Intricate Shapes Research Articles

Programmable and flexible wood-based origami electronics

Natural polymer substrates are gaining attention as substitutes for plastic substrates in electronics, aiming to combine high performance, intricate shape deformation, and environmental sustainability. Herein, natural wood veneer is converted into a transparent wood film (TWF) substrate. The combination of 3D printing and origami technique is established to create programmable wood-based origami electronics, which exhibit superior flexibility with high tensile strength (393 MPa) due to the highly aligned cellulose fibers and the formation of numerous intermolecular hydrogen bonds between them. Moreover, the flexible TWF electronics exhibit editable multiplexed configurations and maintain stable conductivity. This is attributed to the strong adhesion between the cellulose-based ink and TWF substrate by non-covalent bonds. Benefiting from its anisotropic structure, the programmability of TWF electronics is achieved through sequentially folding into predesigned shapes. This design not only promotes environmental sustainability but also introduces its customizable shapes with potential applications in sensors, microfluidics, and wearable electronics.

Taguchi optimization of Wire EDM process parameters for machining LM5 aluminium alloy.

LM5 alloy is suitable for metal castings for marine and aesthetic uses due to its admirable resistance to corrosion. In order to make intricate shapes in the LM5 alloy, this study intends to assess the impact of Wire Electric Discharge Machining process variables, like Pulse on Time (Ton), Pulse off Time (Toff), Gap Voltage (GV) and Wire Feed (WF) on responses like Material Removal Rate (MRR), Surface Roughness (SR), and Kerf Width (Kw). The LM5 aluminium alloy plate was produced through stir casting process. SEM, EDAX and XRD images confirm the LM5 Al alloy's microstructure and crystal structure. WEDM studies were conducted using design of experiments approach based on L9 orthogonal array and analysed using Taguchi's Signal to Noise Ratio (S/N) analysis. Pulse on Time has the greatest statistical effects on MRR (68.25%), SR (79.46%) and kerf (81.97%). In order to assess the surface integrity of the WEDM machined surfaces, the SEM study on the topography was conducted using the optimum surface roughness process variables: Ton 110 μs, Toff 50 μs, GV 40 V, and WF 9 m/min. SEM images show the recast layer and its thickness. The average absolute error for MRR is 1.69%, SR is 3.89% and kerf is 0.88%, based on mathematical (linear regression) models. The Taguchi's Signal to Noise ratio analysis is the most appropriate for single objective optimization of responses.

Comparative analysis on high temperature tribological behaviour of additive manufactured and cast AlSi10Mg alloy

Abstract In recent years, additive manufacturing (AM) of AlSi10Mg, a material widely used in the aerospace and automotive sectors, has gained considerable interest for its ability to produce intricate shapes with improved mechanical properties. This proposed study aims to experimentally assess the high-temperature tribological performance of as-printed AlSi10Mg and compare it with traditionally cast AlSi10Mg to determine its suitability for high-temperature tribological applications. Samples of AlSi10Mg were manufactured via powder bed fusion, followed by tribological testing at elevated temperatures (30 °C, 100 °C, 200 °C, and 300 °C). The performance of both printed and cast AlSi10Mg pins was compared against an EN31 disc. The microstructure, worn surface, and wear debris of both printed and cast AlSi10Mg were examined using XRD and SEM with EDX analyses. As the load and temperature increase, both printed and cast AlSi10Mg materials demonstrate an increase in wear rate and friction coefficient. The wear rate of printed AlSi10Mg decreased by 60 % compared to cast AlSi10Mg at higher temperatures. There is a consistent trend in the coefficient of friction between printed and cast AlSi10Mg samples at low temperatures and loads. Abrasive wear predominantly occurs in both printed and cast AlSi10Mg samples at 30 °C within a load range of 10 to 20 N, while adhesive wear predominates in the same samples at temperatures ranging from 100°C to 300 °C with an applied load of 30 N. At low temperatures, smaller wear debris is observed, whereas at high temperatures and under a load of 30 N, larger wear debris is observed for both cast and printed AlSi10Mg samples.

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization.

