Metallic Nanoislands on Graphene for Biomechanical Sensing

Optics-Free, In Situ Swelling Monitoring of Articular Cartilage with Graphene Strain Sensors

Articular cartilage derives its load-bearing strength from the mechanical and physiochemical coupling between the collagen network and negatively charged proteoglycans, respectively. Current disease modeling approaches and treatment strategies primarily focus on cartilage stiffness, partly because indentation tests are readily accessible. However, stiffness measurements via indentation alone cannot discriminate between proteoglycan degradation versus collagen degradation, and there is a lack of methods to monitor physiochemical contributors in full-stack tissue. To decouple these contributions, here, we developed a platform that measures tissue swelling in full-depth equine cartilage explants using piezoresistive graphene strain sensors. These piezoresistive strain sensors are embedded within an elastomer bulk and have sufficient sensitivity to resolve minute, real-time changes in swelling. By relying on simple DC resistance measurements over optical techniques, our platform can analyze multiple samples in parallel. Using these devices, we found that cartilage explants under enzymatic digestion showed distinctive swelling responses to a hypotonic challenge and established average equilibrium swelling strains in healthy cartilage (4.6%), cartilage with proteoglycan loss (0.5%), and in cartilage with both collagen and proteoglycan loss (-2.6%). Combined with histology, we decoupled the pathologic swelling responses as originating either from reduced fixed charge density or from loss of intrinsic stiffness of the collagen matrix in the superficial zone. By providing scalable and in situ monitoring of cartilage swelling, our platform could facilitate regenerative medicine approaches aimed at restoring osmotic function in osteoarthritic cartilage or could be used to validate physiologically relevant swelling behavior in synthetic hydrogels.

Machine learning-based leaky momentum prediction of plasmonic random nanosubstrate

In this work, we explore the use of machine learning for constructing the leakage radiation characteristics of the bright-field images of nanoislands from surface plasmon polariton based on the plasmonic random nanosubstrate. The leakage radiation refers to a leaky wave of surface plasmon polariton (SPP) modes through a dielectric substrate which has drawn interest due to its possibility of direct visualization and analysis of SPP propagation. A fast-learning two-layer neural network has been deployed to learn and predict the relationship between the leakage radiation characteristics and the bright-field images of nanoislands utilizing a limited number of training samples. The proposed learning framework is expected to significantly simplify the process of leaky radiation image construction without the need of sophisticated equipment. Moreover, a wide range of application extensions can be anticipated for the proposed image-to-image prediction.

The Manufacture of Unbreakable Bionics via Multifunctional and Self-Healing Silk-Graphene Hydrogels

Biomaterials capable of transmitting signals over longer distances than those in rigid electronics can open new opportunities for humanity by mimicking the way tissues propagate information. For seamless mirroring of the human body, they also have to display conformability to its curvilinear architecture, as well as, reproducing native-like mechanical and electrical properties combined with the ability to self-heal on demand like native organs and tissues. Along these lines, a multifunctional composite is developed by mixing silk fibroin and reduced graphene oxide. The material is coined "CareGum" and capitalizes on a phenolic glue to facilitate sacrificial and hierarchical hydrogen bonds. The hierarchal bonding scheme gives rise to high mechanical toughness, record-breaking elongation capacity of ≈25 000%, excellent conformability to arbitrary and complex surfaces, 3D printability, a tenfold increase in electrical conductivity, and a fourfold increase in Young's modulus compared to its pristine counterpart. By taking advantage of these unique properties, a durable and self-healing bionic glove is developed for hand gesture sensing and sign translation. Indeed, CareGum is a new advanced material with promising applications in fields like cyborganics, bionics, soft robotics, human-machine interfaces, 3D-printed electronics, and flexible bioelectronics.

Resistive crack-based nanoparticle strain sensors with extreme sensitivity and adjustable gauge factor, made on flexible substrates

In this paper, we report the demonstration of highly sensitive flexible strain sensors formed by a network of metallic nanoparticles (NPs) grown under vacuum on top of a cracked thin alumina film which has been deposited by atomic layer deposition. It is shown that the sensor sensitivity depends on the surface density of NPs as well as on the thickness of alumina thin films that can both be well controlled via the deposition techniques. This method allows reaching a record strain sensitivity value of 2.6 × 108 at 7.2% strain, while exhibiting high sensitivity in a large strain range from 0.1% to 7.2%. The demonstration is followed by a discussion enlightening the physical understanding of sensor operation, which enables the tuning of its performance according to the above process parameters.