Fibrillar Adhesives Research Articles

Understanding the effects of mineralization and structure on the mechanical properties of tendon-bone insertion using mesoscale computational modeling

Tendon-bone fibrocartilaginous insertion, or enthesis, is a specialized interfacial region that connects tendon and bone, effectively transferring forces while minimizing stress concentrations. Previous studies have shown that insertion features gradient mineralization and branching fiber structure, which are believed to play critical roles in its excellent function. However, the specific structure-function relationship, particularly the effects of mineralization and structure at the mesoscale fiber level on the properties and function of insertion, remains poorly understood. In this study, we develop mesoscale computational models of the distinct fiber organization at tendon-bone insertions, capturing the branching network from tendon to interface fibers and the different mineralization scales. We specifically analyze three key descriptors: the mineralization scale of interface fibers, the mean, and relative standard deviation of the local branching angles of interface fibers. Tensile test simulations on insertion models with varying mineralization scales of interface fibers and structures are performed to mimic the primary loading condition applied to the insertion. We measure and analyze five representative mechanical properties: Young's modulus, strength, toughness, resilience, and failure strain. Our results reveal that mechanical properties are significantly influenced by the three key descriptors, with tradeoffs observed between mutually exclusive properties. For instance, strength and resilience plateau beyond a certain mineralization scale, while failure strain and Young's modulus exhibit monotonic decreasing and increasing trends, respectively. Consequently, there exists an optimal mineralization scale for toughness due to these tradeoffs. By analyzing the mesoscale deformation and failure mechanisms from simulation trajectories, we identify three fracture regimes closely related to the trends in mechanical properties, supporting the observed tradeoffs. Additionally, we examine in detail the effects of the mean and relative standard deviation of local branching angles on mechanical properties and deformation mechanisms. Overall, our study enhances the fundamental understanding of the composition-structure-function relationships at the tendon-bone insertion, complementing recent experimental studies. The mechanical insights from our work have the potential to guide the future biomimetic design of fibrillar adhesives and interfaces for joining soft and hard materials.

Fibrillar adhesives with unprecedented adhesion strength, switchability and scalability.

Bio-inspired fibrillar adhesives have received worldwide attention but their potentials have been limited by a trade-off between adhesion strength and adhesion switchability, and a size scale effect that restricts the fibrils to micro/nanoscales. Here, we report a class of adhesive fibrils that achieve unprecedented adhesion strength (∼2MPa), switchability (∼2000), and scalability (up to millimeter-scale at the single fibril level), by leveraging the rubber-to-glass (R2G) transition in shape memory polymers (SMPs). Moreover, R2G SMP fibrillar adhesive arrays exhibit a switchability of >1000 (with the aid of controlled buckling) and an adhesion efficiency of 57.8%, with apparent contact area scalable to 1000mm2, outperforming existing fibrillar adhesives. We further demonstrate that the SMP fibrillar adhesives can be used as soft grippers and reusable superglue devices that are capable of holding and releasing heavy objects >2000 times of their own weight. These findings represent significant advances in smart fibrillar adhesives for numerous applications, especially those involving high-payload scenarios.

On shear adhesion of adhesive fibrils

Natural fibrillar adhesives, widely recognized for their strong and reversible adhesion, have inspired the development of numerous synthetic adhesive systems applied in diverse fields. These biological and bioinspired adhesive fibrils exhibit a wide range of aspect ratios spanning four orders of magnitude. Accurate prediction of their adhesion performance is crucial for their practical applications. Prior investigations have primarily focused on adhesion under normal loading, although shear loading is also commonly encountered in various scenarios. To date, only a few shear adhesion models exist, and they are limited to either high or low aspect ratios. Rigorous mechanics analysis of adhesive fibrils’ shear adhesion strength across a wide range of aspect ratios is lacking. In this study, we developed a mechanics model based on the compliance method for evaluating the energy-controlled interfacial failure of the adhesive fibrils, estimated the shear adhesion strength of a lap shear system across a wide range of fibrillar aspect ratios, and carried out the finite element analysis (FEA) to validate the model prediction. Our model reveals that, depending on the fibrillar aspect ratio, the adhesive fibril exhibits three distinct deformation regimes: thin film shearing, thick block shearing, and slender beam bending. The important parameters governing the shear adhesion of the lap shear system are the aspect ratio of fibrils, elastic moduli of the fibrils and substrates, while the Poisson's ratio of fibrils has no noticeable effect. Our model captures the shear compliance across a wide aspect ratio range from 10−4 to 102 accurately and is in excellent agreement with the FEA results. Based on this compliance solution, a rigorous scaling law for shear adhesion strength is derived, which can predict the energy-controlled adhesion regime as well as the theoretical strength-limited regime, as the aspect ratio decreases. These results reveal the underlying mechanisms governing the shear adhesion strength of fibrillar adhesives and provide guidance for future design and optimization of fibrillar adhesives.