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co3O4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an "onset radius," which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

One-pot microfluidic fabrication of micro ceramic particles

In the quest for miniaturization across technical disciplines, microscale ceramic blocks emerge as pivotal components, with performance critically dependent on precise scales and intricate shapes. Sharp-edged ceramic microparticles, applied from micromachining to microelectronics, require innovative fabrication techniques for high-throughput production while maintaining structural complexity and mechanical integrity. This study introduces a “one-pot microfluidic fabrication” system incorporating two device fabrication strategies, “groove & tongue” and sliding assembling, achieving an unprecedented array of microparticles with diverse, complex shapes and refined precision, outperforming traditional methods in production rate and quality. Optimally designed sintering profiles based on derivative thermogravimetry enhance microparticles’ shape retention and structural strength. Compression and scratch tests validate the superiority of microparticles, suggesting their practicability for diverse applications, such as precise micromachining, sophisticated microrobotics and delicate microsurgical tools. This advancement marks a shift in microscale manufacturing, offering a scalable solution to meet the demanding specifications of miniaturized technology components.

Seed-Mediated Growth and Advanced Characterization of Chiral Gold Nanorods.

The controlled growth of gold nanostructures with complex shapes and reduced symmetry, exemplified by chiral gold nanorods and nanoparticles, is one of the most dynamic fields of nanochemistry. A timely summary of underlying concepts, including growth mechanisms and redefined chirality measures, would further promote this research area. In this perspective, we aim to establish qualitative connections between the chiral shapes and growth conditions, specifically for the seed-mediated synthesis of chiral gold nanorods as a convenient case of chiral morphogenesis. The crystallographic and morphological features of achiral nanorods used as seeds, the experimental conditions during chiral growth, and the symmetry of the chiral inducers, can all be exploited to obtain nanorods with intricate chiral shapes. Chirality characterization (such as electron tomography techniques) and quantification (including chirality measures) emerge as critical aspects to comprehensively explore and understand such structures, enabling optimization of their geometric and optical features. We conclude by discussing relevant challenges to be addressed toward a better controlled synthesis of chiral plasmonic nanostructures.

Study on the effects of ultrasonic vibration-assisted wire-EDM machining parameters on material removal rate and surface roughness of NiTiV shape memory alloy

Nickel-titanium (NiTi)-based shape memory alloys (SMAs) exhibit remarkable properties, making them increasingly demanding in various engineering and medical applications especially when these SMAs are manufactured with intricate shapes and sizes. This present study examines the machining of Ni50Ti48V2 SMA using ultrasonic-assisted WEDM with input parameters of pulse ON time (TON), pulse OFF time (TOFF), peek current (I), and servo voltage (SV) along with 20 kHz constant ultrasonic vibration on the responses of material removal rate (MRR) and surface roughness (Ra). RSM-BBD response surface methodology was used to conduct the experiments. It was inferred from analysis of variance (ANOVA) analysis, that as the peak current (I) and pulse ON time (TON) increased, the MRR increased by 65.76% and Ra decreased by 54.44%. The confirmation was carried out in USV-WEDM under optimal conditions, which shows that both the predicted and actual MRR and Ra are close to each other. When compared with conventional WEDM, the USV-WEDM shows significant improvement in MRR by 8.6% and also improved surface quality by 6.3%. The surface characteristics of Ni50Ti48V2 after machining were examined using field-emitted scanning electron microscopy, showing small irreularisation peaks and wavy surface topography at optimised parameter conditions. USV-WEDM showed a lower-order micro-hardness variation due to effective flushing of molten layer re-accumulation as compared to WEDM. When comparing the base and machined samples, the temperatures of austenite and martensite are nearly the same, indicating that shape memory effects are unaffected by the USV-WEDM process.

The Immersed Boundary Conformal Method for Kirchhoff–Love and Reissner–Mindlin shells

This work utilizes the Immersed Boundary Conformal Method (IBCM) to analyze linear elastic Kirchhoff–Love and Reissner–Mindlin shell structures within an immersed domain framework. Immersed boundary methods involve embedding complex geometries within a background grid, which allows for great flexibility in modeling intricate shapes and features despite the simplicity of the approach. The IBCM method introduces additional layers conformal to the boundaries, allowing for the strong imposition of Dirichlet boundary conditions and facilitating local refinement. In this study, the construction of boundary layers is combined with high-degree spline-based approximation spaces to further increase efficiency. The Nitsche method, employing non-symmetric average operators, is used to couple the boundary layers with the inner patch, while stabilizing the formulation with minimal penalty parameters. High-order quadrature rules are applied for integration over cut elements and patch interfaces. Numerical experiments demonstrate the efficiency and accuracy of the proposed formulation, highlighting its potential for complex shell structures modeled through Kirchhoff–Love and Reissner–Mindlin theories. These tests include the generation of conformal interfaces, the coupling of Kirchhoff–Love and Reissner–Mindlin theories, and the simulation of a cylindrical shell with a through-the-thickness crack.

