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Nanocomposites are materials of special interest for the development of flexible electronic, optical, and mechanical devices in applications such as transparent conductive electrodes and flexible electronic sensors. These materials take advantage of the electrical, chemical, and mechanical properties of a polymeric matrix, especially in force sensors, as well as the properties of a conductive filler such as silver nanowires (AgNWs). In this work, the fabrication of a force sensor using AgNWs synthesized via the polyol chemical technique is presented. The nanowires were deposited via drop-casting in polyvinyl alcohol (PVA) to form the active (electrode) and resistive (nanocomposite) sensor films, with both films separated by a cellulose acetate substrate. The dimensions of the resulting sensor are 35 mm × 40 mm × 0.1 mm. The sensor shows an applied force ranging from 0 to 3.92 N, with a sensitivity of 0.039 N. The sensor stand-off resistance, exceeding 50 MΩ, indicates a good ability to detect changes in applied force without an external force. Additionally, studies revealed a response time of 10 ms, stabilization of 9 s, and a degree of hysteresis of 1.9%. The voltage response of the sensor under flexion at an angle of 85° was measured, demonstrating its functionality over a prolonged period. The fabricated sensor can be used in applications that require measuring pressure on irregular surfaces or systems with limited space, such as for estimating movement in robot joints.

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This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips' contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types.

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Laparoscopic surgery demands highly skilled surgeons. Traditionally, a surgeon's knowledge is acquired by operating under a mentor-trainee method. In recent years, laparoscopic simulators have gained ground as tools in skill acquisition. Despite the wide range of laparoscopic simulators available, few provide objective feedback to the trainee. Those systems with quantitative feedback tend to be high-end solutions with limited availability due to cost. A modular smart trainer was developed, combining tool-tracking and force-using employing commercially available sensors. Additionally, a force training system based on polydimethylsiloxane (PDMS) phantoms for sample stiffness differentiation is presented. This prototype was tested with 39 subjects, between novices (13), intermediates (13), and experts (13), evaluating execution differences among groups in training exercises. The estimated cost is USD $200 (components only), not including laparoscopic instruments. The motion system was tested for noise reduction and position validation with a mean error of 0.94 mm. Grasping force approximation showed a correlation of 0.9975. Furthermore, differences in phantoms stiffness effectively reflected user manipulation. Subject groups showed significant differences in execution time, accumulated distance, and mean and maximum applied grasping force. Accurate information was obtained regarding motion and force. The developed force-sensing tool can easily be transferred to a clinical setting. Further work will consist on a validation of the simulator on a wider range of tasks and a larger sample of volunteers.

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Force Sensing Resistors (FSRs) are manufactured by sandwiching a Conductive Polymer Composite (CPC) between metal electrodes. The piezoresistive property of FSRs has been exploited to perform stress and strain measurements, but the rheological property of polymers has undermined the repeatability of measurements causing creep in the electrical resistance of FSRs. With the aim of understanding the creep phenomenon, the drift response of thirty two specimens of FSRs was studied using a statistical approach. Similarly, a theoretical model for the creep response was developed by combining the Burger's rheological model with the equations for the quantum tunneling conduction through thin insulating films. The proposed model and the experimental observations showed that the sourcing voltage has a strong influence on the creep response; this observation-and the corresponding model-is an important contribution that has not been previously accounted. The phenomenon of sensitivity degradation was also studied. It was found that sensitivity degradation is a voltage-related phenomenon that can be avoided by choosing an appropriate sourcing voltage in the driving circuit. The models and experimental observations from this study are key aspects to enhance the repeatability of measurements and the accuracy of FSRs.

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Conductive polymer composites are manufactured by randomly dispersing conductive particles along an insulating polymer matrix. Several authors have attempted to model the piezoresistive response of conductive polymer composites. However, all the proposed models rely upon experimental measurements of the electrical resistance at rest state. Similarly, the models available in literature assume a voltage-independent resistance and a stress-independent area for tunneling conduction. With the aim of developing and validating a more comprehensive model, a test bench capable of exerting controlled forces has been developed. Commercially available sensors-which are manufactured from conductive polymer composites-have been tested at different voltages and stresses, and a model has been derived on the basis of equations for the quantum tunneling conduction through thin insulating film layers. The resistance contribution from the contact resistance has been included in the model together with the resistance contribution from the conductive particles. The proposed model embraces a voltage-dependent behavior for the composite resistance, and a stress-dependent behavior for the tunneling conduction area. The proposed model is capable of predicting sensor current based upon information from the sourcing voltage and the applied stress. This study uses a physical (non-phenomenological) approach for all the phenomena discussed here.

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Surface attachment of a planktonic bacteria, mediated by adhesins and extracellular polymeric substances (EPS), is a crucial step for biofilm formation. Some pathogens can modulate cell adhesiveness, impacting host colonization and virulence. A framework able to quantify cell-surface interaction forces and their dependence on chemical surface composition may unveil adhesiveness control mechanisms as new targets for intervention and disease control. Here we employed InP nanowire arrays to dissect factors involved in the early stage biofilm formation of the phytopathogen Xylella fastidiosa. Ex vivo experiments demonstrate single-cell adhesion forces up to 45 nN, depending on the cell orientation with respect to the surface. Larger adhesion forces occur at the cell poles; secreted EPS layers and filaments provide additional mechanical support. Significant adhesion force enhancements were observed for single cells anchoring a biofilm and particularly on XadA1 adhesin-coated surfaces, evidencing molecular mechanisms developed by bacterial pathogens to create a stronger holdfast to specific host tissues.

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