Conductive/Insulating Bioinks with Multitechnology Compatibility and Adjustable Performance.

Conducting/insulating inks have received significant attention for the fabrication of a wide range of additive manufacturing technology. However, current inks often demonstrate poor biocompatibility and face trade-offs between conductivity and mechanical stiffness under physiological conditions. Here, conductive/insulating bioinks based on two-dimensional materials are proposed. The conductive bioink, graphene (GR)-poly(lactic-co-glycolic acid) (PLGA), is prepared by introducing conductive GR into a degradable polymer matrix, PLGA, while the insulating bioink, boron nitride (BN)-PLGA, is synthesized by adding insulating BN. By optimizing the material ratios, this work achieves precise control of the electromechanical properties of the bioinks, thereby enabling the flexible construction of conductive networks according to specific requirements. Furthermore, these bioinks are compatible with a variety of manufacturing technologies such as 3D printing, electrospinning, spin coating, and injection molding, expanding their application range in the biomedical field. Overall, the results suggest that these conducting/insulating bioinks offer improved mechanical, electronic, and biological properties for various emerging biomedical applications.

5.7 GHz Ultrasensitive Shear Horizontal-Surface Acoustic Wave Humidity Sensor Based on LiNbO3/SiO2/SiC Heterostructures with a Sensitive Layer of Polyethyleneimine-SiO2 Nanocomposites.

Humidity sensing and water molecule monitoring have become hot research topics attributed to their potential applications in monitoring breathing/physiological conditions of humans, air conditioning in greenhouses, and soil moisture in agriculture. However, there is a huge challenge for highly sensitive and precision humidity detection with wireless and fast responsive capabilities. In this work, a hybrid/synergistic strategy was proposed using a LiNbO3/SiO2/SiC heterostructure to generate shear-horizontal (SH) surface acoustic waves (SAWs) and using a nanocomposite of polyethylenimine-silicon dioxide nanoparticles (PEI-SiO2 NPs) to form a sensitive layer, thus achieving an ultrahigh sensitivity of SAW humidity sensors. Ultrahigh frequencies (1∼15 GHz) of SAW devices were obtained on a high-velocity heterostructure of LiNbO3/SiO2/SiC. Among the multimodal wave modes, we selected SH waves for humidity sensing and achieved a high mass-sensitivity of 5383 MHz · mm2 · µg-1. With the PEI-SiO2 NP composite as the sensitive layer, an ultrahigh sensitivity of 2.02 MHz/% RH was obtained, which is two orders of magnitude higher than those of the conventional SAW humidity sensors (∼202.5 MHz frequency) within a humidity range of 20-80% RH.

Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel.

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementation of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor for confidential information. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide-based hydrogel sensors. It offers fast response and good sensitivity and allows high-precision recognition of handwritten content from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from seven participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (∼91.30%). Our developed handwriting recognition system has great potential in advanced human-machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.

A new strategy to minimize humidity influences on acoustic wave ultraviolet sensors using ZnO nanowires wrapped with hydrophobic silica nanoparticles.

Surface acoustic wave (SAW) technology has been widely developed for ultraviolet (UV) detection due to its advantages of miniaturization, portability, potential to be integrated with microelectronics, and passive/wireless capabilities. To enhance UV sensitivity, nanowires (NWs), such as ZnO, are often applied to enhance SAW-based UV detection due to their highly porous and interconnected 3D network structures and good UV sensitivity. However, ZnO NWs are normally hydrophilic, and thus, changes in environmental parameters such as humidity will significantly influence the detection precision and sensitivity of SAW-based UV sensors. To solve this issue, in this work, we proposed a new strategy using ZnO NWs wrapped with hydrophobic silica nanoparticles as the effective sensing layer. Analysis of the distribution and chemical bonds of these hydrophobic silica nanoparticles showed that numerous C-F bonds (which are hydrophobic) were found on the surface of the sensitive layer, which effectively blocked the adsorption of water molecules onto the ZnO NWs. This new sensing layer design minimizes the influence of humidity on the ZnO NW-based UV sensor within the relative humidity range of 10-70%. The sensor showed a UV sensitivity of 9.53 ppm (mW/cm2)-1, with high linearity (R 2 value of 0.99904), small hysteresis (<1.65%) and good repeatability. This work solves the long-term dilemma of ZnO NW-based sensors, which are often sensitive to humidity changes.

