In this study, high‐performance ionic soft actuators are developed for the first time using collectively exhaustive boron and sulfur co‐doped porous carbon electrodes (BS‐COF‐Cs), derived from thiophene‐based boronate‐linked covalent organic framework (T‐COF) as a template. The one‐electron deficiency of boron compared to carbon leads to the generation of hole charge carriers, while sulfur, owing to its high electron density, creates electron carriers in BS‐COF‐C electrodes. This antagonistic functionality of BS‐COF‐C electrodes assists the charge‐transfer rate, leading to fast charge separation in the developed ionic soft actuator under alternating current input signals. Furthermore, the hierarchical porosity, high surface area, and synergistic effect of co‐doping of the BS‐COF‐Cs play crucial roles in offering effective interaction of BS‐COF‐Cs with poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), leading to the generation of high electro‐chemo‐mechanical performance of the corresponding composite electrodes. Finally, the developed ionic soft actuator based on the BS‐COF‐C electrode exhibits large bending strain (0.62%), excellent durability (90% retention for 6 hours under operation), and 2.7 times higher bending displacement than PEDOT:PSS under extremely low harmonic input of 0.5 V. This study reveals that the antagonistic functionality of heteroatom co‐doped electrodes plays a crucial role in accelerating the actuation performance of ionic artificial muscles.
Epidermal sensors for remote healthcare and performance monitoring require the ability to operate under the effects of bodily motion, heat, and perspiration. Here, the use of purpose‐synthesized polymer‐based dry electrodes and graphene‐based strain gauges to obtain measurements of swallowed volume under typical conditions of exercise is evaluated. The electrodes, composed of the common conductive polymer poly(3,4 ethylenedioxythiophene) (PEDOT) electrostatically bound to poly(styrenesulfonate)‐b‐poly(poly(ethylene glycol) methyl ether acrylate) (PSS‐b‐PPEGMEA), collect surface electromyography (sEMG) signals on the submental muscle group, under the chin. Simultaneously, the deformation of the surface of the skin is measured using strain gauges comprising single‐layer graphene supporting subcontinuous coverage of gold and a highly plasticized composite containing PEDOT:PSS. Together, these materials permit high stretchability, high resolution, and resistance to sweat. A custom printed circuit board (PCB) allows this multicomponent system to acquire strain and sEMG data wirelessly. This sensor platform is tested on the swallowing activity of a cohort of 10 subjects while walking or cycling on a stationary bike. Using a machine learning (ML) model, it is possible to predict swallowed volume with absolute errors of 36% for walking and 43% for cycling.
more » « less- Award ID(s):
- 2223566
- NSF-PAR ID:
- 10418781
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Sensor Research
- Volume:
- 2
- Issue:
- 4
- ISSN:
- 2751-1219
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract Solid polymer electrolytes for lithium batteries promise improvements in safety and energy density if their conductivity can be increased. Nanostructured block‐copolymer electrolytes specifically have the potential to provide both good ionic conductivity and good mechanical properties. This study shows that the previously neglected nanoscale composition of the polymer electrolyte close to the electrode surface has an important effect on impedance measurements, despite its negligible extent compared to the bulk electrolyte. Using standard stainless steel blocking electrodes, the impedance of lithium salt‐doped poly(isoprene‐
b ‐styrene‐b ‐ethylene oxide) (ISO) exhibits a marked decrease upon thermal processing of the electrolyte. In contrast, covering the electrode surface with a low molecular weight poly(ethylene oxide) (PEO) brush results in higher and more reproducible conductivity values, which are insensitive to the thermal history of the device. A qualitative model of this effect is based on the hypothesis that ISO surface reconstruction at the different electrode surfaces leads to a change in the electrostatic double layer, affecting electrochemical impedance spectroscopy measurements. As a main result, PEO‐brush modification of electrode surfaces is beneficial for the robust electrolyte performance of PEO‐containing block‐copolymers and may be crucial for their accurate characterization and use in Li‐ion batteries. -
more » « less
Abstract Background Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy.
Objective The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging.
Methods Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds.
Results On average, the normalized moment prediction root mean square error was reduced by 14.58 % (
$$p=0.012$$ $$p<0.001$$ Conclusions The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.
-
more » « less
Paper-based biosensors are a potential paradigm of sensitivity achieved via microporous spreading/microfluidics, simplicity, and affordability. In this paper, we develop decorated paper with graphene and conductive polymer (herein referred to as graphene conductive polymer paper-based sensor or GCPPS) for sensitive detection of biomolecules. Planetary mixing resulted in uniformly dispersed graphene and conductive polymer ink, which was applied to laser-cut Whatman filter paper substrates. Scanning electron microscopy and Raman spectroscopy showed strong attachment of conductive polymer-functionalized graphene to cellulose fibers. The GCPPS detected dopamine and cytokines, such as tumor necrosis factor-alpha (TNF-α), and interleukin 6 (IL-6) in the ranges of 12.5–400 µM, 0.005–50 ng/mL, and 2 pg/mL–2 µg/mL, respectively, using a minute sample volume of 2 µL. The electrodes showed lower detection limits (LODs) of 3.4 µM, 5.97 pg/mL, and 9.55 pg/mL for dopamine, TNF-α, and IL-6 respectively, which are promising for rapid and easy analysis for biomarkers detection. Additionally, these paper-based biosensors were highly selective (no serpin A1 detection with IL-6 antibody) and were able to detect IL-6 antigen in human serum with high sensitivity and hence, the portable, adaptable, point-of-care, quick, minute sample requirement offered by our fabricated biosensor is advantageous to healthcare applications.
-
Abstract Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging’s region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ( $ p $ < .001) and increased the prediction coefficient of determination by 20.13% ( $ p $ < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy.more » « less
