Consistent and Reproducible Direct Ink Writing of Eutectic Gallium-Indium for High-Quality Soft Sensors

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

3D Printed Integrated Sensors: From Fabrication to Applications-A Review

The integration of 3D printed sensors into hosting structures has become a growing area of research due to simplified assembly procedures, reduced system complexity, and lower fabrication cost. Embedding 3D printed sensors into structures or bonding the sensors on surfaces are the two techniques for the integration of sensors. This review extensively discusses the fabrication of sensors through different additive manufacturing techniques. Various additive manufacturing techniques dedicated to manufacture sensors as well as their integration techniques during the manufacturing process will be discussed. This review will also discuss the basic sensing mechanisms of integrated sensors and their applications. It has been proven that integrating 3D printed sensors into infrastructures can open new possibilities for research and development in additive manufacturing and sensor materials for smart goods and the Internet of Things.

Direct Ink Writing of Carbon-Doped Polymeric Composite Ink: A Review on Its Requirements and Applications

Direct Ink Writing (DIW) opens new possibilities in three-dimensional (3D) printing of carbon-based polymeric ink. This is due to its ability in design flexibility, structural complexity, and environmental sustainability. This area requires exhaustive study because of its wide application in different manufacturing sectors. The present article is related to the variant emerging 3D printing techniques and DIW of carbonaceous materials. Carbon-based materials, extensively used for various applications in 3D printing, possess impressive chemical stability, strength, and flexible nanostructure. Fine printable inks consist predominantly of uniform solutions of carbon materials, such as graphene, graphene oxide (GO), carbon fibers (CFs), carbon nanotubes (CNTs), and solvents. It also contains compatible polymers and suitable additives. This review article elaborately discusses the fundamental requirements of DIW in structuring carbon-doped polymeric inks viz. ink formulation, required ink rheology, extrusion parameters, print fidelity prediction, layer bonding examination, substrate selection, and curing method to achieve fine functional composites. A detailed description of its application in the fields of electronics, medical, and mechanical segments have also been focused in this study.

Pattern design of a liquid metal-based wearable heater for constant heat generation under biaxial strain

As the wearable heater is increasingly popular due to its versatile applications, there is a growing need to improve the tensile stability of the wearable heater. However, maintaining the stability and precise control of heating in resistive heaters for wearable electronics remains challenging due to multiaxial dynamic deformation with human motion. Here, we propose a pattern study for a circuit control system without complex structure or deep learning of the liquid metal (LM)-based wearable heater. The LM direct ink writing (DIW) method was used to fabricate the wearable heaters in various designs. Through the study about the pattern, the significance of input power per unit area for steady average temperature with tension was proven, and the directionality of the pattern was shown to be a factor that makes feedback control difficult due to the difference in resistance change according to strain direction. For this issue, a wearable heater with the same minimal resistance change regardless of the tension direction was developed using Peano curves and sinuous pattern structure. Lastly, by attaching to a human body model, the wearable heater with the circuit control system shows stable heating (52.64°C, with a standard deviation of 0.91°C) in actual motion.

Shaping a Soft Future: Patterning Liquid Metals

This review highlights the unique techniques for patterning liquid metals containing gallium (e.g., eutectic gallium indium, EGaIn). These techniques are enabled by two unique attributes of these liquids relative to solid metals: 1) The fluidity of the metal allows it to be injected, sprayed, and generally dispensed. 2) The solid native oxide shell allows the metal to adhere to surfaces and be shaped in ways that would normally be prohibited due to surface tension. The ability to shape liquid metals into non-spherical structures such as wires, antennas, and electrodes can enable fluidic metallic conductors for stretchable electronics, soft robotics, e-skins, and wearables. The key properties of these metals with a focus on methods to pattern liquid metals into soft or stretchable devices are summari.