Exploring surface integrity: Experimental investigation into abrasive waterjet deep hole drilling on ss 316L for biomedical applications

Precision drilling of difficult-to-cut materials with exceptional surface integrity is a crucial factor in producing advanced manufactured components in industries like aviation, automotive, defense, electronic, and medical devices. More specifically, deep hole drilling enables precise customization of prostheses, improving patient comfort and functionality. Abrasive waterjet deep hole drilling has become a vital drilling technique in many industries due to its adaptability and effectiveness in drilling intricate shapes on hard-to-cut materials. The interplay between process parameters and surface finish underscores the need for systematic investigation to enhance drilling efficiency and quality. In this research, an attempt has been made to explore the influence of abrasive waterjet deep hole process parameters viz water pressure and standoff distance on the surface integrity of the drilled holes for biomedical applications. A surface roughness tester has been used to evaluate surface roughness at three distinctive places, namely the top, middle, and bottom of the drilled holes. The investigation showed that surface roughness at the top of the holes is less as compared to the middle and bottom of the holes. The average surface roughness along the depth of the drilled holes is greatly influenced by water pressure. Additionally, the morphology of the drilled holes has been studied using a scanning electron microscope (SEM). SEM images revealed that the top surface of the drilled holes is free from defects.

Numerical investigation of premixed syngas/air flame evolution in a closed duct with tulip flame formation

In this study, the dynamic behavior of the premixed syngas/air flames in enclosed ducts with obstacles at the blockage ratio of 0.5, as well as without obstacles, was studied via large eddy simulation and the turbulent flame speed closure model. The hydrogen volume fraction (hydrogen content in fuel) was set to 50 %. Results indicate that the simulation reproduced the flame shapes, flame speed and explosion overpressure observed in the experiments. In cases without obstacles, a “tulip” flame with a tulip cusp can be seen, and secondary cusps emerge on tulip lips due to the high-pressure zone ahead of “tulip” flame lips, leading to in a distorted “tulip” flame. In cases with obstacles, the flame develops hemispherical and finger shapes upstream of the obstacles and evolves into a “tulip” flame downstream of the obstacles due to vortices and adverse pressure gradients. The alteration of the flow direction within the unburned and burned zones results in intricate flame shapes. Flame speed and overpressure show a close relationship and exhibit oscillations following the generation of the “tulip” flame. Typically, the velocity, pressure and flow fields adjacent to the flame front can affect dynamic behaviors of premixed syngas/air flames.

Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation

ABSTRACTRecent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR‐derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post‐processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F1 score of 66%. In England experiments, the system achieved a 59% F1 score when fine‐tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.

Free energy and extension of stiff polymer chains confined in nanotubes with diverse cross-sectional shapes

The statistical mechanics of stiff polymer chains confined within narrow tubes is a foundational topic in polymer physics, extensively analyzed in prior research. For cylindrical, rectangular, and slit-like confinements, the chains’ free energy and extension adhere to a scaling law consistent with the Odijk theory. While this scaling law may not apply to tubes with different cross-sectional geometries, there is a lack of research examining the behavior of stiff chains in tubes with intricate cross-sectional shapes. In this study, we investigate the partition function of a stiff chain confined within an elliptic tube using the path integral approach, deriving a deflection length in a concise closed form through dimensional analysis. This length scale facilitates straightforward expressions for the chain's free energy and extension. Notably, we discover a shape-independent property of these expressions applicable to tubes with a wide variety of cross-sectional geometries. Extensive numerical simulations are conducted using a biased chain-growth Monte Carlo method, incorporating the Pruned and Enriched Rosenbluth algorithm, to validate the theoretical predictions on the confinement free energy and extension of chains in tubes with differing shapes.

Multi-Sensing Inspection System for Thermally Induced Micro-Instability in Metal-Based Selective Laser Melting.

