Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

Flexible electronic systems have received increasing attention in the past few decades because of their wide-ranging applications that include the flexible display, eyelike digital camera, skin electronics, and intelligent surgical gloves, among many other health monitoring devices. As one of the most widely used technologies to integrate rigid functional devices with elastomeric substrates for the manufacturing of flexible electronic devices, transfer printing technology has been extensively studied. Though primarily relying on reversible interfacial adhesion, a variety of advanced transfer printing methods have been proposed and demonstrated. In this review, we first summarize the characteristics of a few representative methods of transfer printing. Next, we will introduce successful demonstrations of each method in flexible electronic devices. Moreover, the potential challenges and future development opportunities for transfer printing will then be briefly discussed.

Laser-induced graphene (LIG) is a simple, environmentally friendly, efficient, and less costly method, as well as can form various shapes on a flexible substrate in situ without the use of masks. More importantly, it can tune the work function of LIG easily by changing laser parameters to control the transportation of carriers. In this work, the work functions of LIG were controlled by adjusting the frequency or speed of the laser, and a series of LIG/GaO_x Schottky photodetectors were formed. When the work function of the graphene increases, the Fermi energy is shifted below the crossing point of the Π and Π^* bands, and then more electrons or holes can be activated to participate in the conduction process, resulting in low resistance. Meanwhile, a large built-in electric field can be formed when using a high work function LIG, which is more beneficial to separate photo-generated carriers. Enabled by the controllable LIG, LIG/GaO_x Schottky photodetectors can be modulated to have high photoresponsivity or self-powered characteristics. Our work provides a high-performance photodetector with excellent mechanical flexibility and long-life stability, promising applications in the flexible optoelectronic fields.

Tactile sensing is essentially required for dexterous manipulation in robotic applications. Mimicking human perception of softness identification in a non-invasive fashion, thus achieving satisfactory interaction with fragile objects remains a grand challenge. Here, a scatheless measuring methodology based on the multisensory electronic skins to quantify the elastic coefficient of soft materials is reported. This recognition approach lies in the preliminary classification of softness by piezoelectric signals with a modified machine learning algorithm, contributing to an appropriate contact force assignment for subsequent quantitative measurements via strain sensing feedback. The integration of multifunctional sensing system allows the manipulator to hold capabilities of self-sensing and adaptive grasping motility in response to objects with the various softness (i.e., kPa-MPa). As a proof-of-concept demonstration, the biomimetic manipulator cooperates with the robotic arm to realize the intelligent sorting of oranges varying in freshness, paving the way for the development of microsurgery robots, human-machine interfacing, and advanced prosthetics.

Highly adherent wettability patterns on the substrate-independent superhydrophobic surfaces of trimethoxyoctadecylsilane modified titanium dioxide (TiO₂)-based coatings were prepared by using commercial photolithography. Three custom unidirectional channels with gradient wettability patterns were obtained by spatially selective wettability conversion from superhydrophobic to superhydrophilic when the coatings were exposed to ultraviolet light (∼365 nm). The movement behavior of droplets on these unidirectional channels was studied and the displacement of droplet movement was effectively controlled. Integrating the idea of gradient wettability patterns into planar microfluidic devices (microreactors), a self-driven fluid transport was achieved to realize droplet metering, merging or reaction, and rapid transport. This self-driven fluid transport with gradient wettability patterns has great potential in fabricating a new category of pump-free microfluidic systems that can be used in various conditions.