A multifunctional sensor for cell traction force, matrix remodeling and biomechanical assays in self-assembled 3D tissues in vitro
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Vision-based tactile sensor design using physically based rendering
High-resolution tactile sensors are very helpful to robots for fine-grained perception and manipulation tasks, but designing those sensors is challenging. This is because the designs are based on the compact integration of multiple optical elements, and it is difficult to understand the correlation between the element arrangements and the sensor accuracy by trial and error. In this work, we introduce the digital design of vision-based tactile sensors using a physically accurate light simulator. The framework modularizes the design process, parameterizes the sensor components, and contains an evaluation metric to quantify a sensor’s performance. We quantify the effects of sensor shape, illumination setting, and sensing surface material on tactile sensor performance using our evaluation metric. The proposed optical simulation framework can replicate the tactile image of the real vision-based tactile sensor prototype without any prior sensor-specific data. Using our approach we can substantially improve the design of a fingertip GelSight sensor. This improved design performs approximately 5 times better than previous state-of-the-art human-expert design at real-world robotic tactile embossed text detection. Our simulation approach can be used with any vision-based tactile sensor to produce a physically accurate tactile image. Overall, our approach enables the automatic design of sensorized soft robots and opens the door for closed-loop co-optimization of controllers and sensors for dexterous manipulation.
Ultrafast humidity sensor and transient humidity detections in high dynamic environments
Limited by the adsorption and diffusion rate of water molecules, traditional humidity sensors, such as those based on polymer electrolytes, porous ceramics, and metal oxides, typically have long response times, which hinder their application in monitoring transient humidity changes. Here we present an ultrafast humidity sensor with a millisecond-level response. The sensor is prepared by assembling monolayer graphene oxide quantum dots on silica microspheres using a simple electrostatic self-assembly technique. Benefiting from the joint action of the micro spheres and the ultrathin humidity-sensitive film, it displays the fastest response time (2.76 ms) and recovery time (12.4 ms) among electronic humidity sensors. With the ultrafast response of the sensor, we revealed the correlation between humidity changes in speech airflow and speech activities, demonstrated the noise immunity of humidity speech activity detection, confirmed the humidity shock caused by explosions, realized ultrahigh frequency respiratory monitoring, and verified the effect of humidity-triggering in the non-invasive ventilator. This ultrafast humidity sensor has broad application prospects in monitoring transient humidity changes.
TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity
Naturally evolved T-cell receptors (TCRs) exhibit remarkably high specificity in discriminating non-self antigens from self-antigens under dynamic biomechanical modulation. In contrast, engineered high-affinity TCRs often lose this specificity, leading to cross-reactivity with self-antigens and off-target toxicity. The underlying mechanism for this difference remains unclear. Our study reveals that natural TCRs exploit mechanical force to form optimal catch bonds with their cognate antigens. This process relies on a mechanically flexible TCR–pMHC binding interface, which enables force-enhanced CD8 coreceptor binding to MHC-α1α2 domains through sequential conformational changes induced by force in both the MHC and CD8. Conversely, engineered high-affinity TCRs create rigid, tightly bound interfaces with cognate pMHCs of their parental TCRs. This rigidity prevents the force-induced conformational changes necessary for optimal catch-bond formation. Paradoxically, these high-affinity TCRs can form moderate catch bonds with non-stimulatory pMHCs of their parental TCRs, leading to off-target cross-reactivity and reduced specificity. We have also developed comprehensive force-dependent TCR–pMHC kinetics-function maps capable of distinguishing functional and non-functional TCR–pMHC pairs and identifying toxic, cross-reactive TCRs. These findings elucidate the mechano-chemical basis of the specificity of natural TCRs and highlight the critical role of CD8 in targeting cognate antigens. This work provides valuable insights for engineering TCRs with enhanced specificity and potency against non-self antigens, particularly for applications in cancer immunotherapy and infectious disease treatment, while minimizing the risk of self-antigen cross-reactivity.
Categorizing robots by performance fitness into the tree of robots
Robots are typically classified based on specific morphological features, like their kinematic structure. However, a complex interplay between morphology and intelligence shapes how well a robot performs processes. Just as delicate surgical procedures demand high dexterity and tactile precision, manual warehouse or construction work requires strength and endurance. These process requirements necessitate robot systems that provide a level of performance fitting the process. In this work, we introduce the tree of robots as a taxonomy to bridge the gap between morphological classification and process-based performance. It classifies robots based on their fitness to perform, for example, physical interaction processes. Using 11 industrial manipulators, we constructed the first part of the tree of robots based on a carefully deduced set of metrics reflecting fundamental robot capabilities for various industrial physical interaction processes. Through significance analysis, we identified substantial differences between the systems, grouping them via an expectation-maximization algorithm to create a fitness-based robot classification that is open for contributions and accessible.
A robust organic hydrogen sensor for distributed monitoring applications
Hydrogen is an abundant and clean energy source that could help to decarbonize difficult-to-electrify economic sectors. However, its safe deployment relies on the availability of cost-effective hydrogen detection technologies. We describe a hydrogen sensor that uses an organic semiconductor as the active layer. It can operate over a wide temperature and humidity range. Ambient oxygen p-dopes the organic semiconductor, which improves hole transport, and the presence of hydrogen reverses this doping process, leading to a drop in current and enabling reliable and rapid hydrogen detection. The sensor exhibits a high responsivity (more than 10,000), fast response time (less than 1 s), low limit of detection (around 192 ppb) and low power consumption (less than 2 μW). It can operate continuously for more than 646 days in ambient air at room temperature. We show that the sensor outperforms a commercial hydrogen detector in realistic sensing scenarios, illustrating its suitability for application in distributed sensor networks for early warning of hydrogen leaks and preventing explosions or fires.
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