Liquid Metal Patterning and Unique Properties for Next-Generation Soft Electronics

Room-temperature liquid metal (LM)-based electronics is expected to bring advancements in future soft electronics owing to its conductivity, conformability, stretchability, and biocompatibility. However, various difficulties arise when patterning LM because of its rheological features such as fluidity and surface tension. Numerous attempts are made to overcome these difficulties, resulting in various LM-patterning methods. An appropriate choice of patterning method based on comprehensive understanding is necessary to fully utilize the unique properties. Therefore, the authors aim to provide thorough knowledge about patterning methods and unique properties for LM-based future soft electronics. First, essential considerations for LM-patterning are investigated. Then, LM-patterning methods-serial-patterning, parallel-patterning, intermetallic bond-assisted patterning, and molding/microfluidic injection-are categorized and investigated. Finally, perspectives on LM-based soft electronics with unique properties are provided. They include outstanding features of LM such as conformability, biocompatibility, permeability, restorability, and recyclability. Also, they include perspectives on future LM-based soft electronics in various areas such as radio frequency electronics, soft robots, and heterogeneous catalyst. LM-based soft devices are expected to permeate the daily lives if patterning methods and the aforementioned features are analyzed and utilized.

Recent Advances in Multi-Material 3D Printing of Functional Ceramic Devices

In recent years, functional ceramic devices have become smaller, thinner, more refined, and highly integrated, which makes it difficult to realize their rapid prototyping and low-cost manufacturing using traditional processing. As an emerging technology, multi-material 3D printing offers increased complexity and greater freedom in the design of functional ceramic devices because of its unique ability to directly construct arbitrary 3D parts that incorporate multiple material constituents without an intricate process or expensive tools. Here, the latest advances in multi-material 3D printing methods are reviewed, providing a comprehensive study on 3D-printable functional ceramic materials and processes for various functional ceramic devices, including capacitors, multilayer substrates, and microstrip antennas. Furthermore, the key challenges and prospects of multi-material 3D-printed functional ceramic devices are identified, and future directions are discussed.

3D printed anthropomorphic left ventricular myocardial phantom for nuclear medicine imaging applications

Background

Anthropomorphic torso phantoms, including a cardiac insert, are frequently used to investigate the imaging performance of SPECT and PET systems. These phantom solutions are generally featuring a simple anatomical representation of the heart. 3D printing technology paves the way to create cardiac phantoms with more complex volume definition. This study aimed to describe how a fillable left ventricular myocardium (LVm) phantom can be manufactured using geometry extracted from a patient image.

Methods

The LVm of a healthy subject was segmented from ¹⁸F-FDG attenuation corrected PET image set. Two types of phantoms were created and 3D printed using polyethylene terephthalate glycol (PETG) material: one representing the original healthy LVm, and the other mimicking myocardium with a perfusion defect. The accuracy of the LVm phantom production was investigated by high-resolution CT scanning of 3 identical replicas. ^99mTc SPECT acquisitions using local cardiac protocol were performed, without additional scattering media (“in air” measurements) for both phantom types. Furthermore, the healthy LVm phantom was inserted in the commercially available DataSpectrum Anthropomorphic Torso Phantom (“in torso” measurement) and measured with hot background and hot liver insert.

Results

Phantoms were easy to fill without any air-bubbles or leakage, were found to be reproducible and fully compatible with the torso phantom. Seventeen segments polar map analysis of the "in air” measurements revealed that a significant deficit in the distribution appeared where it was expected. 59% of polar map segments had less than 5% deviation for the "in torso” and "in air” measurement comparison. Excluding the deficit area, neither comparison had more than a 12.4% deviation. All the three polar maps showed similar apex and apical region values for all configurations.

Conclusions

Fillable anthropomorphic 3D printed phantom of LVm can be produced with high precision and reproducibility. The 3D printed LVm phantoms were found to be suitable for SPECT image quality tests during different imaging scenarios. The flexibility of the 3D printing process presented in this study provides scalable and anthropomorphic image quality phantoms in nuclear cardiology imaging.