Application of machine learning to object manipulation with bio-inspired microstructures

Bioinspired fibrillar adhesives have been proposed for novel gripping systems with enhanced scalability and resource efficiency. Here, we propose an in-situ optical monitoring system of the contact signatures, coupled with image processing and machine learning. Visual features were extracted from the contact signature images recorded at maximum compressive preload and after lifting a glass object. The algorithm was trained to cope with several degrees of misalignment and with unbalanced weight distributions by off-center gripping. The system allowed an assessment of the picking process for objects of various mass (200, 300, and 400 g). Several classifiers showed a high accuracy of about 90 % for successful prediction of attachment, depending on the mass of the object. The results promise improved reliability of handling objects, even in difficult situations.

Machine Learning-Based Shear Optimal Adhesive Microstructures with Experimental Validation.

Bioinspired fibrillar structures are promising for a wide range of disruptive adhesive applications. Especially micro/nanofibrillar structures on gecko toes can have strong and controllable adhesion and shear on a wide range of surfaces with residual-free, repeatable, self-cleaning, and other unique features. Synthetic dry fibrillar adhesives inspired by such biological fibrils are optimized in different aspects to increase their performance. Previous fibril designs for shear optimization are limited by predefined standard shapes in a narrow range primarily based on human intuition, which restricts their maximum performance. This study combines the machine learning-based optimization and finite-element-method-based shear mechanics simulations to find shear-optimized fibril designs automatically. In addition, fabrication limitations are integrated into the simulations to have more experimentally relevant results. The computationally discovered shear-optimized structures are fabricated, experimentally validated, and compared with the simulations. The results show that the computed shear-optimized fibrils perform better than the predefined standard fibril designs. This design optimization method can be used in future real-world shear-based gripping or nonslip surface applications, such as robotic pick-and-place grippers, climbing robots, gloves, electronic devices, and medical and wearable devices.

Designing directional adhesive pillars using deep learning-based optimization, 3D printing, and testing

Nature-inspired fibrillar adhesives have versatile applications, such as wall-climbing robots and grippers, which require residue-free and repeatable adhesion. To meet the requirements of excellent adhesion strength combined with controllability of adhesion and detachment, directional adhesive pillars with anisotropic adhesion properties have been extensively investigated. However, existing designs that simply mimic nature suffer from relatively weak adhesion and insufficient directionality, because most of them were designed without considering a sufficiently large design space. In this study, we rigorously defined adhesive directionality and systematically investigated the optimal pillar shape to obtain an excellent combination of adhesion strength and directionality using deep-learning-based optimization. A data-driven model based on artificial neural networks was trained using finite element analysis data obtained from 199,466 adhesive pillar shapes, which can predict the directionality and adhesion strength of given pillar shapes. Optimization was performed using the trained neural network to obtain the optimal pillar shape under the geometric constraints necessary to secure the reliability of the pillar. Finally, we suggest adhesive pillar shapes having severe directionality and high adhesive strength at the same time. For validation of our optimization model and optimized result, the proposed pillar shapes were fabricated using a 3D polyjet printer, and their adhesion strength and directionality were tested. We noticed that optimized pillar shapes we obtained have superior directionality and adhesion strength in real.

Predicting adhesion strength of micropatterned surfaces using gradient boosting models and explainable artificial intelligence visualizations

Fibrillar dry adhesives are widely used due to their effectiveness in air and vacuum conditions. However, their performance depends on various factors. Previous studies have proposed analytical methods to predict adhesion strength on micro-patterned surfaces. However, the method lacks interpretation on which parameters are critical. This research utilizes gradient-boosting machine learning (ML) algorithms to accurately predict adhesion strength. Additionally, explainable machine learning (XML) methods are employed to interpret the underlying reasoning behind the predictions. The analysis demonstrates that gradient boosting models achieve a high correlation coefficient (R > 0.95) in accurately predicting pull-off force on micro-patterned surfaces. The use of XML methods provides insights into the importance of features, their interactions, and their contributions to specific predictions. This novel, explainable, and data-driven approach holds potential for real-time applications, aiding in the identification of critical features that govern the performance of fibrillar adhesives. Furthermore, it improves end-users' confidence by offering human-comprehensible explanations and facilitates understanding among non-technical audiences.

