In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose-Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition

Self-powered temperature and humidity sensing platform based on Ag2S/poly(5-carboxyindole)/hydroxyethyl cellulose nanocomposite

A self-powered dual-function temperature and humidity sensing platform is constructed based on Ag2S/poly(5-carboxyindole)/hydroxyethyl cellulose composite material (Ag2S/P5ICA/HEC). The Ag2S nanomaterial with reticular porous structure is successfully synthesized via a solvothermal method for use as electrodes in supercapacitors and temperature sensors. Then, a highly sensitive Ag2S/P5ICA/HEC-based humidity sensor is successfully prepared by drop-coating the surface of Ag2S material with P5ICA/HEC composite. The analysis results reveal that the Ag2S electrode material shows good electrochemical properties with high specific capacitance (6.11F/cm3 at 0.04 A/cm3 current density) and cycle stability. Moreover, the symmetric supercapacitor constructed based on the Ag2S electrode material also exhibits high energy storage capacity and long term cycling stability. Additionally, the Ag2S material showed temperature sensitivity, with its resistance values positively correlated with temperature in a linear relationship (R2 = 0.992). The rough and reticular porous structure of Ag2S/P5ICA/HEC provides large surface area for adsorbing more water molecules, thereby increasing the sensitivity of humidity detection. The analysis results show that the sensor is sensitive to humidity at room temperature. In the detection range of 20-80 % relative humidity (RH), the natural logarithm of the resistance response signal shows a good linear relationship with humidity (R2 = 0.996). The sensor exhibits a sensitivity of 0.078 Ω / % RH, coupled with excellent repeatability and stability. Finally, a self-powered temperature and humidity sensing platform is constructed using a symmetric supercapacitor with Ag2S electrodes as the energy storage component, and Ag2S/P5ICA/HEC composite material as the temperature and humidity sensing component. This platform exhibits excellent temperature and humidity detection capabilities and holds potential for applications in areas such as pharmaceutical storage, food storage, and agriculture.

Giant Pyroelectric Figure of Merits in Strain-Engineered Ferroelectric 2D-SnSe Layered Nanosheets: An Efficient Transient Thermal Energy Harvester

Two-dimensional (2D) layered semiconductors reveal boundless potential in next-generation ferro-, piezo-, and pyroelectric property-based self-powered devices. In this scenario, we report the experimental observations, which are consistent with the first-principles calculations of the out-of-plane ferro- and piezoelectricity in 2D tin selenide (SnSe) of van der Waals-bonded monolayers. It has a strain-engineered, synergetic trigonal crystal structure. Thin 2D-SnSe nanosheets of a few monolayers are exfoliated from bulk orthorhombic SnSe, which facilitates enormous potential of out-of-plane pyroelectric-aided transient thermal energy harvesting. In particular, phase and amplitude hysteresis of piezoresponse force microscopy affirms that the van der Waals-bonded trilayer SnSe nanosheets exhibit profound ferro- and piezoelectric (d33 ∼ 3.45 pm/V) responses. A robust pyroelectric response persists with a figure of merit (Fv ∼ 8 m2 C-1 and FD ∼ 132 × 10-6 Pa-1/2), an order higher than the commercially benchmark bulk pyroelectrics. Hence, 2D-SnSe layers could be promising materials for pyroelectric-based waste heat scavenging, infrared imaging, and detectors for various applications.

Co-Sensitization of ZnCo2O4 Hierarchical Microspheres with MoO3 and Ag Nanoparticles for Chemiresistive Detection of Acetone

Recently, the detection of acetone by using a gas sensor fabricated with metal oxide semiconductors has stimulated intense research enthusiasm, owing to its promising application in the noninvasive diagnosis of diabetes by monitoring human exhaled breath. In this work, a potential acetone sensor was fabricated by comodification of the nanosheets-assembled ZnCo2O4 hierarchical microspheres (HMSs) with MoO3 and Ag nanoparticles (NPs). The ZnCo2O4 HMSs were synthesized via a solvothermal route and then sequentially modified with MoO3 and Ag NPs via immersion, calcination, and photoreduction routes. The results of gas sensing measurement indicated that after modification with MoO3 and Ag, a continuous improvement of acetone sensitivity was realized on the ZnCo2O4 sensor. At the optimal working temperature of 260 °C, the response of the best Ag/MoO3/ZnCo2O4 sensor to 50 ppm of acetone was 7.23, which was about 3.3 and 1.4 times higher than that of ZnCo2O4 and MoO3/ZnCo2O4, respectively. Apart from the higher response, the selectivity and response speed of the Ag/MoO3/ZnCo2O4 sensor were also improved. The results of density functional theory calculations showed that the enhanced absorbability of ZnCo2O4 for acetone after modification with MoO3 and Ag should be responsible for the improved acetone sensitivity and selectivity of the Ag/MoO3/ZnCo2O4 sensor.