Processing of Self-Healing Polymers for Soft Robotics

Soft robots are, due to their softness, inherently safe and adapt well to unstructured environments. However, they are prone to various damage types. Self-healing polymers address this vulnerability. Self-healing soft robots can recover completely from macroscopic damage, extending their lifetime. For developing healable soft robots, various formative and additive manufacturing methods have been exploited to shape self-healing polymers into complex structures. Additionally, several novel manufacturing techniques, noted as (re)assembly binding techniques that are specific to self-healing polymers, have been created. Herein, the wide variety of processing techniques of self-healing polymers for robotics available in the literature is reviewed, and limitations and opportunities discussed thoroughly. Based on defined requirements for soft robots, these techniques are critically compared and validated. A strong focus is drawn to the reversible covalent and (physico)chemical cross-links present in the self-healing polymers that do not only endow healability to the resulting soft robotic components, but are also beneficial in many manufacturing techniques. They solve current obstacles in soft robots, including the formation of robust multi-material parts, recyclability, and stress relaxation. This review bridges two promising research fields, and guides the reader toward selecting a suitable processing method based on a self-healing polymer and the intended soft robotics application.

Smart Skin: Vision-Based Soft Pressure Sensing System for In-Home Hand Rehabilitation

We introduce a novel in-home hand rehabilitation system for monitoring hand motions and assessing grip forces of stroke patients. The overall system is composed of a sensing device and a computer vision system. The sensing device is a lightweight cylindrical object for easy grip and manipulation, which is covered by a passive sensing layer called "Smart Skin." The Smart Skin is fabricated using soft silicone elastomer, which contains embedded microchannels partially filled with colored fluid. When the Smart Skin is compressed by grip forces, the colored fluid rises and fills in the top surface display area. Then, the computer vision system captures the image of the display area through a red-green-blue camera, detects the length change of the liquid through image processing, and eventually maps the liquid length to the calibrated force for estimating the gripping force. The passive sensing mechanism of the proposed Smart Skin device works in conjunction with a single camera setup, making the system simple and easy to use, while also requiring minimum maintenance effort. Our system, on one hand, aims to support home-based rehabilitation therapy with minimal or no supervision by recording the training process and the force data, which can be automatically conveyed to physical therapists. In contrast, the therapists can also remotely instruct the patients with their training prescriptions through online videos. This study first describes the design, fabrication, and calibration of the Smart Skin, and the algorithm for image processing, and then presents experimental results from the integrated system. The Smart Skin prototype shows a relatively linear relationship between the applied force and the length change of the liquid in the range of 0-35 N. The computer vision system shows the estimation error <4% and a relatively high stability in estimation under different hand motions.

Recent advances in liquid-metal-based wearable electronics and materials

Soft wearable electronics are rapidly developing through exploration of new materials, fabrication approaches, and design concepts. Although there have been many efforts for decades, a resurgence of interest in liquid metals (LMs) for sensing and wiring functional properties of materials in soft wearable electronics has brought great advances in wearable electronics and materials. Various forms of LMs enable many routes to fabricate flexible and stretchable sensors, circuits, and functional wearables with many desirable properties. This review article presents a systematic overview of recent progresses in LM-enabled wearable electronics that have been achieved through material innovations and the discovery of new fabrication approaches and design architectures. We also present applications of wearable LM technologies for physiological sensing, activity tracking, and energy harvesting. Finally, we discuss a perspective on future opportunities and challenges for wearable LM electronics as this field continues to grow.

Advanced Flexible Skin-Like Pressure and Strain Sensors for Human Health Monitoring

Recently, owing to their excellent flexibility and adaptability, skin-like pressure and strain sensors integrated with the human body have the potential for great prospects in healthcare. This review mainly focuses on the representative advances of the flexible pressure and strain sensors for health monitoring in recent years. The review consists of five sections. Firstly, we give a brief introduction of flexible skin-like sensors and their primary demands, and we comprehensively outline the two categories of design strategies for flexible sensors. Secondly, combining the typical sensor structures and their applications in human body monitoring, we summarize the recent development of flexible pressure sensors based on perceptual mechanism, the sensing component, elastic substrate, sensitivity and detection range. Thirdly, the main structure principles and performance characteristic parameters of noteworthy flexible strain sensors are summed up, namely the sensing mechanism, sensitive element, substrate, gauge factor, stretchability, and representative applications for human monitoring. Furthermore, the representations of flexible sensors with the favorable biocompatibility and self-driven properties are introduced. Finally, in conclusion, besides continuously researching how to enhance the flexibility and sensitivity of flexible sensors, their biocompatibility, versatility and durability should also be given sufficient attention, especially for implantable bioelectronics. In addition, the discussion emphasizes the challenges and opportunities of the above highlighted characteristics of novel flexible skin-like sensors.