Opportunities and Challenges in Space Robotics

Robotic technology has the potential to play a major part in the quest to uncover the origins of life and investigate the feasibility of long-term human habitation outside of Earth. However, the harsh conditions present in outer space, including radiation, vacuum, temperature fluctuations, and complex, unpredictable terrain, pose significant obstacles to the development and operation of robotic systems. Although a variety of robotic platforms have been created for exploration, construction, and maintenance in space, their capacity to address fundamental questions remains largely untapped. In particular, the design, construction, and control of robots that can withstand the rigors of extreme space environments remain a significant challenge. In this special section, which is building on a workshop co-organized by the guest editor at the 2020 IEEE International Conference on Robotics and Automation, we focus on the recent advancements of space robots, as well as opportunities and critical challenges of this field. In particular, we cover topics of significant interest to the field including mobility on granular terrain and bio-inspired and soft robots for extraterrestrial applications. Because research pertaining to space robotics is generally interdisciplinary in nature, we include articles not only from roboticists but also from physicists and planetary scientists. This special section includes two research articles (2100040, 2100125), two review articles (2100063, 2200071), and two perspectives (2100106, 2100195). The research article by Khajenejad et al., presents an alternative set-valued or set-membership estimation framework for estimation of unknown planetary terrain parameters based on how they affect rover motion (2100040). The other research article by Green et al., presents CASPER, a novel screw-propelled excavation system for planetary exploration and excavation. This platform employs screw propellers for both mobility and excavation and demonstrates significant excavation capability with low mass and power requirements (2100125). The two review papers in this special section discuss robotic attachment mechanisms (2100063) and soft robots (2200071) for space applications. In particular, the article by Spenko, reviews robotic attachment mechanisms for extraterrestrial applications. Specifically, bio-inspired fibrillar dry adhesives for smooth surfaces, electrostatic adhesives for smooth and rougher surfaces, and microspines for rocky and rough surfaces are discussed (2100063). The article by Zhang et al., reviews soft robotics technologies for space applications. Particularly, advantages of soft robots for such applications and potential design, modeling, fabrication, sensing, and control improvements for adapting to space environments are described (2200071). Finally, there are two perspective articles included in this special section that provide insight on soft robots (2100106), and bio-inspired multi-legged and limbless robots (2100195) for mobility on extra-terrestrial terrain. Ng and Lum elaborate on key advantages and potential changes to actuation, fabrication, and locomotion of soft robots for future planetary explorations (2100106). Li and Lewis discuss using terradynamics insights for developing bio-inspired ground robots that are effective in maneuvering on granular and rocky planetary terrain (2100195). The guest editor extends heartfelt gratitude to the authors, reviewers, and the editorial team for their great contributions to this special section. We hope this collection of articles will inspire researchers currently working in space robotics, and also encourage other researchers to engage this exciting and challenging field. Hamid Marvi

Robust and Elevated Adhesion and Anisotropic Friction in a Bioinspired Bridged Micropillar Array.

Bioinspired structured adhesives have promising applications in the fields of robotics, electronics, medical engineering, and so forth. The strong adhesion and friction as well as the durability of bioinspired hierarchical fibrillar adhesives are essential for their applications, which require fine submicrometer structures to stay stable during repeated use. Here, we develop a bioinspired bridged micropillars array (BP), which realizes a 2.18-fold adhesion and a 2.02-fold friction as compared to that of poly(dimethylsiloxane) (PDMS) original micropillar arrays. The aligned bridges offer BP strong anisotropic friction. The adhesion and friction of BP can be finely regulated by changing the modulus of the bridges. Moreover, BP shows strong adaptability to surface curvature (ranging from 0 to 800 m-1), excellent durability over 500 repeating cycles of attachment/detachment, and self-cleaning ability. This study presents a novel approach for designing robust structured adhesives with strong and anisotropic friction, which may find applications in areas such as climbing robots and cargo transportation.

Gradient Micropillar Array Inspired by Tree Frog for Robust Adhesion on Dry and Wet Surfaces.