Recent advancements in wearable sensors: integration with machine learning for human-machine interaction

Wearable sensors have emerged as a transformative technology, enabling real-time monitoring and advanced functionality in various fields, including healthcare, human-machine interaction, and environmental sensing. This review provides a comprehensive overview of the latest advancements in wearable sensor technologies, focusing on innovations in sensor design, material flexibility, and integration with machine learning. We explore the feasibility of wearable electronics in achieving high-performance, flexible devices and discuss their potential to enhance human-machine interactions through intelligent data processing and decision-making. The combination of wearable electronics and machine learning offers immense potential for applications requiring real-time responsiveness and advanced analytics. By analyzing recent developments of sensors, this review aims to inspire further innovations in this rapidly evolving field, paving the way for the next generation of wearable technologies.

Prognosis of Cardiovascular Conditions Noninvasively Using Printable Elastomeric Electronic Skin

Lack of timely prognosis of cardiovascular condition (CVC) is resulting in increased mortality across the globe. Currently, available techniques are confined to medical facilities and need the intervention of specialists. Frequently, this impedes timely treatment, driven by socioeconomic factors. Consequently, the disease transcends toward incurable complications. In such a scenario, point-of-care diagnostic tools can help with prognosis at an early stage. Albeit there are such tools available, it is imperative to develop affordably in uncomplicated manufacturing techniques and should have simple readout and analysis modules for monitoring CVC. Accordingly, the solvent-free manufacturing of stencil printable liquid elastomer-carbon nanotube electronic skin-based strain sensor, capable of accurately detecting pulse (at different positions) and other parameters like augmentation index and stiffness index of artery related to the CVC, is reported. The Poincare plot, derived from the recorded data, measures heart rate variability, a key indicator linked to mortality. Thanks to the staggering linearity, gauge factor of 234.26, fast response time of 85 ms (measured from pulse data), and cyclic stability (over 500 cycles), assist in the ease of detection of vital parameters. Furthermore, the sensor patch demonstrates its capability to acquire pulse waves under different real-time artery conditions using cuff-based pressure applications.

Self-healing PVA/Chitosan/MXene triple network hydrogel for strain and temperature sensors

Conductive hydrogels have attracted intensive attention for their promising applications in flexible electronics, sensors, and electronic skins. However, extremely poor adaptability under cold or dry environmental conditions along with inferior repairability seriously hinders the development of hydrogels in wearable electronics. Here, a triple network conductive hydrogel (PBCPA-MXene) was prepared by proportionally mixing polyvinyl alcohol (PVA), borax, chitosan (CS), phytic acid (PA), and MXene. The prepared triple network hydrogels composed of robust chitosan polysaccharide as the first network, tough PVA biopolymer gel as the second network, and MXene nanosheets as the third network. Facilitated by triple networks, multiple hydrogen bonds, and electrostatic interactions of CS and PA, the obtained hydrogels not only exhibited outstanding mechanical properties (tensile strain of ∼1580 %, stress of ∼280 kPa) and electrical properties (∼ 2.72 S/m), but also possessed excellent self-healing, self-adhesion, anti-freezing and anti-drying properties. This work presents a strategy for the development of biopolysaccharide hydrogels for applications in the field of sensors.