Additive manufacturing (AM) excels in engineering intricate shapes, pioneering functional components, and lightweight structures. Nevertheless, components fabricated through AM often manifest elevated residual stresses and a myriad of thermally induced micro-instabilities, including cracking, incomplete fusion, crazing, porosity, spheroidization, and inclusions. In response, this study proposed a sophisticated multi-sensing inspection system specifically tailored for the inspection of thermally induced micro-instabilities at the micro-nano scale. Simulation results substantiate that the modulation transfer function (MTF) values for each field of view in both visible and infrared optical channels surpass the benchmark of 0.3, ensuring imaging fidelity conducive to meticulous examination. Furthermore, the innovative system can discern and accurately capture data pertaining to thermally induced micro-instabilities across visible and infrared spectra, seamlessly integrating this information into a backend image processing system within operational parameters of a 380-450 mm distance and a 20-70 °C temperature range. Notably, the system's design is harmoniously aligned with the requisites of processing and assembly, heralding a significant advancement in bolstering the inspection effect of thermally induced micro-instabilities for the AM component.

How complexity affects and shapes the dynamics in the football industry

This paper examines the complex dynamics at play within the football industry, highlighting how intricate and auto-catalytic processes impact and shape its operational landscape. By examining the interplay and mutual interactions in the objective functions (1) and the transition stages in the value chain (2), the study tries to shed light on the very diverse range of interactions characterising football industry. By examining the interplay and mutual interactions within the objective functions system (1), a source of complexity was found due to the impossibility of isolating the effect of a single variable on the entire structure. Then, the study analysed the transition stages in the value chain (2), from lower levels to elite competitions and vice versa, revealing the complexity that impacts the organisation, strategies, and decision-making of football clubs. The paper aims to evaluate the contributions of different components, such as economic and financial requirements and team achievements, to the self-sustaining nature of the football ecosystem. By examining these two themes, this article aims to provide a critical analysis of the intricacies of the football industry. This analysis should aid in identifying potential avenues for future research and determining the managerial frameworks and mechanisms required to navigate this complex industry. * Despite being a collaborative effort, §§ 1, 3.3 and 4 are attributed to Claudio Nigro; § 3.1 to Simona Curiello; §§ 3.3.1, 3.3.2 and 3.3.3 to Enrica Iannuzzi; §§ 3.2, 3.2.1 and 3.2.2 to Enrico Lubrano; §§ 2, 3 and 5 to Vincenzo Sanguigni; § 3.4 to Raffaele Silvestri; §§ 2.1, 2.2, 2.3, 3.1.1 and 3.1.2 to Rosa Spinnato.

Energy-efficient dynamic 3D metasurfaces via spatiotemporal jamming interleaved assemblies for tactile interfaces

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications.

DUNET D UNET Dilated UNET for Brain Tumor Sub Region Segmentation using MRI Images

The precise diagnosis and treatment planning of brain tumors significantly rely on the accurate segmentation of sub-regions from Magnetic Resonance Imaging (MRI) data. In this research, we propose a framework, D-UNET (Dilated-UNET), which enhances the traditional UNET architecture by incorporating dilated convolutions. UNET is the deep CNN architecture widely adopted for biomedical image segmentation tasks. D-UNET is specifically designed for brain tumor sub-region segmentation from multi-modal (T1, T2, T1ce, Flair) MRI images in nifti file format, each comprising 155 slices. The framework comprises of four distinct steps viz. data collection, data preprocessing, model training, and outcome evaluation. D-UNET employs two key modules during training, the dilated encoding module and the dilated decoding module. These modules enable the model to efficiently capture multi-scale contextual information, facilitating better representation learning for complex and varied tumor sub[1]regions. We evaluated the performance of D-UNET using Intersection over Union and Dice Coefficient metrics. The experimental results demonstrate that D-UNET outperforms the traditional UNET and other benchmark models in terms of segmentation accuracy. Notably, D-UNET excels in capturing finer details and intricate shapes of tumor sub-regions, contributing to its superiority in brain tumor segmentation. The ability to precisely delineate tumor sub-regions from different modalities provides crucial insights for medical professionals in treatment planning and decision-making.

Investigating micro-EDM machinability of Ti-35Nb-7Zr-5Ta (TNZT) alloy

ABSTRACT Titanium and its new-generation alloys are widely employed in high-end applications due to their special properties. Modern industries frequently use micro – Electro Discharge Machining (μ-EDM), a non-conventional machining technique that is especially useful for processing hard to machine materials like titanium alloys. μ-EDM can produce intricate shapes with excellent dimensional precision. The main emphasis of this work is an experimental analysis of the μ-EDM of TNZT alloy (Ti-35Nb-7Zr-5Ta) employing a tungsten carbide (WC) electrode. The results showed that with the increase in capacitance (C) 10nF to 100nF at Voltage (V) 80 V, the machining performance parameters such as material removal rate (MRR), overcut (OC), crater area, surface microhardness, and surface roughness increase by 6.38, 1.52, 4.66, 1.07, and 3.29 times, respectively. However, the circularity error of the µ-hole increases by 1.81 times. This study intends to show how the two key machining factors (C & V) influence significant machining performance parameters.