The strong adhesion on dry and wet surfaces and the durability of bioinspired hierarchical fibrillar adhesives are critical for their applications. However, the critical design for the strong adhesion normally depends on fine sub-micron structures which could be damaged during repeat usage. Here, we develop a tree frog-inspired gradient composite micropillars array (GP), which not only realizes a 2.3-times dry adhesion and a 5.6-times wet adhesion as compared to the pure polydimethylsiloxane (PDMS) micropillars array (PP), but also shows excellent durability over 200 repeating cycles of attachment/detachment and self-cleaning ability. A GP consists of stiffer tips and softer roots by incorporating gradient dispersed CaCO3 nanoparticles in PDMS micropillar stalks. The modulus gradient along the micropillar height facilitates the contact formation and enhances the maximum stress during the detaching. The study here provides a new design strategy for robust adhesives for practical applications in the fields of robotics, electronics, medical engineering, etc.

Mechanisms of detachment in fibrillar adhesive systems

Several creatures can climb on smooth surfaces with the help of hairy adhesive pads on their legs. A rapid change from strong attachment to effortless detachment of the leg is enabled by the asymmetric geometry of the tarsal hairs. In this study, we propose mechanisms by which the hairy pad can be easily detached, even when the hairs possess no asymmetry. Here, we examine the possible function of the tibia-tarsus leg joint and the claws. Based on a spring-based model, we consider three modes of detachment: vertically pulling the pad while maintaining either a (1) fixed or a (2) free joint, or by (3) flexing the pad about the claw. We show that in all cases, the adhesion force can be significantly reduced due to elastic forces when the hairs deform non-uniformly across the array. Our proposed model illustrates the design advantage of such fibrillar adhesive systems, that not only provide strong adhesion, but also allow easy detachment, making them suitable as organs for fast locomotion and reliable hold. The presented approaches can be potentially used to switch the adhesion state in bio-inspired fibrillar adhesives, by incorporating artificial joints and claws into their design, without the need of asymmetric or stimuli-responsive fibrillar structures.

Rate-dependent adhesion in dynamic contact of spherical-tip fibrillar structures

Fibrillar adhesives that rely on the Van der Waals interactions exhibit fascinating prospects in dynamic attachment applications like perching of aerial robots and capture of orbital debris. Understanding the dynamic attachment/detachment behaviors of fibrillar adhesive structures plays a crucial role in realizing reliable attachment to target surfaces. In this paper, we investigate the dynamic contact of a single spherical-tip fibrillar structure model and emphasize the significance of rate-dependent adhesion. A theoretical model considering both crack-tip and bulk viscoelasticity is presented. The proposed model is verified by comparing with elaborate collision tests under different dynamic contact conditions. The dynamic evolution of contact radius, displacement and force are investigated in detail. The energy dissipation mechanism is discussed by analyzing the variation of coefficient of restitution vs. initial contact velocity. Under the combined effect of rate-dependent adhesion and inertia force, the radius-displacement curve in sticking mode appears peculiar superposition loops, and a new critical sticking mode is found which can broaden the initial velocity range that ensures successful attachment. It is demonstrated that rate-dependent adhesion and bulk dissipation are coupled closely while the former is dominant near the critical sticking velocity. Moreover, the decrease of the reference speed, pillar size, and modulus of materials are favorable for increasing the critical sticking velocity, thereby enhancing the capacity to resist dynamic detachment. This work provides an underlying theoretical support on the dynamic contact of fibrillar adhesives and offers a clear insight on the role of the rate-dependent adhesion during the process.

Frictional shear stress of ZnO nanowires on natural and pyrolytic graphite substrates

The friction behaviour of ZnO nanowires on natural graphite (NG) and highly oriented pyrolytic graphite (HOPG) substrates was tested in ambient conditions by use of optical microscopy based nanomanipulation. Nanowires on the step-free and waviness-free NG substrate exhibit a diameter-independent nominal frictional shear stress of 0.48 MPa, and this provides a benchmark for studying how the surface topography of graphite influences nanowire friction. Nanowires on the HOPG substrate present a significant diameter-dependent frictional shear stress, increasing from 0.25 to 2.78 MPa with the decrease of nanowire diameter from 485 to 142 nm. The waviness of HOPG has a limited effect on the nanowire friction, as a nanowire can fully conform to the substrate. The surface steps on the HOPG can significantly enhance the nanowire friction and lead to a much higher frictional shear stress than that on NG due to mechanical blocking and the presence of a Schwoebel barrier at step edges. The surface steps, however, can also generate small wedge-shaped gaps between a nanowire and substrate, and thus reduce the nanowire friction. With the decrease in nanowire diameter, the capacity for the nanowire to better conform to the substrate reduces the length of the wedge-shaped gaps, leading to the observed increase in nanowire friction. The results have improved our understanding of the unique friction behaviour of nanowires. Such an improved understanding is expected to benefit the design and operation of nanowire-friction-based devices, including bio-inspired fibrillar adhesives, soft grippers, rotary nanomotors, and triboelectric nanogenerators.