Engineering an Ultrafast Ambient NO2 Gas Sensor Using Cotton-Modified LaFeO3/MXene Composites

This work presents a room-temperature (RT) NO2 gas sensor based on cotton-modified LaFeO3 (CLFO) combined with MXene. LaFeO3 (LFO), CLFO, and CLFO/MXene composites were synthesized via a hydrothermal method. The fabricated sensor, utilizing MXene/CLFO, exhibits a p-type behavior and fully recoverable sensing capabilities for low concentrations of NO2, achieving a higher response of 14.2 times at 5 ppm. The sensor demonstrates excellent performance with a response time of 2.7 s and a recovery time of 6.2 s, along with notable stability. The sensor's sensitivity is attributed to gas interactions on the material's surface, adsorption energy, and charge-transfer mechanisms. Techniques such as in situ FTIR (Fourier transform infrared) spectroscopy, GC-MS (gas chromatography-mass spectroscopy), and near-ambient pressure X-ray photoelectron spectroscopy were employed to verify gas interactions and their byproducts. Additionally, finite-difference time-domain simulations were used to model the electromagnetic field distribution and provide insight into the interaction between NO2 molecules and the sensor surface at the nanoscale. A prototype wireless IoT (Internet of Things)-based NO2 gas leakage detection system was also developed, showcasing the sensor's practical application. This study offers valuable insight into the development of room-temperature NO2 sensors with a low detection limit.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Ultrasensitive Flexible Strain Sensor Made with Carboxymethyl-Cellulose-Anchored Carbon Nanotubes/MXene for Machine-Learning-Assisted Handwriting Recognition

The combination of wearable sensors with machine learning enables intelligent perception in human-machine interaction and healthcare, but achieving high sensitivity and a wide working range in flexible strain sensors for signal acquisition and accurate recognition remains challenging. Herein, we introduced carboxymethyl cellulose (CMC) into a carbon nanotubes (CNTs)/MXene hybrid network, forming tight anchoring among the conductive materials and, thus, bringing enhanced interaction. The silicone-rubber-encapsulated CMC-anchored CNTs/MXene (CCM) strain sensor exhibits an excellent sensitivity (maximum gauge factor up to 71 294), wide working range (200%), ultralow detection limit (0.05%), and outstanding durability (over 10 000 cycles), which is superior to most of the recently reported counterparts also based on a conductive composite film. Moreover, the sensor achieves seamless integration with human skin with the help of a poly(acrylic acid) adhesive layer, successfully obtaining stable and clear waveforms with meaningful profiles from the human body. On this basis, we proposed and realized a novel in-air handwriting recognition method via extracting multiple features of high-quality strain signals assisted by deep neural networks, achieving a high classification accuracy of 98.00 and 94.85% for Arabic numerals and letters, respectively. Our work provides an effective approach for significantly improving strain sensing performance, thereby facilitating innovative applications of flexible sensors.

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.

MXenes for Wearable Physical Sensors toward Smart Healthcare

The gradual rise of personal healthcare awareness is accelerating the deployment of wearable sensors, whose ability of acquiring physiological vital signs depends on sensing materials. MXenes have distinct chemical and physical superiorities over other 2D nanomaterials for wearable sensors. This review presents a comprehensive summary of the latest advancements in MXenes-based materials for wearable physical sensors. It begins with an introduction to special structural features of MXenes for sensing performance, followed by an in-depth exploration of versatile functionalities. A detailed description of different sensing mechanisms is also included to illustrate the contribution of MXenes to the sensing performance and its improvement. In addition, the real-world applications of MXenes-based physical sensors for monitoring different physiological signs are included as well. The remaining challenges of MXenes-based materials for wearable physical sensors and their promising opportunities are finally narrated, in conjunction with a prospective for future development.

Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process.

Bioinspired High-Linearity, Wide-Sensing-Range Flexible Stretchable Bioelectronics Based on MWCNTs/GR/Nd2Fe14B/PDMS Nanocomposites for Human-Computer Interaction and Biomechanics Detection

In recent years, flexible and stretchable strain sensors have emerged as a prominent area of research, primarily due to their remarkable stretchability and extremely low strain detection threshold. Nevertheless, the advancement of sensors is currently constrained by issues such as complexity, high costs, and limited durability. To tackle the aforementioned issues, this study introduces a lepidophyte-inspired flexible, stretchable strain sensor (LIFSSS). The stretchable bioelectronics composites were composed of multiwalled carbon nanotubes, graphene, neodymium iron boron, and polydimethylsiloxane. Unique biolepidophyted microstructures and magnetic conductive nanocomposites interact with each other through synergistic interactions, resulting in the effective detection of tensile strain and magnetic excitation. The LIFSSS exhibits a 170% tensile range, a linearity of 0.99 in 50-170% strain (0.96 for full-scale range), and a fine durability of 7000 cycles at 110% tensile range. The sensor accurately detects variations in linear tensile force, human movement, and microexpressions. Moreover, LIFSSS demonstrates enhanced efficacy in sign language recognition for individuals with hearing impairments and magnetic grasping for robotic manipulators. Hence, the LIFSSS proposed in this study shows potential applications in various fields, including bioelectronics, electronic skin, and physiological activity monitoring.

