Flexible Pyroresistive Graphene Composites for Artificial Thermosensation Differentiating Materials and Solvent Types

Machine Vision with a CMOS-Based Hyperspectral Imaging Sensor Enables Sensing Meat Freshness

Imaging spectral information of materials and analysis of its properties have become an intriguing tool for consumer electronics used for food inspection, beauty care, etc. Those sensory physical quantities are difficult to quantify. Hyperspectral imaging cameras, which capture the figure and spectral information simultaneously, can be a good candidate for nondestructive remote sensing. In this study, with the aid of a hyperspectral imaging system (HIS) and machine learning (ML) techniques, meat freshness is converted into a measurable physical quantity, i.e., the freshness index (FI). Herein, the FI is defined as meat fluorescence, which has a strong correlation with the bacterial density. Combined with ML techniques, hyperspectral data are processed more efficiently. By employing linear discriminant and quadratic component analyses, the FI can be estimated from its decision boundary after hyperspectral data are obtained in an unknown freshness state. We demonstrate that the HIS integrated with ML performs as the artificial eye and brain, which is advanced machine vision for consumer electronics, including refrigerators and smartphones. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life.

Research progress of graphene-based nanomaterials in the diagnosis and treatment of head and neck cancer

Head and neck cancer (HNC) is the sixth most common cancer in the world, and its incidence is increasing year by year. Due to the late-stage diagnosis and poor prognosis of HNC, as well as the limitations of traditional treatment methods, it is urgent to improve early detection rates and explore alternative treatment approaches. Graphene-based nanomaterials (GBNs) have been widely applied in biomedical fields due to their high surface area, excellent photothermal properties, and high loading capacity. This literature review introduces the functionalization and biocompatibility of GBNs, followed by a focus on their applications in the diagnosis and treatment of HNC. This includes their potential as bioimaging or biosensing platforms for diagnosis and monitoring, as well as their research progress in chemotherapy drug delivery, phototherapy, and gene transfection. The tremendous potential of GBNs as a platform for combination therapies is emphasized. Finally, in this literature review, we briefly discuss the toxicity and limitations of GBNs in the current research and provide an outlook on their future clinical applications in the diagnosis and treatment of HNC.

Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram

Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

Solution-Processed Flexible Temperature Sensor Array for Highly Resolved Spatial Temperature and Tactile Mapping Using ESN-Based Data Interpolation

High-performance flexible temperature sensors are crucial in various technological applications, such as monitoring environmental conditions and human healthcare. The ideal characteristics of these sensors for stable temperature monitoring include scalability, mechanical flexibility, and high sensitivity. Moreover, simplicity and low power consumption will be essential for temperature sensor arrays in future integrated systems. This study introduces a solution-based approach for creating a V2O5 nanowire network temperature sensor on a flexible film. Through optimization of the fabrication conditions, the sensor exhibits remarkable performance, sustaining long-term stability (>110 h) with minimal hysteresis and excellent sensitivity (∼-1.5%/°C). In addition, this study employs machine learning techniques for data interpolation among sensors, thereby enhancing the spatial resolution of temperature measurements and adding tactile mapping without increasing the sensor count. Introducing this methodology results in an improved understanding of temperature variations, advancing the capabilities of flexible-sensor arrays for various applications.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Data-Driven Contact-Based Thermosensation for Enhanced Tactile Recognition

Thermal feedback plays an important role in tactile perception, greatly influencing fields such as autonomous robot systems and virtual reality. The further development of intelligent systems demands enhanced thermosensation, such as the measurement of thermal properties of objects to aid in more accurate system perception. However, this continues to present certain challenges in contact-based scenarios. For this reason, this study innovates by using the concept of semi-infinite equivalence to design a thermosensation system. A discrete transient heat transfer model was established. Subsequently, a data-driven method was introduced, integrating the developed model with a back propagation (BP) neural network containing dual hidden layers, to facilitate accurate calculation for contact materials. The network was trained using the thermophysical data of 67 types of materials generated by the heat transfer model. An experimental setup, employing flexible thin-film devices, was constructed to measure three solid materials under various heating conditions. Results indicated that measurement errors stayed within 10% for thermal conductivity and 20% for thermal diffusion. This approach not only enables quick, quantitative calculation and identification of contact materials but also simplifies the measurement process by eliminating the need for initial temperature adjustments, and minimizing errors due to model complexity.

