Monolithic integrated optoelectronic chip for vector force detection.

Sensors with a small footprint and real-time detection capabilities are crucial in robotic surgery and smart wearable equipment. Reducing device footprint while maintaining its high performance is a major challenge and a significant limitation to their development. Here, we proposed a monolithic integrated micro-scale sensor, which can be used for vector force detection. This sensor combines an optical source, four photodetectors, and a hemispherical silicone elastomer component on the same sapphire-based AlGaInP wafer. The chip-scale optical coupling is achieved by employing the laser lift-off techniques and the flip-chip bonding to a processed sapphire substrate. This hemispherical structure device can detect normal and shear forces as low as 1 mN within a measurement range of 0-220 mN for normal force and 0-15 mN for shear force. After packaging, the sensor is capable of detecting forces over a broader range, with measurement capabilities extending up to 10 N for normal forces and 0.2 N for shear forces. It has an accuracy of detecting a minimum normal force of 25 mN and a minimum shear force of 20 mN. Furthermore, this sensor has been validated to have a compact footprint of approximately 1.5 mm2, while maintaining high real-time response. We also demonstrate its promising potential by combining this sensor with fine surface texture perception in the fields of compact medical robot interaction and wearable devices.

Non-equilibrium compression achieving high sensitivity and linearity for iontronic pressure sensors.

Flexible pressure sensors with high sensitivity and linearity are highly desirable for robot sensing and human physiological signal detection. However, the current strategies for stabilizing axial microstructures (e.g., micro-pyramids) are mainly susceptible to structural stiffening during compression, thereby limiting the realization of high sensitivity and linearity. Here, we report a bending-induced non-equilibrium compression process that effectively enhances the compressibility of microstructures, thereby crucially improving the efficiency of interfacial area growth of electric double layer (EDL). Based on this principle, we fabricate an iontronic flexible pressure sensor with vertical graphene (VG) array electrodes. Ultra-high sensitivity (185.09 kPa-1) and linearity (R2 = 0.9999) are realized over a wide pressure range (0.49 Pa-66.67 kPa). It also exhibits remarkable mechanical stability during compression and bending. The sensor is successfully employed in a robotic gripping task to recognize the targets of different materials and shapes based on a multilayer perception (MLP) neural network. It opens the door to realizing haptic sensing capabilities for robotic hands and prosthetic limbs.

Giant Negative Thermopower Enabled by Bidirectionally Anchored Cations in Multifunctional Polymers.

The lack of high-quality ionic thermoelectric materials with negative thermopowers has stimulated scientists' broad research interest. The effective adjustment of the interaction between ions and a polymer network is an important way to achieve high-quality ion thermoelectric properties. Integrating different types of ion-polymer interactions into the same thermoelectric device seems to lead to unexpected gains. In this work, we propose a strategy for bidirectionally anchoring cations to synergistically generate a giant negative thermopower and high ionic conductivity. This is mainly achieved through synergistic ion-polymer coordination and Coulomb interactions. An ionic thermoelectric material was prepared by infiltrating a polycation electrolyte [poly(diallyldimethylammonium chloride)] with CuCl2 into the poly(vinyl alcohol)-chitosan aerogel. The confinement effect of copper-coordinated chitosan on cations, the repulsive property of the polycationic electrolyte on cations, and the unique chemical configuration of a transition metal chloride anion ([CuCl4]2-) are the fundamental guarantees for achieving a thermopower of -28.4 mV·K-1. Moreover, benefiting from the high charge density of the polycationic electrolyte, we obtain an ionic conductivity of 40.5 mS·cm-1. These findings show the application prospect of synergistic different types of ion-polymer interactions in designing multifunctional ionic thermoelectric materials.

Wearable Pressure Sensors with Capacitive Response over a Wide Dynamic Range.