Machine Learning Assisted Electronic/Ionic Skin Recognition of Thermal Stimuli and Mechanical Deformation for Soft Robots

Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real-world applications. The development of electronic skin (E-skin) perception systems is crucial for the advancement of soft robots. However, achieving both exteroceptive and proprioceptive capabilities in E-skins, particularly in terms of decoupling and classifying sensing signals, remains a challenge. This study presents an E-skin with mixed electronic and ionic conductivity that can simultaneously achieve exteroceptive and proprioceptive, based on the resistance response of conductive hydrogels. It is integrated with soft robots to enable state perception, with the sensed signals further decoded using the machine learning model of decision trees and random forest algorithms. The results demonstrate that the newly developed hydrogel sensing system can accurately predict attitude changes in soft robots when subjected to varying degrees of pressing, hot pressing, bending, twisting, and stretching. These findings that multifunctional hydrogels combine with machine learning to decode signals may serve as a basis for improving the sensing capabilities of intelligent soft robots in future advancements.

Multispecies-coadsorption-induced rapid preparation of graphene glass fiber fabric and applications in flexible pressure sensor

Direct chemical vapor deposition (CVD) growth of graphene on dielectric/insulating materials is a promising strategy for subsequent transfer-free applications of graphene. However, graphene growth on noncatalytic substrates is faced with thorny issues, especially the limited growth rate, which severely hinders mass production and practical applications. Herein, graphene glass fiber fabric (GGFF) is developed by graphene CVD growth on glass fiber fabric. Dichloromethane is applied as a carbon precursor to accelerate graphene growth, which has a low decomposition energy barrier, and more importantly, the produced high-electronegativity Cl radical can enhance adsorption of active carbon species by Cl-CH2 coadsorption and facilitate H detachment from graphene edges. Consequently, the growth rate is increased by ~3 orders of magnitude and carbon utilization by ~960-fold, compared with conventional methane precursor. The advantageous hierarchical conductive configuration of lightweight, flexible GGFF makes it an ultrasensitive pressure sensor for human motion and physiological monitoring, such as pulse and vocal signals.

Stretchable MXene/Carbon Nanotube Bilayer Strain Sensors with Tunable Sensitivity and Working Ranges

Stretchable strain sensors have gained increasing popularity as wearable devices to convert mechanical deformation of the human body into electrical signals. Two-dimensional transition metal carbides (Ti3C2Tx MXene) are promising candidates to achieve excellent sensitivity. However, MXene films have been limited in operating strain ranges due to rapid crack propagation during stretching. In this regard, this study reports MXene/carbon nanotube bilayer films with tunable sensitivity and working ranges. The device is fabricated using a scalable process involving spray deposition of well-dispersed nanomaterial inks. The bilayer sensor's high sensitivity is attributed to the cracks that form in the MXene film, while the compliant carbon nanotube layer extends the working range by maintaining conductive pathways. Moreover, the response of the sensor is easily controlled by tuning the MXene loading, achieving a gauge factor of 9039 within 15% strain at 1.92 mg/cm2 and a gauge factor of 1443 within 108% strain at 0.55 mg/cm2. These tailored properties can precisely match the operation requirements during the wearable application, providing accurate monitoring of various body movements and physiological activities. Additionally, a smart glove with multiple integrated strain sensors is demonstrated as a human-machine interface for the real-time recognition of hand gestures based on a machine-learning algorithm. The design strategy presented here provides a convenient avenue to modulate strain sensors for targeted applications.

