Piezoelectric microelectromechanical system (piezo-MEMS)-based mass sensors including the piezoelectric microcantilevers, surface acoustic waves (SAW), quartz crystal microbalance (QCM), piezoelectric micromachined ultrasonic transducer (PMUT), and film bulk acoustic wave resonators (FBAR) are highlighted as suitable candidates for highly sensitive gas detection application. This paper presents the piezo-MEMS gas sensors’ characteristics such as their miniaturized structure, the capability of integration with readout circuit, and fabrication feasibility using multiuser technologies. The development of the piezoelectric MEMS gas sensors is investigated for the application of low-level concentration gas molecules detection. In this work, the various types of gas sensors based on piezoelectricity are investigated extensively including their operating principle, besides their material parameters as well as the critical design parameters, the device structures, and their sensing materials including the polymers, carbon, metal–organic framework, and graphene.

Graphene as a material for optoelectronic design applications has been significantly restricted owing to zero bandgap and non-compatible handling procedures compared with regular microelectronic ones. In this work, nitrogen-doped reduced graphene oxide (N-rGO) with tunable optical bandgap and enhanced electrical conductivity was synthesized via a microwave-assisted hydrothermal method. The properties of the synthesized N-rGO were determined using XPS, FTIR and Raman spectroscopy, UV/vis, as well as FESEM techniques. The UV/vis spectroscopic analysis confirmed the narrowness of the optical bandgap from 3.4 to 3.1, 2.5, and 2.2 eV in N-rGO samples, where N-rGO samples were synthesized with a nitrogen doping concentration of 2.80, 4.53, and 5.51 at.%. Besides, an enhanced n-type electrical conductivity in N-rGO was observed in Hall effect measurement. The observed tunable optoelectrical characteristics of N-rGO make it a suitable material for developing future optoelectronic devices at the nanoscale.

This study presents an innovative and compact monopole antenna with dual-band frequency reconfigurability for LoRa applications. It operates within the 915 MHz and 868 MHz frequencies, aligning with the designated bands for use in America, Asia and Europe. No existing compact reconfigurable antenna with these features for LoRa applications within ISM bands below 1 GHz is known. Employing an economical FR-4 substrate in its design, the antenna attains a compact size of $40 \times 42 \text {mm}^{2}$ ( $0.12\,\,\lambda _{0} \times 0.12\,\,\lambda _{0}$ ), where $\lambda _{0}$ denotes the wavelength in free space corresponding to 868 MHz. A single RF PIN diode enables seamless switching between 868 MHz and 915 MHz bands. Design, simulation, and optimization employed CST MWS® software. Supervised regression Machine Learning (ML) models predicted resonance frequencies, with Gaussian Process Regression emerging as optimal, achieving R-squared and variance scores of 92.87% and 93.77%, respectively. A maximum gain of 2 dBi at 915 MHz and 70% efficiency, boasting good radiation patterns and matching was demonstrated by the antenna. Experimental validation in a football field at Universiti Teknologi PETRONAS, Malaysia, assessed the proposed antenna’s performance on a LoRa transceiver system based on LoRa SX1276. The Received Signal Strength Indicator (RSSI) of the proposed antenna consistently exceeded the conventional commercially available monopole antenna by an average of $\mathbf {-12 \mathrm {dBm}}$ at every point up to 300 m, showcasing enhanced signal reception. The antenna proves promising for wireless sensor nodes in long-range applications.

Chemicals and pesticides contamination in the food, drinking water, and environment ecosystem have become one of the most serious problems for human public health in the world due to their large amount used and wide application in the agriculture industry. Therefore, the detection and analysis of contamination in the food and drinking water by using techniques that are simple and suitable for fast screening are important. This review gives an overview of the last trends and recent advances biosensors for chemicals and pesticides detection based on electrochemical, optical and mechanical transducers strategies. Furthermore, the biosensors are classified according to their immobilized biorecognition elements including aptamer, antibodies, enzymes, and molecularly imprinted polymers. The implementation of nanomaterials such as graphene, carbon nanotubes, and metals nanoparticles are also emphasized and discussed in this review, these nanomaterials provides remarkable features to the biosensors such highly sensitive and accurate which allowing efficient pesticides detection. In addition to highlighting and summarizing various novel sensors, this review also provided some drawbacks, challenging, prospects as well as the current efforts to enhanced optical sensors.

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535${\lambda }_0\times$0.714${\lambda }_0$, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.