E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin.

The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.

A highly sensitive and anti-freezing conductive strain sensor based on polypyrrole/cellulose nanofiber crosslinked polyvinyl alcohol hydrogel for human motion detection.

Electro-conductive hydrogels emerge as a stretchable conductive materials with diverse applications in the synthesis of flexible strain sensors. However, the high-water content and low cross-links density cause them to be mechanically destroyed and freeze at subzero temperatures, limiting their practical applications. Herein, we report a one-pot strategy by co-incorporating cellulose nanofiber (CNF), Poly pyrrole (PPy) and glycerol with polyvinyl alcohol (PVA) to prepare hydrogel. The addition of PPy endowed the hydrogel with good conductivity (∼0.034 S/m) compared to the no PPy@CNF group (∼0.0095 S/m), the conductivity was increased by 257.9 %. The hydrogel exhibits comparable ionic conductivity at -18 °C as it does at room temperature. It's attributed to the glycerol as a cryoprotectant and the formation of hydrated [Zn(H2O)n]2+ ions via strong interaction between Zn2+ and water molecules. Moreover, the cellulose nanofiber intrinsically assembled into unique hierarchical structures allow for strong hydrogen bonds between adjacent cellulose and PPy polymer chains, greatly improve the mechanical strength (stress∼0.65 MPa, strain∼301 %) and excellent viscoelasticity (G'max âˆ¼ 82.7 KPa). This novel PPy@CNF-PVA hydrogel exhibits extremely high Gauge factor (GF) of 2.84 and shows excellent sensitivity, repeatability and stability. Therefore, the hydrogel can serve as reliable and stable strain sensor which shows excellent responsiveness in human activities monitoration.

Polymer-acid-metal quasi-ohmic contact for stable perovskite solar cells beyond a 20,000-hour extrapolated lifetime.

The development of a robust quasi-ohmic contact with minimal resistance, good stability and cost-effectiveness is crucial for perovskite solar cells. We introduce a generic approach featuring a Lewis-acid layer sandwiched between dopant-free semicrystalline polymer and metal electrode in perovskite solar cells, resulting in an ideal quasi-ohmic contact even at elevated temperature up to 85 °C. The solubility of Lewis acid in alcohol facilitates nondestructive solution processing on top of polymer, which boosts hole injection from polymer into metal by two orders of magnitude. By integrating the polymer-acid-metal structure into solar cells, devices exhibit remarkable resilience, retaining 96% ± 3%, 96% ± 2% and 75% ± 7% of their initial efficiencies after continuous operation in nitrogen at 35 °C for 2212 h, 55 °C for 1650 h and 85 °C for 937 h, respectively. Leveraging the Arrhenius relation, we project an impressive T80 lifetime of 26,126 h at 30 °C.

A predictive model for frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease.

Background: The frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is characterized by experiencing at least two exacerbations per year, leading to a significant economic burden on healthcare systems worldwide. Although several biomarkers have been shown to be effective in assessing AECOPD severity in recent years, there is a lack of studies on markers to predict the frequent exacerbator phenotype of AECOPD. The current study aimed to develop a new predictive model for the frequent exacerbator phenotype of AECOPD based on rapid, inexpensive, and easily obtained routine markers. Methods: This was a single-center, retrospective study that enrolled a total of 2,236 AECOPD patients. The participants were divided into two groups based on the frequency of exacerbations: infrequent group (n=1,827) and frequent group (n=409). They underwent a complete blood count, as well as blood biochemistry, blood lipid and coagulation testing, and general characteristics were also recorded. Univariate analysis and binary multivariate logistic regression analyses were used to explore independent risk factors for the frequent exacerbator phenotype of AECOPD, which could be used as components of a new predictive model. The receiver operator characteristic (ROC) curve was used to assess the predictive value of the new model, which consisted of all significant risk factors predicting the primary outcome. The nomogram risk prediction model was established using R software. Results: Age, gender, length of stay (LOS), neutrophils, monocytes, eosinophils, direct bilirubin (DBil), gamma-glutamyl transferase (GGT), and the glucose-to-lymphocyte ratio (GLR) were independent risk factors for the frequent exacerbator phenotype of AECOPD. The area under the curve (AUC) of the new predictive model was 0.681 [95% confidence interval (CI): 0.653-0.708], and the sensitivity was 63.6% (95% CI: 58.9-68.2%) and the specificity was 65.0% (95% CI: 60.3-69.6%). Conclusions: A new predictive model based on demographic characteristics and blood parameters can be used to predict the frequency of acute exacerbations in the management of chronic obstructive pulmonary disease (COPD).