MXene Key Composites: A New Arena for Gas Sensors

With the development of science and technology, the scale of industrial production continues to grow, and the types and quantities of gas raw materials used in industrial production and produced during the production process are also constantly increasing. These gases include flammable and explosive gases, and even contain toxic gases. Therefore, it is very important and necessary for gas sensors to detect and monitor these gases quickly and accurately. In recent years, a new two-dimensional material called MXene has attracted widespread attention in various applications. Their abundant surface functional groups and sites, excellent current conductivity, tunable surface chemistry, and outstanding stability make them promising for gas sensor applications. Since the birth of MXene materials, researchers have utilized the efficient and convenient solution etching preparation, high flexibility, and easily functionalize MXene with other materials to prepare composites for gas sensing. This has opened a new chapter in high-performance gas sensing materials and provided a new approach for advanced sensor research. However, previous reviews on MXene-based composite materials in gas sensing only focused on the performance of gas sensing, without systematically explaining the gas sensing mechanisms generated by different gases, as well as summarizing and predicting the advantages and disadvantages of MXene-based composite materials. This article reviews the latest progress in the application of MXene-based composite materials in gas sensing. Firstly, a brief summary was given of the commonly used methods for preparing gas sensing device structures, followed by an introduction to the key attributes of MXene related to gas sensing performance. This article focuses on the performance of MXene-based composite materials used for gas sensing, such as MXene/graphene, MXene/Metal oxide, MXene/Transition metal sulfides (TMDs), MXene/Metal-organic framework (MOF), MXene/Polymer. It summarizes the advantages and disadvantages of MXene composite materials with different composites and discusses the possible gas sensing mechanisms of MXene-based composite materials for different gases. Finally, future directions and inroads of MXenes-based composites in gas sensing are presented and discussed.

Highly Sensitive Iontronic Pressure Sensor with Side-by-Side Package Based on Alveoli and Arch Structure

Flexible pressure sensors play a significant role in wearable devices and electronic skin. Iontronic pressure sensors with high sensitivity, wide measurement range, and high resolution can meet requirements. Based on the significant deformation characteristics of alveoli to improve compressibility, and the ability of the arch to disperse vertical pressure into horizontal thrust to increase contact area, a graded hollow ball arch (GHBA) microstructure is proposed, greatly improving sensitivity. The fabrication of GHBA ingeniously employs a double-sided structure. One side uses mold casting to create convex structures, while the other utilizes the evaporation of moisture during the curing process to form concave structures. At the same time, a novel side-by-side package structure is proposed, ensuring pressure on flexible substrate is maximally transferred to the GHBA microstructure. Within the range of 0.2 Pa-300 kPa, the iontronic pressure sensor achieves a maximum sensitivity of 10 420.8 kPa-1, pressure resolution of 0.1% under the pressure of 100 kPa, and rapid response/recovery time of 40/35 ms. In wearable devices, it is capable of monitoring dumbbell curl exercises and wirelessly correcting sitting positions. In electronic skin, it can non-contactly detect the location of the wind source and achieve object classification prediction when combined with the CNN model.

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

The purpose of this study is to deeply understand the psychological state of boxers before the competition, and explore an efficient boxing action classification and recognition model supported by artificial intelligence (AI) technology through these psychological characteristics. Firstly, this study systematically measures the key psychological dimensions of boxers, such as anxiety level, self-confidence, team identity, and opponent attitude, through psychological scale survey to obtain detailed psychological data. Then, based on these data, this study innovatively constructs a boxing action classification and recognition model based on BERT fusion 3D-ResNet, which not only comprehensively considers psychological information, but also carefully considers action characteristics to improve the classification accuracy of boxing actions. The performance evaluation shows that the model proposed in this study is significantly superior to the traditional model in terms of loss value, accuracy and F1 value, and the accuracy reaches 96.86%. Therefore, through the comprehensive application of psychology and deep learning, this study successfully constructs a boxing action classification and recognition model that can fully understand the psychological state of boxers, which provides strong support for the psychological training and action classification of boxers.

Performance analysis of solution-processed nanosheet strain sensors-a systematic review of graphene and MXene wearable devices

Nanotechnology has led to the realisation of many potentialInternet of Thingsdevices that can be transformative with regards to future healthcare development. However, there is an over saturation of wearable sensor review articles that essentially quote paper abstracts without critically assessing the works. Reported metrics in many cases cannot be taken at face value, with researchers overly fixated on large gauge factors. These facts hurt the usefulness of such articles and the very nature of the research area, unintentionally misleading those hoping to progress the field. Graphene and MXenes are arguably the most exciting organic and inorganic nanomaterials for polymer nanocomposite strain sensing applications respectively. Due to their combination of cost-efficient, scalable production and device performances, their potential commercial usage is very promising. Here, we explain the methods for colloidal nanosheets suspension creation and the mechanisms, metrics and models which govern the electromechanical properties of the polymer-based nanocomposites they form. Furthermore, the many fabrication procedures applied to make these nanosheet-based sensing devices are discussed. With the performances of 70 different nanocomposite systems from recent (post 2020) publications critically assessed. From the evaluation of these works using universal modelling, the prospects of the field are considered. Finally, we argue that the realisation of commercial nanocomposite devices may in fact have a negative effect on the global climate crisis if current research trends do not change.

