Sensing lead ions in water: a comprehensive review on strategies and sensor materials.

It is well-known fact that elevated lead ions (Pb2+), the third most toxic among heavy metal ions in aqueous systems, pose a threat to human health and aquatic ecosystems when they exceed permissible limits. Pb2+ is commonly found in industrial waste and fertilizers, contaminating water sources and subsequently entering the human body, causing various adverse health conditions. Unlike being expelled, Pb2+ accumulates within the body, posing potential health risks. The harmful impact of presence of Pb2+ in water have prompted researchers to diligently work toward maintaining water quality. Recognizing the importance of Pb2+, this review article makes a sincere and effective effort to address the issues associated with Pb2+. This overview article gives insights into various sensing approaches to detect Pb2+ in water using different sensing materials, including 2-dimensional materials, thiols, quantum dots, and polymers. Herein, different sensing approaches such as electrochemical, optical, field effect transistor-based, micro-electromechanical system-based, and chemi resistive are thoroughly explained. Field effect transistor-based and chemiresistive work on similar principles and are compared on the basis of their fabrication processes and sensing capabilities. In conclusion, future directions for chemiresistive sensors in Pb2+ detection are proposed, emphasizing their simplicity, portability, straightforward functionality, and ease of fabrication. Notably, it sheds light on various thiol and ligand compounds and coupling strategies utilized in Pb2+ detection. This comprehensive study is expected to benefit individuals engaged in Pb2+ detection.

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient.

The Covid-19 pandemic is one of the most significant global health concerns that have emerged in this decade. Intelligent healthcare technology and techniques based on speech signal and artificial intelligence make it feasible to provide a faster and more efficient timely detection of Covid-19. The main objective of our study is to design speech signal-based noninvasive, low-cost, remote diagnosis of Covid-19. In this study, we have developed system to detect Covid-19 from speech signal using Mel frequency magnitude coefficients (MFMC) and machine learning techniques. In order to capture higher-order spectral features, the spectrum is divided into a larger number of subbands with narrower bandwidths as MFMC, which leads to better frequency resolution and less overall noise. As a consequence of an improvement in frequency resolution as well as a decrease in the quantity of noise that is included with the extraction of MFMC, the higher-order MFMCs are able to identify Covid-19 from speech signals with an increased level of accuracy. The procedures for machine learning are often less complicated than those for deep learning, and they may commonly be carried out on regular computers. However, deep learning systems need extensive computing power and data storage. Twelve, twenty-four, thirty, and forty spectral coefficients are obtained using MFMC in our study, and from these coefficients, performance is accessed using machine learning classifiers, such as random forests and K-nearest neighbor (KNN); however, KNN has performed better than the other model with having AUC score of 0.80.

Highly selective sensor for the detection of Hg2+ ions using homocysteine functionalised quartz crystal microbalance with cross-linked pyridinedicarboxylic acid.

This study reports an insightful portable vector network analyser (VNA)-based measurement technique for quick and selective detection of Hg2+ ions in nanomolar (nM) range using homocysteine (HCys)-functionalised quartz-crystal-microbalance (QCM) with cross-linked-pyridinedicarboxylic acid (PDCA). The excessive exposure to mercury can cause damage to many human organs, such as the brain, lungs, stomach, and kidneys, etc. Hence, the authors have proposed a portable experimental platform capable of achieving the detection in 20-30 min with a limit of detection (LOD) 0.1 ppb (0.498 nM) and a better dynamic range (0.498 nM-6.74 mM), which perfectly describes its excellent performance over other reported techniques. The detection time for various laboratory-based techniques is generally 12-24 h. The proposed method used the benefits of thin-film, nanoparticles (NPs), and QCM-based technology to overcome the limitation of NPs-based technique and have LOD of 0.1 ppb (0.1 µg/l) for selective Hg2+ ions detection which is many times less than the World Health Organization limit of 6 µg/l. The main advantage of the proposed QCM-based platform is its portability, excellent repeatability, millilitre sample volume requirement, and easy process flow, which makes it suitable as an early warning system for selective detection of mercury ions without any costly measuring instruments.

Ultrasensitive detection of cadmium ions using a microcantilever-based piezoresistive sensor for groundwater.

This paper proposes the selective and ultrasensitive detection of Cd(II) ions using a cysteamine-functionalized microcantilever-based sensor with cross-linked ᴅʟ-glyceraldehyde (DL-GC). The detection time for various laboratory-based techniques is generally 12-24 hours. The experiments were performed to create self-assembled monolayers (SAMs) of cysteamine cross-linked with ᴅʟ-glyceraldehyde on the microcantilever surface to selectively capture the targeted Cd(II). The proposed portable microfluidic platform is able to achieve the detection in 20-23 min with a limit of detection (LOD) of 0.56 ng (2.78 pM), which perfectly describes its excellent performance over other reported techniques. Many researchers used nanoparticle-based sensors for the detection of heavy metal ions, but daily increasing usage and commercialization of nanoparticles are rapidly expanding their deleterious effect on human health and the environment. The proposed technique uses a blend of thin-film and microcantilever (micro-electromechanical systems) technology, which mitigate the disadvantages of the nanoparticle approaches, for the selective detection of Cd(II) with a LOD below the WHO limit of 3 µg/L.

Fabrication, calibration, and preliminary testing of microcantilever-based piezoresistive sensor for BioMEMS applications.

In this study, the authors demonstrate the fabrication, calibration, and testing of a piezoresistive microcantilever-based sensor for biomedical microelectromechanical system (BioMEMS) application. To use any sensor in BioMEMS application requires surface modification to capture the targeted biomolecules. The surface alteration comprises self-assembled monolayer (SAM) formation on gold (Au)/chromium (Cr) thin films. So, the Au/Cr coating is essential for most of the BioMEMS applications. The fabricated sensor uses the piezoresistive technique to capture the targeted biomolecules with the SAM/Au/Cr layer on top of the silicon dioxide layer. The stiffness (k) of the cantilever-based biosensor is a crucial design parameter for the low-pressure range and also influence the sensitivity of the microelectromechanical system-based sensor. Based on the calibration data, the average stiffness of the fabricated microcantilever with and without Au/Cr thin film is 141.39 and 70.53 mN/m, respectively, which is well below the maximum preferred range of stiffness for BioMEMS applications. The fabricated sensor is ultra-sensitive and selective towards Hg2+ ions in the presence of other heavy metal ions (HMIs) and good enough to achieve a lower limit of detection 0.75 ng/ml (3.73 pM/ml).