GPU-Enabled Exascale Quantum Transport Modeling of Carbon Nanotube Devices

The work will describe our efforts to develop an exascale electrostatic-quantum transport framework, Quantum-eXstatic (https://github.com/AMReX-Microelectronics/eXstatic), currently supporting the modeling of carbon nanotube field-effect transistors (CNTFETs). The framework is capable of modeling realistic three-dimensional structures, efficiently, using CPU/GPU heterogeneous architectures of state-of-the-art supercomputers. It is built on top of the AMReX library, which is a GPU-enabled software library containing functionality for writing particle-mesh applications on structured grids. The Quantum-eXstatic framework comprises three major components: the electrostatic module, the quantum transport module, and the part that self-consistently couples the two modules. The electrostatic module using a finite-volume multigrid solver to compute the electrostatic potential induced by charges on the surface of carbon nanotubes, as well as by source, drain, and gate terminals, which can be modeled using an embedded boundary approach to allow for intricate shapes. The quantum transport module uses the Nonequilibrium Green's Function (NEGF) method to model induced charge. Currently, it supports coherent (ballistic) transport, mode-space approximation for subbands of nanotube, contacts modeled as semi-infinite leads, and Hamiltonian representation using the tight-binding approximation. Special features are being included for accurate modeling of long nanotubes, such as adaptive refinement of integration contours to resolve van Hove singularities in long channels. Particular attention is paid to overlap computations with communication to optimize GPU performance. The self-consistency between the two modules is achieved using Broyden's modified second algorithm, which is parallelized using CPUs and GPUs. Preliminary scaling studies show that the code exhibits ~77% weak-scaling efficiency on 512 NVIDIA A100 GPUs of Perlmutter supercomputer for modeling gate-all-around CNTFETs with a channel length of 13.5 microns at equilibrium conditions. This case involved computations of electrostatic potential in 14.5 billion computational cells and Green’s function computation for a Hamiltonian of size 131,072 x 131,072. For problems of this large size, the code takes about 5 min to reach self-consistency. Figure 1

Few-shot bronze vessel classification via siamese fourier networks

Exploring ancient Chinese artifacts is crucial for analyzing East Asian technological development, with bronze vessel being the critical element. Bronze vessels, typically featuring intricate carvings, hold historical significance and provide valuable insights into past civilizations. However, identifying bronze patterns can be challenging for human vision, and most RGB-domain methods fail to capture periodic designs. Addressing these issues, we propose the Siamese Fourier Networks (SFN), a parallel network model designed for few-shot regular pattern classification. The Siamese network can differentiate between intricate shapes, while Fourier features enable the extraction of regular textures. To optimize parallel networks, we combine the BCE loss and focal contrastive loss, balancing positive and negative samples. Moreover, we introduce the Bronze Vessel Dataset, featuring 527 samples with diverse shapes and unbalanced distributions. Extensive experiments with advanced few-shot methods demonstrate the superiority of SFN and focal mechanism, significantly improving accuracy.

Versatile, modular, and customizable magnetic solid-droplet systems

Magnetic miniature robotic systems have attracted broad research interest because of their precise maneuverability in confined spaces and adaptability to diverse environments, holding significant promise for applications in both industrial infrastructures and biomedical fields. However, the predominant construction methodology involves the preprogramming of magnetic components into the system's structure. While this approach allows for intricate shape transformations, it exhibits limited flexibility in terms of reconfiguration and presents challenges when adapting to diverse materials, combining, and decoupling multiple functionalities. Here, we propose a construction strategy that facilitates the on-demand assembly of magnetic components, integrating ferrofluid droplets with the system's structural body. This approach enables the creation of complex solid-droplet robotic systems across a spectrum of length scales, ranging from 0.8 mm to 1.5 cm. It offers a diverse selection of materials and structural configurations, akin to assembling components like building blocks, thus allowing for the seamless integration of various functionalities. Moreover, it incorporates decoupling mechanisms to enable selective control over multiple functions, leveraging the fluidity, fission/fusion, and magneto-responsiveness properties inherent in the ferrofluid. Various solid-droplet systems have validated the feasibility of this strategy. This study advances the complexity and functionality achievable in small-scale magnetic robots, augmenting their potential for future biomedical and other applications.