At present, there are mainly two types of capacitive pressure sensors based on ordinary capacitance and electrical double layer (EDL) capacitance. However, few researchers have combined these two types of capacitors in pressure sensing to improve the dynamic range of a sensor under pressure. Here, we fabricated a capacitive pressure sensor with an asymmetric structure based on poly(vinylidene fluoride-co-hexafluoropropylene) using a simple electrospinning process. A layer of mixed ionic nanofiber membrane and a layer of pure nanofiber membrane were stacked and used as the dielectric layer of the sensor. Due to the porous structure and non-stickiness of the pure nanofiber membrane, it can be penetrated by the mixed ionic nanofiber membrane under pressure, realizing the reversible conversion from ordinary capacitance to EDL capacitance, thereby achieving a great change in the capacitance value. The sensitivities of the sensor are 55.66 and 24.72 kPa-1 in the pressure ranges of 0-31.11 and 31.11-66.67 kPa, respectively, with good cycle stability, fast loading-unloading response time, and an ultra-low pressure detection limit as low as 0.087 Pa. Finally, this sensor was used for the detection of human physiological signals, and the sensor would have potential applications in the fields of human tactile sensing systems, bionic robots, and wearable devices.

Giant Thermopower of Hydrogen Ion Enhanced by a Strong Hydrogen Bond System.

Ionic thermoelectric materials based on organic polymers are of great significance for low-grade heat harvesting and self-powered wearable temperature sensing. Here, we demonstrate a poly(vinyl alcohol) (PVA) hydrogel that relies on the differential transport of H+ in PVA hydrogels with different degrees of crystallization. After the inorganic acid is infiltrated into the physically cross-linked PVA hydrogel, the ionic conductor exhibits a huge ionic thermopower of 38.20 mV K-1, which is more than twice the highest value reported for hydrogen ion transport thermoelectric materials. We attribute the enhanced thermally generated voltage to the movement of H+ in the strong hydrogen bond system of PVA hydrogels and the restrictive effect of the strong hydrogen bond system on anions. This ionic thermoelectric hydrogel opens up a new way for thermoelectric conversion devices using H+ as an energy carrier.

Giant negative thermopower of ionic hydrogel by synergistic coordination and hydration interactions.

The design of ultrasensitive ionic thermopiles is important for low-grade heat collection and temperature sensing. However, high-quality ionic thermoelectric materials with negative thermopower have been rarely reported to date. Effective adjustment of the interaction between the polymer network and the electrolyte anion/cation is an important method to achieve notable thermopower. Here, we demonstrate an ionic hydrogel thermoelectric material with giant negative thermopower obtained by synergistic coordination and hydration interactions. The ionic hydrogel, made of polyvinyl alcohol and sodium hydroxide, is prepared by simple dry-annealed process and exhibits a thermopower of up to −37.61 millivolts per kelvin, an extremely high absolute thermopower for electronic and ionic conductors. This ionic hydrogel is promising for the design of high-thermopower ionic thermoelectric materials and the low-grade heat energy harvesting.

Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials.

Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive neurophysiological investi-gation of the human cortex, which might be potentially used for CI detection. Objectives: The aim of this study is to explore whether the spatiotemporal features of TMS Evoked Potentials (TEPs) could classify CI from healthy controls (HC). Methods: Twenty-one patients with CI and 22 HC underwent a single-pulse TMS-EEG stimulus in which the pulses were delivered to the left dorsolateral prefrontal cortex (left DLPFC). After preprocessing, seven regions of interest (ROIs) and two most reliable TEPs' components: N100 and P200 were selected. Next, seven simple and interpretable linear features of TEPs were extracted for each region, three common machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used to detect CI. Meanwhile, data augmentation and voting strategy were used for a more robust model. Finally, the performance differences of features in classifiers and their contributions were investigated. Results: 1. In the time domain, the features of N100 had the best performance in the SVM classifier, with an accuracy of 88.37%. 2. In the aspect of spatiality, the features of the right frontal region and left parietal region had the best performance in the SVM classifier, with an accuracy of 83.72%. 3. The Local Mean Field Power (LMFP), Average Value (AVG), Latency and Amplitude contributed most in classification. Conclusions: The TEPs induced by TMS over the left DLPFC has significant differences spatially and temporally between CI and HC. Machine learning based on the spatiotemporal features of TEPs have the ability to separate the CI and HC which suggest that TEPs has potential as non-invasive biomarkers for CI diagnosis.