Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Technology Roadmap for Flexible Sensors.

Recent Progress in Essential Functions of Soft Electronic Skin

The flexible strain sensor is of significant importance in wearable electronics, since it can help monitor the physical signals from the human body. Among various strain sensors, the foam-shaped ones have received widespread attention owing to their light weight and gas permeability. However, the working range of these sensors is still not large enough, and the sensitivity needs to be further improved. In this work, we develop a high-performance foam-shaped strain sensor composed of Ti3C2Tx MXene, multiwalled carbon nanotubes (MWCNTs), and thermoplastic polyurethane (TPU). MXene sheets are adsorbed on the surface of a composite foam of MWCNTs and TPU (referred to as TPU/MWCNTs foam), which is prefabricated by using a salt-templating method. The obtained TPU/MWCNTs@MXene foam works effectively as a lightweight, easily processable, and sensitive strain sensor. The TPU/MWCNTs@MXene device can deliver a wide working strain range of ∼100% and an outstanding sensitivity as high as 363 simultaneously, superior to the state-of-the-art foam-shaped strain sensors. Moreover, the composite foam shows an excellent gas permeability and suitable elastic modulus close to those of skin, indicating its being highly comfortable as a wearable sensor. Owing to these advantages, the sensor works effectively in detecting both subtle and large human movements, such as joint motion, finger motion, and vocal cord vibration. In addition, the sensor can be used for gesture recognition, demonstrating its perspective in human-machine interaction. Because of the high sensitivity, wide working range, gas permeability, and suitable modulus, our foam-shaped composite strain sensor may have great potential in the field of flexible and wearable electronics in the near future.

High-Performance Foam-Shaped Strain Sensor Based on Carbon Nanotubes and Ti3C2Tx MXene for the Monitoring of Human Activities.

Electronic skins (e-skins) are devices that can respond to mechanical stimuli and enable robots to perceive their surroundings. A great challenge for existing e-skins is that they may easily fail under extreme mechanical conditions due to their multilayered architecture with mechanical mismatch and weak adhesion between the interlayers. Here we report a flexible pressure sensor with tough interfaces enabled by two strategies: quasi-homogeneous composition that ensures mechanical match of interlayers, and interlinked microconed interface that results in a high interfacial toughness of 390 J·m-2. The tough interface endows the sensor with exceptional signal stability determined by performing 100,000 cycles of rubbing, and fixing the sensor on a car tread and driving 2.6 km on an asphalt road. The topological interlinks can be further extended to soft robot-sensor integration, enabling a seamless interface between the sensor and robot for highly stable sensing performance during manipulation tasks under complicated mechanical conditions. 

/pdf/highly-stable-flexible-pressure-sensors-with-a-quasi-121bljkt.pdf

Highly stable flexible pressure sensors with a quasi-homogeneous composition and interlinked interfaces

Flexible Transparent Supercapacitors: Materials and Devices

Human skin is a self-healing mechanosensory system that detects various mechanical contact forces efficiently through three-dimensional innervations. Here, we propose a biomimetic artificially innervated foam by embedding three-dimensional electrodes within a new low-modulus self-healing foam material. The foam material is synthesized from a one-step self-foaming process. By tuning the concentration of conductive metal particles in the foam at near-percolation, we demonstrate that it can operate as a piezo-impedance sensor in both piezoresistive and piezocapacitive sensing modes without the need for an encapsulation layer. The sensor is sensitive to an object’s contact force directions as well as to human proximity. Moreover, the foam material self-heals autonomously with immediate function restoration despite mechanical damage. It further recovers from mechanical bifurcations with gentle heating (70 °C). We anticipate that this material will be useful as damage robust human-machine interfaces. Designing mechanosensory system that detects mechanical contact forces like human skin remains a challenge. Here, the authors present artificially innervated self-healing foams by embedding 3D electrodes for piezo-impedance sensors that can operate in both piezoresistive and piezocapacitive sensing modes to address various proximity and mechanical interactions efficiently.

/pdf/artificially-innervated-self-healing-foams-as-synthetic-1vje6kbgdq.pdf

Artificially innervated self-healing foams as synthetic piezo-impedance sensor skins

This retrospective study aims to evaluate the predictability of the Invisalign appliance (Align Technology, Santa Clara, CA) in leveling the maxillary curve of Wilson (COW).53 adult subjects treated by orthodontists who are experienced Invisalign providers, between 2013 and 2019 were selected. All patients had either Angle Class I or II malocclusions and were treated by non-extraction in the maxillary arch with a minimum of 14 Invisalign aligners with no bite ramps or auxiliaries. Initial, predicted, and actual outcomes were analyzed with Geomagic Control X software (3D systems, Rock Hill, SC; Version 2017.0.3).Despite planning mean (SD) COW leveling of 0.25 mm (1.91), the curve became significantly more pronounced by 1.15 mm (0.85) (P < .001). There was a significant overexpression of buccal inclinations between mean prescribed versus actual values from 0.10 mm (0.39) (P = .007) to 0.29 mm (0.47) (P < .001) from the first premolars to the second molars, respectively. These differences were more pronounced posteriorly. The differences between the predicted and actual arch width values were underexpressed for all teeth except the second molars; a mean underexpression between 0.28 mm (0.92) and 0.60 mm (1.14). The second molars were the only teeth to overexpress with a mean of 0.42 mm (1.02).Overall mean maxillary COW was not predictably controlled with Invisalign. The appliance tends to overexpress the buccal crown tip regardless of prescribed direction, especially posteriorly. Arch expansion was underexpressed at all levels of the arch except for second molars which overexpressed by almost four times. 

The predictability of maxillary curve of Wilson leveling with the Invisalign appliance.

OBJECTIVES
To evaluate the predictability of the Invisalign appliance (Align Technology, Santa Clara, Calif) in leveling the maxillary curve of Spee (COS).


MATERIALS AND METHODS
A retrospective sample of adult subjects treated with the Invisalign appliance between 2013 and 2019 were selected. Patients were treated nonextraction in the maxillary arch and had either Angle Class I or II malocclusions with a minimum of 14 aligners with no bite ramps. Initial, predicted, and actual outcomes were analyzed with Geomagic Control X software (version 2017.0.3; 3D Systems, Cary, NC).


RESULTS
A sample of 53 cases satisfied inclusion/exclusion criteria. Paired t-tests demonstrated a significant difference between mean predicted and actual maxillary COS leveling with a shortfall of 0.11 mm (SD = 0.37; P = .033). Planned intrusion tended to be more accurate posteriorly with an overexpression of 117% for the first molars. Planned extrusion was the least accurate, with the mid-arch demonstrating expressions of -14% to -48%. These teeth intruded despite a prescribed extrusive movement.


CONCLUSIONS
The Invisalign appliance did not accurately predict maxillary COS leveling. Planned intrusive movements were overcorrected, and planned extrusive movements were either undercorrected or resulted in intrusion. This effect was most apparent for the upper first molar, which expressed 117% and -48% of planned intrusion and extrusion, respectively.

The predictability of maxillary curve of Spee leveling with the Invisalign appliance.

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Artificially innervated self-healing foams as synthetic piezo-impedance sensor skins

The predictability of maxillary curve of Wilson leveling with the Invisalign appliance.

The predictability of maxillary curve of Spee leveling with the Invisalign appliance.