Gecko inspired reversible adhesion via quantum dots enabled photo-detachment

• High efficiency photo driven reversible adhesion smart surface inspired by gecko. • The smart surface could increase up to 88.5 °C in 60 s trigger by MoO 3 dots. • The mechanism of the reversible process were discussed. Inspired by the detachment behavior of gecko toe valgus, a reversible dry adhesive surface was developed, which is composed of polydimethylsiloxane (PDMS) micropillar arrays and MoO 3-x quantum dots (MoO 3-x QDs). The reversibility of adhesion is caused by the thermal expansion of the overall structure of the PDMS micropillar array due to the thermal response of MoO 3-x QDs to external stimuli. Under infrared laser irradiation, the interface temperature of MoO 3-x -PDMS surface increased from 20 °C to 88.5 °C in 60 s. The MoO 3-x QDs-induced photothermal conversion facilitate the reduction of the real contact fraction between the bio-inspired micropillar arrays and the opposing/target surface, resulting in a significant decrease in adhesion. This work demonstrates a new strategy for realizing remote control of smart adhesives or surfaces. The mechanism elucidated in this work may find a broad application, such as in climbing robots, micro-manipulation/micro-selection in advanced manufacturing, active self-cleaning surface for solar panels, fibrillar dry adhesives and touch sensitive grippers in outer space explorations, and more.

A self-adhesion criterion for slanted micropillars

Micropillar arrays see use in many fields such as biochemistry, mechanobiology, microfluidics, and micro/nanotechnology via applications like carbon nanotube forests and bioinspired fibrillar adhesives. In these fields, it is commonly desirable to either pack the micropillars as densely as possible or manufacture them to be as long/compliant possible. A barrier to either aim is the phenomenon of self-adhesion, where adjacent micropillars adhere to each other due to Van der Waals forces. In this paper, with self-adhesion as the limiting constraint, we show that longer, more compliant, and/or more densely packed micropillar arrays can be made by slanting the micropillars. From their typical orthogonal configuration, small inclinations produce an increment in micropillar packing and length up to a critical (optimum) angle, beyond which significant slanting produces a reduction in both. For parameter values typical of bioinspired fibrillar adhesives, we estimate that slanted micropillars arranged in a rectangular (triangular) lattice can improve in density by 10% (20%). Slanted micropillars also have increased compliance to loads applied orthogonally to the substrate (we refer to this as ‘orthogonal compliance’), achieved by engaging bending over axial deformation modes. This is particularly useful in fibrillar adhesives for conformation to surface roughness, hence increasing the overall adhesive strength. Our model provides a quantitative tool for the design of slanted micropillar arrays and computes the resulting enhanced array-properties.

Predicting the adhesion strength of micropatterned surfaces using supervised machine learning

Fibrillar dry adhesives have shown great potential in many applications thanks to their tunable adhesion, notably for pick-and-place handling of fragile objects. However, controlling and monitoring alignment with the target objects is mandatory to enable reliable handling. In this paper, we present an in-line monitoring system that allows optical analysis of an array of individual fibrils (with a contact radius of 350 µm) in contact with a smooth glass substrate, followed by the prediction of their adhesion performance. Images recorded at maximum compressive preload represent characteristic contact signatures that were used to extract visual features. These features, in turn, were used to create a linear model and to train different linear and non-linear regression models for predicting adhesion force depending on the misalignment angle. Support vector regression and boosted tree models exhibited highest accuracies and outperformed an analytical model reported in literature. Overall, this new approach enables predictions in gripping objects by contact observations in near real-time, which likely improves the reliability of handling operations.

Machine Learning-Based and Experimentally Validated Optimal Adhesive Fibril Designs.