Multiscale and hierarchical wrinkle enhanced graphene/Ecoflex sensors integrated with human-machine interfaces and cloud-platform.

Current state-of-the-art stretchable/flexible sensors have received stringent demands on sensitivity, flexibility, linearity, and wide-range measurement capability. Herein, we report a methodology of strain sensors based on graphene/Ecoflex composites by modulating multiscale/hierarchical wrinkles on flexible substrates. The sensor shows an ultra-high sensitivity with a gauge factor of 1078.1, a stretchability of 650%, a response time of ~140 ms, and a superior cycling durability. It can detect wide-range physiological signals including vigorous body motions, pulse monitoring and speech recognition, and be used for monitoring of human respirations in real-time using a cloud platform, showing a great potential for the healthcare internet of things. Complex gestures/sign languages can be precisely detected. Human-machine interface is demonstrated by using a sensor-integrated glove to remotely control an external manipulator to remotely defuse a bomb. This study provides strategies for real-time/long-range medical diagnosis and remote assistance to perform dangerous tasks in industry and military fields.

MCP-1-2518A>G polymorphism and myocardial infarction risk: a meta-analysis and meta-regression.

OBJECTIVE: Monocyte chemoattractant protein-1 (MCP-1), a key chemokine in atherosclerotic inflammation, plays an important role in the etiology of myocardial infarction (MI). Emerging evidence has shown that the common polymorphism (-2518A>G; rs1024611) in the MCP-1 gene may contribute to the risk of MI, but individually published studies showed inconclusive results. This meta-analysis aimed to derive a more precise estimation of the associations between MCP-1-2518A>G polymorphism and susceptibility to MI. METHODS: A literature search of PubMed, Embase, Web of Science, and China BioMedicine (CBM) databases was conducted on articles published before May 15th, 2013. The crude odds ratios with 95% confidence intervals were calculated. RESULTS: Eleven case-control studies were included with a total 2325 MI patients and 6310 healthy controls. The meta-analysis results indicated that MCP-1-2518A>G polymorphism was associated with an increased risk of MI. In further subgroup analysis based on ethnicity, there were significant associations between MCP-1-2518A>G polymorphism and an increased risk of MI among Asian populations. However, no statistically significant association was found among Caucasian populations. Univariate and multivariate meta-regression analyses showed that ethnicity may be the major source of heterogeneity. No publication bias was detected in this meta-analysis. CONCLUSION: In conclusion, the current meta-analysis indicates that MCP-1-2518A>G polymorphism may be a risk factor for MI, especially among Asian populations.

C-reactive protein gene polymorphisms and myocardial infarction risk: a meta-analysis and meta-regression.

AIMS: C-reactive protein (CRP), the classic acute-phase protein, plays an important role in the etiology of myocardial infarction (MI). Emerging evidence has shown that the common polymorphisms in the CRP gene may influence an individual's susceptibility to MI; but individually published studies showed inconclusive results. This meta-analysis aimed to derive a more precise estimation of the associations between CRP gene polymorphisms and MI risk. METHODS: A literature search of PubMed, Embase, Web of Science, and China BioMedicine (CBM) databases was conducted on articles published before June 1st, 2013. Crude odds ratio (OR) with 95% confidence interval (CI) were calculated. RESULTS: Nine case-control studies were included with a total of 2992 MI patients and 4711 healthy controls. The meta-analysis results indicated that CRP rs3093059 (T>C) polymorphism was associated with decreased risk of MI, especially among Asian populations. However, similar associations were not observed in CRP rs1800947 (G>C) and rs2794521 (G>A) polymorphisms (all p>0.05) among both Asian and Caucasian populations. Univariate and multivariate meta-regression analyses showed that ethnicity may be a major source of heterogeneity. No publication bias was detected in this meta-analysis. CONCLUSION: In conclusion, the current meta-analysis indicates that CRP rs3093059 (T>C) polymorphism may be associated with decreased risk of MI, especially among Asian populations.