Browsing by Author "Liu, Xianhu"

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Magnetic field-driven particle assembly and jamming for bistable memory and response plasticity
(2022-11-11) Liu, Xianhu; Tan, Hongwei; Rigoni, Carlo; Hartikainen, Teemu; Asghar, Nazish; van Dijken, Sebastiaan; Timonen, Jaakko V.I.; Peng, Bo; Ikkala, Olli
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Unlike classic synthetic stimulus-responsive and shape-memory materials, which remain limited to fixed responses, the responses of living systems dynamically adapt based on the repetition, intensity, and history of stimuli. Such plasticity is ubiquitous in biology, which is profoundly linked to memory and learning. Concepts thereof are searched for rudimentary forms of "intelligent materials." Here, we show plasticity of electroconductivity in soft ferromagnetic nickel colloidal supraparticles with spiny surfaces, assembling/disassembling to granular conducting micropillars between two electrodes driven by magnetic field B. Colloidal jamming leads to conduction hysteresis and bistable memory upon increasing and subsequently decreasing B. Abrupt B changes induce larger conduction changes than gradual B-changes. Periodic B pulsing drives to frequency-dependent facilitation or suppression of conductivity compared to exposing the same constant field. The concepts allow remotely controlled switching plasticity, illustrated by a rudimentary device. More generally, we foresee adaptive functional materials inspired by response plasticity and learning.
Magnetic field-induced particle assembly and jamming
(2024) Liu, Xianhu
School of Science | Doctoral dissertation (article-based)
Ferromagnetic materials possess the ability to undergo magnetization in the presence of an external magnetic field and exhibit rapid responsiveness to magnetic field stimuli, rendering them highly suitable as carriers for stimuli-responsive materials. Furthermore, the assembly of ferromagnetic particles under the influence of a magnetic field allows for the design of assembled superstructures with diverse properties, enabling the fulfillment of specific application requirements. In this thesis, ferromagnetic cobalt (Co) and nickel (Ni) particles with various surface roughness were synthesized utilizing the polyol method. These particles were employed as building blocks for magnetic fieldinduced assembly, resulting in the formation of assembled superstructures with distinct properties. The unique characteristics of these superstructures were systematically characterized, followed by an exploration of their potential applications. Publication I utilizes a relatively smooth-surfaced ferromagnetic Co particles as building blocks enabled the assembly of weakly jammed superstructures under the influence of a magnetic field. The shape of these assembled superstructures could be controlled by adjusting the magnetic field strength, imparting tunable sensitivity in response to pressure stimuli. Publication II employs ferromagnetic Ni particles with comparatively rougher surfaces as building blocks, the heightened interparticle friction reduced the critical packing density required for achieving jamming. Consequently, strongly jammed superstructures were formed under the influence of a magnetic field. The pronounced jamming effect induced structural memory in the assembled superstructures, enabling tunable of jamming through magnetic field application kinetics and ultimately yielding broad tunability in response to stimuli. Publication III demonstrates the transfer of jamming-induced structural memory to electrical signals, which could be further transformed into visible light signals. Additionally, pulsed magnetic fields were employed to modulate the system's responsiveness.
Mechanosensing of Stimuli Changes with Magnetically Gated Adaptive Sensitivity
(2025) Hu, Xichen; Liu, Xianhu; Xu, Quan; Ikkala, Olli; Peng, Bo
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Inspired by biological sensors that characteristically adapt to varying stimulus ranges, efficiently detecting stimulus changes sooner than the absolute stimulus values, we propose a mechanosensing concept in which the resolution can be adapted by magnetic field (H) gating to detect small pressure-changes under a wide range of compressive stimuli. This is realized with resistive sensing by pillared H-driven assemblies of soft ferromagnetic electrically conducting particles between planar electrodes under a voltage bias. By modulation of H, the pillars respond with mechanically adaptable sensitivity. Higher H enhances current resolution, while it increases scatter among repeating measurements due to increased magnetic structural jamming between colloids in their assembly. To manage the trade-off between electrical resolution and scatter, machine learning is introduced for searching optimum H gatings, thus facilitating efficient pressure prediction. This approach suggests bioinspired pathways for developing adaptive stimulus-responsive mechanosensors, detecting subtle changes across varying stimuli levels with enhanced effectiveness through machine learning.
Trainable bioinspired magnetic sensitivity adaptation using ferromagnetic colloidal assemblies
(2024-04-17) Liu, Xianhu; Tan, Hongwei; Stråka, Emil; Hu, Xichen; Chen, Min; van Dijken, Sebastiaan; Scacchi, Alberto; Sammalkorpi, Maria; Ikkala, Olli; Peng, Bo
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Nature has already suggested bioinspired functions. Beyond them, adaptive and trainable functions could be the inspiration for novel responsive soft matter beyond the state-of-the-art classic static bioinspired, stimulus-responsive, and shape-memory materials. Here, we describe magnetic assembly/disassembly of electrically conducting soft ferromagnetic nickel colloidal particles into surface topographical pillars for bistable electrical trainable memories. They allow magnetic sensing with adaptable and rescalable sensitivity ranges, enabled by bistable memories and kinetic concepts inspired by biological sensory adaptations. Based on the soft ferromagnetism of the nanogranular composition and the resulting rough particle surfaces prepared via a solvothermal synthesis, triggerable structural memory is achieved by the magnetic field-driven particle assembly and disassembly, promoted by interparticle jamming. Electrical conversion from current to frequency for electrical spikes facilitates rescalable and trainable frequency-based sensitivity on magnetic fields. This work suggests an avenue for designing trainable and adaptable life-inspired materials, for example, for soft robotics and interactive autonomous devices.