Ultra-Stretchable Spiral Hybrid Conductive Fiber with 500%-Strain Electric Stability and Deformation-Independent Linear Temperature Response

Stretchable configuration occupies priority in devising flexible conductors used in intelligent electronics and implantable sensors. While most conductive configurations cannot suppress electrical variations against extreme deformation and ignore inherent material characteristics. Herein, a spiral hybrid conductive fiber (SHCF) composed of aramid polymeric matrix and silver nanowires (AgNWs) coating is fabricated through shaping and dipping processes. The homochiral coiled configuration mimicked by plant tendrils not only enables its high elongation (958%), but also generates a superior deformation-insensitive effect to existing stretchable conductors. The resistance of SHCF maintains remarkable stability against extreme strain (500%), impact damage, air exposure (90 days), and cyclic bending (150 000 times). Moreover, the thermal-induced densification of AgNWs on SHCF achieves precise and linear temperature response toward a broad range (-20 to 100 °C). Its sensitivity further manifests high independence to tensile strain (0%-500%), allowing for flexible temperature monitoring of curved objects. Such unique strain-tolerant electrical stability and thermosensation hold broad prospects for SHCF in lossless power transferring and expeditious thermal analysis.

Pyroresistive Properties of Composites Based on HDPE and Carbon Fillers

Electrothermal processes were studied in pyroresistive composites based on high-density polyethylene (HDPE) containing 8 vol.% carbon black (CB), 8 vol.% carbon fibers (CF), and their mixture 4 vol.% CB + 4 vol.% CF. It is shown that the kinetic heating curves of composites are well described by an exponential dependence with a certain heating rate constant k for each type of composite. After a short heating time, the equilibrium temperature T_e is reached in the sample. When the applied voltage exceeds a certain value, the T_e value decreases due to the presence of the positive temperature coefficient of resistance (PTC) effect. Due to the PTC effect, the composites exhibit a self-regulating effect relative to the T_e. Relations between the applied voltage, electric power, and equilibrium temperature are found, the T_e value depends on the applied voltage according to the quadratic law whereas there is a linear relationship between the T_e and electric power. A possible application of such pyroresistive composites is resistance welding of plastics using a heating element (HE) made of a pyroresistive material. The use of HDPE-CB composite to create HE for resistance welding is demonstrated and the welded joint of HDPE parts obtained using HE is shown.

Highly Sensitive Temperature-Pressure Bimodal Aerogel with Stimulus Discriminability for Human Physiological Monitoring

Multimodal sensor with high sensitivity, accurate sensing resolution, and stimuli discriminability is very desirable for human physiological state monitoring. A dual-sensing aerogel is fabricated with independent pyro-piezoresistive behavior by leveraging MXene and semicrystalline polymer to assemble shrinkable nanochannel structures inside multilevel cellular walls of aerogel for discriminable temperature and pressure sensing. The shrinkable nanochannels, controlled by the melt flow-triggered volume change of semicrystalline polymer, act as thermoresponsive conductive channels to endow the pyroresistive aerogel with negative temperature coefficient of resistance of -10.0% °C^-1 and high accuracy within 0.2 °C in human physiological temperature range of 30-40 °C. The flexible cellular walls, working as pressure-responsive conductive channels, enable the piezoresistive aerogel to exhibit a pressure sensitivity up to 777 kPa^-1 with a detectable pressure limit of 0.05 Pa. The pyro-piezoresistive aerogel can detect the temperature-dependent characteristics of pulse pressure waveforms from artery vessels under different human body temperature states.