MXene-Based Chemo-Sensors and Other Sensing Devices

MXenes have received worldwide attention across various scientific and technological fields since the first report of the synthesis of Ti3C2 nanostructures in 2011. The unique characteristics of MXenes, such as superior mechanical strength and flexibility, liquid-phase processability, tunable surface functionality, high electrical conductivity, and the ability to customize their properties, have led to the widespread development and exploration of their applications in energy storage, electronics, biomedicine, catalysis, and environmental technologies. The significant growth in publications related to MXenes over the past decade highlights the extensive research interest in this material. One area that has a great potential for improvement through the integration of MXenes is sensor design. Strain sensors, temperature sensors, pressure sensors, biosensors (both optical and electrochemical), gas sensors, and environmental pollution sensors targeted at volatile organic compounds (VOCs) could all gain numerous improvements from the inclusion of MXenes. This report delves into the current research landscape, exploring the advancements in MXene-based chemo-sensor technologies and examining potential future applications across diverse sensor types.

2D Materials Nanoarchitectonics for 3D Structures/Functions

It has become clear that superior material functions are derived from precisely controlled nanostructures. This has been greatly accelerated by the development of nanotechnology. The next step is to assemble materials with knowledge of their nano-level structures. This task is assigned to the post-nanotechnology concept of nanoarchitectonics. However, nanoarchitectonics, which creates intricate three-dimensional functional structures, is not always easy. Two-dimensional nanoarchitectonics based on reactions and arrangements at the surface may be an easier target to tackle. A better methodology would be to define a two-dimensional structure and then develop it into a three-dimensional structure and function. According to these backgrounds, this review paper is organized as follows. The introduction is followed by a summary of the three issues; (i) 2D to 3D dynamic structure control: liquid crystal commanded by the surface, (ii) 2D to 3D rational construction: a metal-organic framework (MOF) and a covalent organic framework (COF); (iii) 2D to 3D functional amplification: cells regulated by the surface. In addition, this review summarizes the important aspects of the ultimate three-dimensional nanoarchitectonics as a perspective. The goal of this paper is to establish an integrated concept of functional material creation by reconsidering various reported cases from the viewpoint of nanoarchitectonics, where nanoarchitectonics can be regarded as a method for everything in materials science.

Ionic conductive konjac glucomannan/liquid crystal cellulose composite hydrogels with dual sensing of photo- and electro-signals capacities as wearable strain sensors

The ionic conductive hydrogel-based sensor exhibits wide applications in wearable electronic devices. However, the strength and ductility trade-off, multimodal requirements, and water-soluble polymer alternatives are significant challenges for the hydrogel-based sensor. Herein, a stretchable and conductive hydrogel is developed with a double network formed by incorporating polyacrylamide and ionic liquid into the konjac glucomannan network. The hydrogel displays significantly enhanced mechanical properties, and good tear/puncture resistance owing to the existence of covalent and non-covalent interactions. In addition, by the introduction of nematic liquid crystal hydroxypropyl cellulose, the hydrogel/cellulose-based strain sensor demonstrates excellent sensing performance in monitoring human motions and writing recognition ability with optical and electrical bimodal sensing response. This work provides new insights to further expand the options of hydrogel-based sensor matrix and to construct bimodal sensors.

