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We report the synthesis of Bi 0.9 La 0.1 Fe 0.95 Mn 0.05 O 3 (BLFMO) films with enhanced multiferroic properties and without second phase using Ba 0.7 Sr 0.3 Ti 0.95 Co 0.05 O 3 (BSTCO) film as a buffer layer. Sol–gel processes were used to prepare the crystallized BLFMO/BSTCO films, in which magnetic domains were observed and no impurity phase was detected. We found that the buffer layer could effectively enhance the saturation magnetization and retentivity values of the BLFMO films, and especially the magnetic coercive field of these bilayer films was significantly stronger than that of the BLFMO films without buffer layers. [ABSTRACT FROM AUTHOR]

Sedentary and poor sitting postures will significantly raise the risk of cardiovascular disease and musculoskeletal disorders in humans. Thus, it is urgent to realize sedentary and sitting postures monitoring through various types of sensors. However, these sensors are commonly rigid, and non-degradable with low sensitivity and sensing range, which greatly restricts their practical applications in seamless, eco-friendly, and accurate monitoring in real-time. Here, we report flexible, degradable, transient, highly sensitive MXene/cellulose nanofibers (CNF) dual-type sensors including pressure sensor and bioelectric sensor. The pressure sensor is fabricated with hierarchical multi-layer MXene/CNF (MMC) as sensing layer, which shows wide detection range (0–950 kPa), high sensitivity (34.6 kPa−1), excellent stability (3000 cycles), transient and degradability. Moreover, the bioelectric sensor is obtained by delaminated MXene/CNF (DMC) sensing film, exhibiting high conductive, low contact impedance in high-quality electrocardiogram signal recording. Furthermore, with the aid of the deep neural network, the dual sensors can be integrated into an intelligent health monitoring system to achieve continous electrocardiogram, heart rate, sedentary, and sitting posture monitoring simultaneously. The system can realize multi-users identification and sitting postures recognition with high accuracy (>97%), showing great potential in health status monitoring with more privacy compared with previous methods. [Display omitted] [ABSTRACT FROM AUTHOR]