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

A review of flexible force sensors for human health monitoring

BACKGROUND

In recent years, health monitoring systems (HMS) have aroused great interest due to their broad prospects in preventive medicine. As an important component of HMS, flexible force sensors (FFS) with high flexibility and stretch-ability can monitor vital health parameters and detect physical movements.

AIM OF REVIEW

In this review, the novel materials, the advanced additive manufacturing technologies, the selective sensing mechanisms and typical applications in both wearable and implantable HMS are discussed.

KEY SCIENTIFIC CONCEPTS AND IMPORTANT FINDINGS OF REVIEW

We recognized that the next generation of the FFS will have higher sensitivity, wider linear range as well as better durability, self-power supplied and multifunctional integrated. In conclusion, the FFS will provide powerful socioeconomic benefits and improve people's quality of life in the future.

MakeSense: Automated Sensor Design for Proprioceptive Soft Robots

Soft robots have applications in safe human-robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle.

Direct Wiring of Eutectic Gallium-Indium to a Metal Electrode for Soft Sensor Systems

For wider applications of liquid metal-based stretchable electronics, electrical interface has remained a crucial issue due to its fragile electromechanical stability and complex fabrication steps. In this study, a direct writing-based technique is introduced to form the writing paths of conductive liquid metal (eutectic gallium-indium, eGaIn) and electrical connections to off-the-shelf metal electrodes in a single process. Specifically, by extending eGaIn wires written on a silicone substrate, the eGaIn wires were physically connected to five different metal electrodes, of which stability as an electrical connection was investigated. Among the five different surface materials, the metal electrode finished by electroless nickel immersion gold (ENIG) was reproducible and had low contact resistance without time-dependent variation. In our experiments, it was verified that the electrode part made by an ENIG-finished flexible flat cable (FFC) was mechanically (strain, ≤100%; pressure, ≤600 kPa) and thermally (temperature, ≤180 °C) durable. By modifying the trajectories of eGaIn wires, soft sensor systems composed of 10 sensing units were fabricated and tested to measure finger joint angles and ground reaction forces, respectively. The proposed method enables eGaIn-based soft sensors or circuits to be connected to typical electronic components through FFCs or weldable surfaces, using only off-the-shelf materials without additional mechanical or chemical treatments.

Coaxial Printing of Silicone Elastomer Composite Fibers for Stretchable and Wearable Piezoresistive Sensors

Despite the tremendous efforts dedicated to developing various wearable piezoresistive sensors with sufficient stretchability and high sensitivity, challenges remain pertaining to fabrication scalability, cost, and efficiency. In this study, a facile, scalable, and low-cost coaxial printing strategy is employed to fabricate stretchable and flexible fibers with a core–sheath structure for wearable strain sensors. The highly viscous silica-modified silicone elastomer solution is used to print the insulating sheath layer, and the silicone elastomer solutions containing multi-walled carbon nanotubes (CNTs) are used as the core inks to print the conductive inner layer. With the addition of silica powders as viscosifiers, silica-filled silicone ink (sheath ink) converts to printable ink. The dimensions of the printed coaxial fibers can be flexibly controlled via adjusting the extrusion pressure of the inks. In addition, the electro-mechanical responses of the fiber-shaped strain sensors are investigated. The printed stretchable and wearable fiber-like CNT-based strain sensor exhibits outstanding sensitivities with gauge factors (GFs) of 1.4 to 2.5 × 10⁶, a large stretchability of 150%, and excellent waterproof performance. Furthermore, the sensor can detect a strain of 0.1% and showed stable responses for over 15,000 cycles (high durability). The printed fiber-shaped sensor demonstrated capabilities of detecting and differentiating human joint movements and monitoring balloon inflation. These results obtained demonstrate that the one-step printed fiber-like strain sensors have potential applications in wearable devices, soft robotics, and electronic skins.