Materials Nanoarchitectonics at Dynamic Interfaces: Structure Formation and Functional Manipulation

The next step in nanotechnology is to establish a methodology to assemble new functional materials based on the knowledge of nanotechnology. This task is undertaken by nanoarchitectonics. In nanoarchitectonics, we architect functional material systems from nanounits such as atoms, molecules, and nanomaterials. In terms of the hierarchy of the structure and the harmonization of the function, the material created by nanoarchitectonics has similar characteristics to the organization of the functional structure in biosystems. Looking at actual biofunctional systems, dynamic properties and interfacial environments are key. In other words, nanoarchitectonics at dynamic interfaces is important for the production of bio-like highly functional materials systems. In this review paper, nanoarchitectonics at dynamic interfaces will be discussed, looking at recent typical examples. In particular, the basic topics of "molecular manipulation, arrangement, and assembly" and "material production" will be discussed in the first two sections. Then, in the following section, "fullerene assembly: from zero-dimensional unit to advanced materials", we will discuss how various functional structures can be created from the very basic nanounit, the fullerene. The above examples demonstrate the versatile possibilities of architectonics at dynamic interfaces. In the last section, these tendencies will be summarized, and future directions will be discussed.

Flexible piezo-resistive strain sensors using all-polydimethylsiloxane based hybrid nanocomposites for wearable electronics

We report flexible piezo-resistive strain sensors composed of silver nanoparticle (Ag NP), graphene nanoplatelet (GNP), and multi walled carbon nanotube (MWCNT)-based ternary conductive hybrid nanocomposites as an active sensing layer fabricated using a simple solution processing method on flexible polydimethylsiloxane (PDMS) substrates. The electrical characteristics have been studied in PDMS-based flexible devices having three different kinds of structures, namely Ag NPs/MWCNT/PDMS, GNP/PDMS and Ag NPs/GNP/PDMS. The microscopic analysis of the hybrid nanocomposites is undertaken using field emission scanning electron microscopy. The diameter of the CNTs is found to be in the range of 20-40 nm, whereas the length is determined to be 100-800 nm. The average diameter and length of the GNPs are observed to be 30-50 nm and 100-500 nm, respectively. The crystallite size of the silver nanoparticles in the Ag NPs/MWCNT/PDMS and Ag NPs/GNP/PDMS-based nanocomposites is determined to be 22.8 nm and 29.1 nm, respectively. The prepared sample of Ag NPs shows four distinct peaks in the X-ray diffraction pattern, which correspond to the (111), (200), (220), and (311) face-centered cubic (FCC) crystalline planes. Raman spectroscopy is undertaken to study the fundamental physical properties and chemical analysis of the nanocomposites. Ag NPs/GNP/PDMS-based sensors exhibit superior performance in terms of sensitivity, response and recovery time during breathing/unbreathing analysis. The large surface area of the Ag NPs and GNPs promotes uniform distribution of Ag NPs to fill into the porous GNP surface, thereby facilitating high contact area along with better electron transport in the Ag NPs/GNP/PDMS hybrid nanocomposite-based sensors. The gauge factor (GF), response and recovery time of the Ag NPs/GNP/PDMS hybrid nanocomposite-based sensors are determined to be 221, 130 ms and 119 ms, respectively. The ternary conductive nanocomposite-based sensors are free from the drawbacks of binary nanocomposite-based sensors where the high percolation threshold and poor mechanical behaviour lead to the degradation of the device performance.

Novel Deposition Method of Crosslinked Polyethylene Thin Film for Low-Refractive-Index Mid-Infrared Optical Coatings

Mid-infrared optics require optical coatings composed of high- and low-refractive-index dielectric layers for the design of optical mirrors, filters, and anti-reflection coatings. However, there are not many technologies for depositing a material with a refractive index of less than 2 and a low loss in the mid-infrared region. Here, we present a unique deposition method of crosslinked polyethylene thin film for mid-IR optical filter design. Polyethylene has a refractive index of 1.52 in the mid-infrared region and a small number of absorption peaks, so it is useful for making optical filters in the mid-infrared region. Only 1 keV of energy is required to crosslink the entire film by irradiating an electron beam while depositing polyethylene. In addition, crosslinked polyethylene thin film has high mechanical strength, so there is no cracking or peeling when used with germanium. This allows for the use of crosslinked polyethylene as a low refractive index for mid-infrared optical coating.

