In recent years, lead-free perovskite materials are exponentially emerging in photovoltaic and optoelectronic applications due to their low toxicity and superior optical properties. On the other hand, the demand for flexible, wearable, and lightweight optoelectronic devices is significantly growing in sensor and actuator technologies. In this scenario, lead-free perovskite-based flexible piezoelectric polymer composites have sparked considerable attention in this field due to their excellent piezo-, pyro-, ferroelectric, and photovoltaic properties. Thus, in this work, a long-term stable lead-free Cs 3 Bi 2 I 9 -PVDF composite is introduced. The in situ growth of the Cs 3 Bi 2 I 9 perovskite induces 92% yield of the electroactive phase in the PVDF matrix. The possible mechanism behind the electroactive β-phase transformation is presented via interfacial interactions of PVDF moieties with the Cs 3 Bi 2 I 9 (CBI) perovskite, which also give rise to long-term environmental stability. Next, a piezoelectric nanogenerator (PNG) has been fabricated with the Cs 3 Bi 2 I 9 -PVDF composite for mechanical energy harvesting, biophysiological motion monitoring, and voice recognitions that have potential utility in the health-care sector. Furthermore, a photodetector is developed to realize the piezophototronic effect. It exhibits a fast photoswitching behavior with rise and decay times of 141 and 278 ms, respectively. Thus, it is confirmed that the flexible Cs 3 Bi 2 I 9 -PVDF composite has shown tremendous potential to be used as an optical signal-modulated piezo-responsive wearable sensor.
Interest in the development of new generation injectable bone cements having appropriate mechanical properties, biodegradability, and bioactivity has been rekindled with the advent of nanoscience. Injectable bone cements made with calcium sulfate (CS) are of significant interest, owing to its compatibility and optimal self‐setting property. Its rapid resorption rate, lack of bioactivity, and poor mechanical strength serve as a deterrent for its wide application. Herein, a significantly improved CS‐based injectable bone cement (modified calcium sulfate termed as CSmod), reinforced with various concentrations (0–15%) of a conductive nanocomposite containing gold nanodots and nanohydroxyapatite decorated reduced graphene oxide (rGO) sheets (AuHp@rGO), and functionalized with vancomycin, is presented. The piezo‐responsive cement exhibits favorable injectability and setting times, along with improved mechanical properties. The antimicrobial, osteoinductive, and osteoconductive properties of the CSmod cement are confirmed using appropriate in vitro studies. There is an upregulation of the paracrine signaling mediated crosstalk between mesenchymal stem cells and human umbilical vein endothelial cells seeded on these cements. The ability of CSmod to induce endothelial cell recruitment and augment bone regeneration is evidenced in relevant rat models. The results imply that the multipronged activity exhibited by the novel‐CSmod cement would be beneficial for bone repair.
All-organic piezoelectric mechanical energy harvesters display an excellent electrical output with higher sensitivity due to the superior electrode compatibility between active materials and organic electrodes in comparison to that of metal electrodes. Herein, a stretchable, breathable, and flexible all-organic piezoelectric nanogenerator, made up of PVDF nanofibers and δ-PVDF nanoparticles, fabricated through the electrospinning process in a single step, has been demonstrated for prospective machine learning applications. The δphase PVDF nanoparticles serve as efficient active piezoelectric and ferroelectric components with a piezoelectric coefficient of ∼13 pm/V. In terms of electrical response, a peak-to-peak ∼V OC of 4 V, I SC of 1.8 μA, and maximum power density of ∼1600 μW/m 2 were obtained. The fabricated device also exhibits excellent stretchability and air permeability, enabling the properties of robust wearable devices with a water vapor transmission rate of ∼250 g m −2 day −1 . Here, we have shown that a machine learning algorithm proposed for the different finger motion responses can predict with 94.6% accuracy. Thus, it could recognize different finger gestures efficiently with the highest possible accuracy and predict the possible source point. This feature could be advantageous for prospective health care and security purposes apart from the device and sensor applications.
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