No abstract

Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

Formation process of the insulator–conductor–insulator sandwich structure. After addition of the BTO NPs into the PVDF–GO solution, a 3 μm thin film was achieved by a spin coated method.

No abstract

No abstract

Herein we report an enhanced triboelectric nanogenerator (TENG) based on the contact-separation mode between a patterned film of polydimethylsiloxane (PDMS) with a semimetallic elastomer of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and a nylon fiber film. The addition of ethylene glycol to the PEDOT:PSS film improves the functionality of the TENG significantly, yielding promising applicability in both indoor and outdoor (i.e., under sunlight) environments, with the maximum instantaneous power of 0.09 mW (indoors) and 0.2 mW (outdoors) for the load resistance of 3.8 MΩ. The device can also generate 11.2 V and 0.08 μA cm in response to the forearm movement of a human. Additionally, by replacing the bare nylon fiber in the TENG design with a Ag@ZnO/nylon fiber film, a self-powered active sensor (triboelectric nanogenerator-based sensor; TENS) has been realized to detect acetylene (CH) gas. The TENS exhibits excellent sensitivity of 70.9% (indoors) and 89% (outdoors) to CH gas of 1000 ppm concentration. The proposed approach for harvesting energy and sensing can be advantageous in practical applications and may stimulate new research that will enhance nanogenerators as well as wearable, self-powered active sensors.

No abstract

No abstract