Machine learning-guided design and development of multifunctional flexible Ag/poly (amic acid) composites using the differential evolution algorithm

Toward an AI Era: Advances in Electronic Skins

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

A double-crack structure for bionic wearable strain sensors with ultra-high sensitivity and a wide sensing range

Flexible strain sensors are crucial in fully monitoring human motion, and they should have a wide sensing range and ultra-high sensitivity. Herein, inspired by lyriform organs, a flexible strain sensor based on the double-crack structure is designed. An MXene layer and an Au layer with cracks are constructed on both sides of the insulated polydimethylsiloxane (PDMS) film, forming an equivalent parallel circuit that guarantees the integrity of the conductive path under a large strain. The rapid disconnection of the crack junctions causes a significant change in the resistance value. Due to the effect of cracks on the conductive path, the sensitivity of the sensor is largely improved. Benefiting from the double-crack structure, the as-obtained sensor shows ultra-high sensitivity (maximum gauge factor of up to 14 373.6), a wide working range (up to 21%), a fast response time (183 ms) and excellent dynamical stability (almost no performance loss after 1000 stretching cycles and different frequency cycles). In practical applications, the sensor is applied to different parts of the human body to sense the deformation of the skin, demonstrating its great potential application value in human physiological detection and the human-machine interaction. This study can provide new ideas for preparing high-performance flexible strain sensors.

Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding

Skin-like flexible sensors play vital roles in healthcare and human-machine interactions. However, general goals focus on pursuing intrinsic static and dynamic performance of skin-like sensors themselves accompanied with diverse trial-and-error attempts. Such a forward strategy almost isolates the design of sensors from resulting applications. Here, a machine learning (ML)-guided design of flexible tactile sensor system is reported, enabling a high classification accuracy (≈99.58%) of tactile perception in six dynamic touch modalities. Different from the intuition-driven sensor design, such ML-guided performance optimization is realized by introducing a support vector machine-based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. This inverse design merges the statistical learning criteria into the design phase of sensing hardware, bridging the gap between the device structures and algorithms. Using the optimized tactile sensor, the high-quality recognizable signals in handwriting applications are obtained. Besides, with the additional data processing, a robot hand assembled with the sensor is able to complete real-time touch-decoding of an 11-digit braille phone number with high accuracy.

Machine Learning-Enhanced Flexible Mechanical Sensing

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction

Letter handwriting, especially stroke correction, is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the public. In this study, a biodegradable and conductive carboxymethyl chitosan-silk fibroin (CSF) film is prepared to design wearable triboelectric nanogenerator (denoted as CSF-TENG), which outputs of Voc ≈ 165 V, Isc ≈ 1.4 μA, and Qsc ≈ 72 mW cm-2. Further, in vitro biodegradation of CSF film is performed through trypsin and lysozyme. The results show that trypsin and lysozyme have stable and favorable biodegradation properties, removing 63.1% of CSF film after degrading for 11 days. Further, the CSF-TENG-based human-machine interface (HMI) is designed to promptly track writing steps and access the accuracy of letters, resulting in a straightforward communication media of human and machine. The CSF-TENG-based HMI can automatically recognize and correct three representative letters (F, H, and K), which is benefited by HMI system for data processing and analysis. The CSF-TENG-based HMI can make decisions for the next stroke, highlighting the stroke in advance by replacing it with red, which can be a candidate for calligraphy practice and correction. Finally, various demonstrations are done in real-time to achieve virtual and real-world controls including writing, vehicle movements, and healthcare.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis

Artificial intelligence (AI) is an emerging technology with great potential, and its robust calculation and analysis capabilities are unmatched by traditional calculation tools. With the promotion of deep learning and open-source platforms, the threshold of AI has also become lower. Combining artificial intelligence with traditional fields to create new fields of high research and application value has become a trend. AI has been involved in many disciplines, such as medicine, materials, energy, and economics. The development of AI requires the support of many kinds of data, and microfluidic systems can often mine object data on a large scale to support AI. Due to the excellent synergy between the two technologies, excellent research results have emerged in many fields. In this review, we briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis. Finally, we discuss the development trend of the combination of the two technologies.

In situ fabrication of silver/polyimide composite films with enhanced heat dissipation

In this study, silver/polyimide (Ag/PI) composite films with enhanced heat dissipation properties were prepared. Ag was formed in situ by reducing AgNO₃ at various locations according to the reduction method. Two different types of soluble PIs capable of solution processing were used, namely Matrimid and hydroxy polyimide (HPI). Unlike Matrimid with bulky substituents, HPI with polar hydroxy groups formed ion-dipole interactions with Ag ions to form Ag particles with uniform size distribution. The location and distribution of Ag particles affect the heat emission characteristics of the composite films, resulting in better heat dissipation properties with the thermally and photochemically reduced Ag/HPI films having more Ag particles distributed inside of the films than the chemically reduced films.

A Cyclic BMP-2 Peptide Upregulates BMP-2 Protein-Induced Cell Signaling in Myogenic Cells

In the current study, we designed four cyclic peptide analogues by incorporating two cysteine residues in a BMP-2 linear knuckle epitope in such a way that the active region of the peptide could be either inside or outside the cyclic ring. Bone morphogenetic protein receptor BMPRII was immobilized on the chip surface, and the interaction of the linear and cyclic peptide analogues was studied using surface plasmon resonance (SPR). From the affinity data, the peptides with an active region inside the cyclic ring had a higher binding affinity in comparison to the other peptides. To confirm that our affinity data are in line in vitro, we studied the expression levels of RUNX2 (runt-related transcription factor) and conducted an osteogenic marker alkaline phosphatase (ALP) assay and staining. Based on the affinity data and the in vitro experiments, peptide P-05 could be a suitable candidate for osteogenesis, with higher binding affinity and increased RUNX2 and ALP expression in comparison to the linear peptides.