Setae, fibrils located on a gecko's feet, have been an inspiration of synthetic dry microfibrillar adhesives in the last two decades for a wide range of applications due to unique properties: residue-free, repeatable, tunable, controllable and silent adhesion; self-cleaning; and breathability. However, designing dry fibrillar adhesives is limited by a template-based-design-approach using a pre-determined bioinspired T- or wedge-shaped mushroom tip. Here, a machine learning-based computational approach to optimize designs of adhesive fibrils is shown, exploring a much broader design space. A combination of Bayesian optimization and finite element methods creates novel optimal designs of adhesive fibrils, which are fabricated by two-photon-polymerization-based 3D microprinting and double-molding-based replication out of polydimethylsiloxane. Such optimal elastomeric fibril designs outperform previously proposed designs by maximum 77% in the experiments of dry adhesion performance on smooth surfaces. Furthermore, finite-element-analyses reveal that the adhesion of the fibrils is sensitive to the 3D fibril stem shape, tensile deformation, and fibril microfabrication limits, which contrast with the previous assumptions that mostly neglect the deformation of the fibril tip and stem, and focus only on the fibril tip geometry. The proposed computational fibril design could help design future optimal fibrils with less help from human intuition.

Making Contact: A Review of Robotic Attachment Mechanisms for Extraterrestrial Applications

The space environment offers unique challenges for robotic grippers and controllable attachment mechanisms. Applications include satellite and orbital debris capture, perching for free‐floating robots inside the International Space Station, climbing rocky surfaces in planetary exploration tasks, and grasping asteroids and other rough substrates for drilling and sample collection operations. Many traditional adhesive technologies, such as pressure‐sensitive adhesives, suction, and electromagnetism, are not tenable in the space environment due to the lack of an atmosphere (e.g., pressure‐sensitive adhesives outgas and suction requires a pressure differential) or suitable substrates (e.g., materials for space applications are rarely ferromagnetic). Instead, a number of other technologies have shown promise for space, including bio‐inspired fibrillar dry adhesives that offer controllable adhesion on both flat and curved smooth surfaces, electrostatic adhesives that work on smooth surfaces but also rougher surfaces and fabrics, and microspines for rocky and rough substrates. Herein, a review of these technologies, focusing on their operation and providing examples of the technologies applied to space and other extraterrestrial missions is presented.

The role of interfacial curvature in controlling the detachment strength of bioinspired fibrillar adhesives

In this paper we investigate the influence of interfacial curvature on the detachment strength and load distribution of bio-inspired fibrillar adhesives subjected to normal loading. Previous investigations unraveled the benefits of backing layer (BL) thickness in counteracting the detrimental load concentration created by interfacial misalignment. However, little attention was dedicated to the role of interfacial curvature on the load distribution and the emerging adhesive strength. Based on the concavity of the curvature and on the BL thickness, the adhesive can detach more easily or develop stronger adhesion, compared to a flat-on-flat contact. Investigating concavity suggests the possibility of controlling the detachment strength of the adhesive via shape-actuation, where curvature can provide adhesive strengthening or ease of detachment. We analyze the curvature-induced strengthening/weakening of the adhesive in combination to BL thickness, interfacial misalignment, and the geometric characteristics of the array of fibrils. We discover that detrimental load concentrations, created by BL interaction and interfacial misalignment, drastically reduce when the curvature prompts larger stretch to the central fibrils. Conversely, when the curvature prompts larger stretch to the peripheral fibrils, load concentrations superpose to the detriment of adhesive strength. Our quantitative analysis provides a design tool for strong and controllable curvature-based adhesives.

Breakdown of continuum models for spherical probe adhesion tests on micropatterned surfaces

The adhesion of fibrillar dry adhesives, mimicking nature's principles of contact splitting, is commonly characterized by using axisymmetric probes having either a flat punch or spherical geometry. When using spherical probes, the adhesive pull-off force measured depends strongly on the compressive preload applied when making contact and on the geometry of the probe. Together, these effects complicate comparisons of the adhesive performance of micropatterned surfaces measured in different experiments. In this work we explore these issues, extending previous theoretical treatments of this problem by considering a fully compliant backing layer with an array of discrete elastic fibrils on its surface. We compare the results of the semi-analytical model presented to existing continuum theories, particularly with respect to determining a measurement system- and procedure-independent metric for the local adhesive strength of the fibrils from the global pull-off force. It is found that the discrete nature of the interface plays a dominant role across a broad range of relevant system parameters. Accordingly, a convenient tool for simulation of a discrete array is provided. An experimental procedure is recommended for use in conjunction with this tool in order to extract a value for the local adhesive strength of the fibrils, which is independent of the other system properties (probe radius, backing layer thickness, and preload) and thus is suitable for comparison across experimental studies.