliance and chemical compatibility that silicon cannot match, which points directly toward the study’s most eye-catching experiment.

That experiment involved creating a biohybrid neural circuit by interfacing the in vivo neuro-iontronic processor with the transected sciatic nerve of a rat. The sciatic nerve, the major peripheral nerve running down the hind limb, was cut and the processor was connected across the transection, allowing nerve-generated spikes to drive ionic processing in the gel and, reciprocally, allowing the processor’s output to stimulate downstream nerve segments. The result was bioneuron-driven ionic neuromorphic processing: the device’s synaptic states were updated by genuine biological action potentials, and in turn the device reconfigured the interneural signals passing through the injured nerve. The experiment, approved by the Animal Protection Ethics Committee of Capital Medical University, suggests a route toward prosthetic or regenerative interfaces in which a soft computational material does not simply relay nerve signals but adaptively reshapes them according to neuromorphic learning rules.

The theoretical underpinnings of the system were developed in parallel with the experiments, with collaborators at Tsinghua University performing calculations that connect the observed macroscopic behavior to the microscopic interionic dynamics at each heterointerface. This modeling work clarifies how hierarchical cross-interface retardation and dissipation give rise to the device’s memristive characteristics and provides a design framework for tuning trap strength, cascade depth, and phase chemistry. The work builds on a rapidly maturing field of iontronics and nanofluidic computing, in which recent years have seen fluidic memristors, droplet-based modular iontronics, and mechano-ionic logic switches emerge from laboratories around the world. What distinguishes the present contribution is the deliberate move away from spatial confinement as the sole control lever and toward interfacial design in soft, multiphasic matter—a shift that promises devices that are cheaper to fabricate, easier to scale, and far more amenable to integration with living tissue.

The implications extend across several frontiers. For brain-inspired computing, the system offers a hardware substrate in which the informational carriers, the physical dynamics, and the material compliance all resemble biology far more closely than conventional transistors do, potentially enabling machine learning implementations that exploit the same temporal plasticity principles the brain uses. For medicine, a soft iontronic processor that can learn from and modulate peripheral nerve activity hints at adaptive neural prostheses, smart neuro-repair scaffolds, and closed-loop bioelectronic therapies that reconfigure damaged signaling pathways in real time. And for the broader materials community, the demonstration that cascaded heterointerfaces can substitute for nanoconfinement opens a design space in which chemistry, phase architecture, and interfacial engineering replace lithographic miniaturization. Much work remains before such devices leave the laboratory—long-term biostability, manufacturing reproducibility, and integration with clinical hardware all present substantial hurdles—but the convergence of picojoule energy consumption, dual-order synaptic plasticity, and verified in vivo biohybrid operation marks a genuinely notable step toward computing materials that think the way biology does.

The choice of gel-based ion transporters also reflects a broader trend in which soft ionic conductors are increasingly viewed as viable active materials rather than passive wiring. Because ions are the native charge carriers of living systems, devices that process signals ionically can, in principle, couple to tissue without the transduction penalties that arise when electronic implants must convert ionic bioelectricity into electron flow and back again. The multiphasic architecture takes advantage of this by letting phase chemistry, rather than channel geometry, define the functional behavior, which means trap dynamics can in principle be tuned through material formulation.

The dual-order plasticity reported here is also notable from a computational standpoint. Spike-strength dependence and timing dependence together enable learning rules that resemble Hebbian and predictive forms of adaptation studied in neuroscience, and memristive hardware implementing such rules has been proposed as a route to energy-efficient spatiotemporal learning. Demonstrating both orders of plasticity in a purely ionic, soft-matter platform suggests that such rules need not depend on solid-state electronics.

The in vivo sciatic nerve experiment, conducted under ethics approval AEEI-2025-036, additionally illustrates how a processor driven by genuine biological action potentials could serve as a testbed for studying signal reconfiguration in injured nerves, complementing conventional cuff electrodes and stimulation implants.

Subject of Research: Soft-matter iontronic devices using cascaded-heterointerface ion traps for multistate ionic neuromorphic processing and biohybrid neural interfacing.

Article Title: Soft-matter multistate ionic neuromorphic processing based on cascaded-heterointerface ion traps

Article References: Wu, Z., Zhang, S., Zhai, L., Zhang, A., Zhu, X., Xu, J., Liu, H., Xu, Z., Jiang, L., & Zhao, Z. (2026). Soft-matter multistate ionic neuromorphic processing based on cascaded-heterointerface ion traps. Nature Electronics. https://doi.org/10.1038/s41928-026-01688-z

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01688-z

Keywords: neuromorphic computing, iontronics, soft matter, ionic memory, synaptic plasticity, nanofluidics, biohybrid circuits, gel ion transporter, ion traps, energy-efficient computing, neural interfaces, Nature Electronics