Self-powered sensor development is moving towards miniaturization and requires a suitable power source for its operation. The piezoelectric nanogenerator (PENG) is a potential candidate to act as a partial solution to suppress the burgeoning energy demand. The present work is focused on the development of the PENG based on flexible polymer-ceramic composite films. The X-ray spectra suggest that the BTO particles have tetragonal symmetry and the PVDF-BTO composite films (CF) have a mixed phase. The dielectric constant increases with the introduction of the particles in the PVDF polymer and the loss of the CF is much less for all compositions. The BTO particles have a wide structural diversity and are lead-free, which can be further employed to make a CF. An attempt was made to design a robust, scalable, and cost-effective piezoelectric nanogenerator based on the PVDF-BTO CFs. The solvent casting route was a facile approach, with respect to spin coating, electrospinning, or sonication routes. The introduction of the BTO particles into PVDF enhanced the dielectric constant and polarization of the composite film. Furthermore, the single-layered device output could be increased by strategies such as internal polarization amplification, which was confirmed with the help of the polarization-electric field loop of the PVDF-BTO composite film. The piezoelectric nanogenerator with 10 wt% BTO-PVDF CF gives a high electrical output of voltage 7.2 V, current 38 nA, and power density of 0.8 μW/cm² at 100 MΩ. Finally, the energy harvesting using the fabricated PENG is done by various actives like coin dropping, under air blowing, and finger tapping. Finally, low-power electronics such as calculator is successfully powered by charging a 10 μF capacitor using the PENG device.

Piezoelectric nanogenerators are dependent on a piezoelectric material for the conversion of mechanical stress into useful electrical energy. This paves the way for the development of piezoelectric material compositions starting from ceramic oxides, polymers, and organic compounds to design piezoelectric energy harvesters towards energy harvesting and self-powered sensor development. In the present work, we introduce a multiferroic composition, BiMgFeCeO₆ (henceforth referred to as BMFCO), using a high-temperature reaction route. The physicochemical properties of the synthesized samples were systematically investigated, providing insight into their interesting structure–property correlation. The BMFCO particles were further blended with PDMS at different weight percentages (wt%) and converted into flexible composite films. Stability and capacitor charging tests confirm their stable and durable electrical output over a longer period. Biomechanical energy harvesting was performed by attaching the 10 wt% BMFCO–PDMS-based device (hereafter referred to as PENG10) to a computer mouse and for several body movements. Digital signal processing techniques such as wavelet transform and wavelet packet transform were employed to recognize the difference between a comfortable sound level and excessive volume based on the output recorded by a PENG attached to a speaker.

This work presents the fabrication of crumpled carbon nanotubes (C-CNTs) thin film heaters and their application towards high sensitivity and low drift hydrogen gas sensing. Utilizing a spray coating of pristine multi-walled carbon nanotubes (MWCNTs) and thermal shrinkage of polystyrene (PS) substrate, we have fabricated C-CNTs with closely packed junctions. Joule heating of C-CNTs gives higher temperature at a given input voltage compared to as-deposited CNTs. In addition, temperature coefficient of resistance (TCR) is analyzed for accurate temperature control and measurement of the heater. The C-CNT heaters are capable of hydrogen gas sensing while demonstrating higher measurement sensitivities along with lower drift compared to as-deposited CNT devices. In addition, the self-heating of C-CNT heaters help rapid desorption of hydrogen, and thus allowing repetitive and stable sensor operation. Our findings reveal that both CNT morphologies and heating temperatures affect the hydrogen sensing performances.

Autonomous vehicles are composed of various perception technologies, among which the lane detection model serves as a key technology performing numerous roles. These include providing basic information for maintaining and changing lanes in autonomous vehicles, and developing driving route plans. Existing lane perception models improve performance by adopting additional inference processes or complex neural network structures to detect lanes even when obscured by other objects. However, this results in high computational and memory complexity during the system integration process of autonomous vehicles, thereby necessitating an appropriate neural network structure. This paper proposes a method known as CLIMB (Computational Lane Identification using Multi-objective Bayesian optimization) to address this problem, ensuring convergence to the Pareto optimum between accuracy and model efficiency. Unlike traditional methods using Bayesian optimization that maximize performance with model efficiency as a regularization or constraint term, the proposed CLIMB method leverages multi-objective Bayesian optimization to give users the choice of various neural network structures, balancing both computational efficiency and accurate lane detection. To verify the efficacy of the proposed CLIMB method, we used a publicly recognized lane detection dataset and confirmed notable performance improvement compared to previous methods.