Highly Sensitive Flexible Sensors for Human Activity Monitoring and Personal Healthcare

Flexible sensors are capable of converting multiple human physiological signals into electrical signals for various applications in clinical diagnostics, athletics, and human-machine interaction. High-performance flexible strain sensors are particularly desirable for sensitive, reliable, and long-term monitoring, but current applications are still constrained due to high response threshold, low recoverability properties, and complex preparation methods. In this study, we present a stable and flexible strain sensor by a cost-effective self-assemble approach that demonstrates remarkable sensitivity (2169), ultrafast response and recovery time (112 ms), and wide dynamic response range (0-50%), as confirmed in human pulse and human-computer interaction. These excellent performances can be attributed to the design of a Polydimethylsiloxane (PDMS) substrate integrated with multiwalled carbon nanotubes (MWCNT) and graphene nanosheets (GNFs), which results in high electrical conductivity. The MWCNT serves as a bridge, connecting the GNFs to create an efficient conductive path even under a strain of 50%. We also demonstrate the strain sensor's capability in weak physiological signal pulse measurement and excellent resistance to mechanical fatigue. Moreover, the sensor shows diverse sensitivities in various tensile states with different signal patterns, making it highly suitable for full-range human monitoring and flexible wearable systems.

Cellulose-Based Conductive Materials for Energy and Sensing Applications

Cellulose-based conductive materials (CCMs) have emerged as a promising class of materials with various applications in energy and sensing. This review provides a comprehensive overview of the synthesis methods and properties of CCMs and their applications in batteries, supercapacitors, chemical sensors, biosensors, and mechanical sensors. Derived from renewable resources, cellulose serves as a scaffold for integrating conductive additives such as carbon nanotubes (CNTs), graphene, metal particles, metal-organic frameworks (MOFs), carbides and nitrides of transition metals (MXene), and conductive polymers. This combination results in materials with excellent electrical conductivity while retaining the eco-friendliness and biocompatibility of cellulose. In the field of energy storage, CCMs show great potential for batteries and supercapacitors due to their high surface area, excellent mechanical strength, tunable chemistry, and high porosity. Their flexibility makes them ideal for wearable and flexible electronics, contributing to advances in portable energy storage and electronic integration into various substrates. In addition, CCMs play a key role in sensing applications. Their biocompatibility allows for the development of implantable biosensors and biodegradable environmental sensors to meet the growing demand for health and environmental monitoring. Looking to the future, this review emphasizes the need for scalable synthetic methods, improved mechanical and thermal properties, and exploration of novel cellulose sources and modifications. Continued innovation in CCMs promises to revolutionize sustainable energy storage and sensing technologies, providing environmentally friendly solutions to pressing global challenges.

Highly Sensitive Fiber Pressure Sensors over a Wide Pressure Range Enabled by Resistive-Capacitive Hybrid Response

Soft capacitive pressure sensors with high performance are becoming increasingly in demand in the emerging flexible wearable field. While capacitive fiber pressure sensors have achieved high sensitivity, their sensitivity range is limited to low-pressure levels. As fiber sensors typically require preloading and fixation, this narrow range of high sensitivity poses a challenge for practical applications. To overcome this limitation, the study proposes resistive-capacitive hybrid response fiber pressure sensors (HFPSs) with three-layer core-sheath structures. The trigger and sensitivity enhancement mechanisms of the hybrid response are determined through model analysis and experimental verification. By adjustment of the sensitivity enhancement range of the hybrid response, the sensitivity attenuation of HFPSs is alleviated significantly. The obtained results demonstrate that HFPSs have excellent characteristics such as fast response, low hysteresis, wide response frequency, small signal drift, and good durability. The hybrid response enhances the practical sensitivity of HFPSs for various applications. With enhanced sensitivity, HFPSs can effectively monitor pulse signals at preloads ranging from 0 to 22.7 kPa. This wide range of preloads improves the fault tolerance of pulse monitoring and expands the potential application scenarios of fiber pressure sensors.

Bubble Printing of Ti3C2TX MXene for Patterning Conductive and Plasmonic Nanostructures

MXenes represent a novel class of 2D materials with unique properties and have great potential for diverse applications in sensing and electronics; however, their directed assembly at interfaces has not yet been achieved. Herein, the plasmonic heating of MXenes was exploited to achieve the controlled deposition of MXene assemblies via a laser-directed microbubble. The influence of various factors such as solvent composition, substrate surface chemistry, MXene concentration, and laser fluence was investigated, establishing the optimal conditions for rapid patterning with good fidelity. Printed MXene assemblies showed good electrical conductivity and plasmonic sensing capabilities and were able to meet or exceed the state of the art without additional postprocessing steps. This represents the first study of a directed approach for microfabrication using MXenes and lays the foundation for future work in optically directed assembly of MXenes and MXene-based nanocomposites at interfaces toward sensors and devices.