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The purpose of this study is to develop a molecular imprinting electrochemical sensor for the specific detection of the anticancer drug amsacrine. The sensor used a composite of bacterial cellulose (BC) and silver nanoparticles (AgNPs) as a platform for the immobilization of a molecularly imprinted polymer (MIP) film. The main objective was to enhance the electrochemical properties of the sensor and achieve a high level of selectivity and sensitivity toward amsacrine molecules in complex biological samples.

The composite of BC-AgNPs was synthesized and characterized using FTIR, XRD and SEM techniques. The MIP film was molecularly imprinted to selectively bind amsacrine molecules. Electrochemical characterization, including cyclic voltammetry and electrochemical impedance spectroscopy, was performed to evaluate the modified electrode’s conductivity and electron transfer compared to the bare glassy carbon electrode (GCE). Differential pulse voltammetry was used for quantitative detection of amsacrine in the concentration range of 30–110 µM.

The developed molecular imprinting electrochemical sensor demonstrated significant improvements in conductivity and electron transfer compared to the bare GCE. The sensor exhibited a linear response to amsacrine concentrations between 30 and 110 µM, with a low limit of detection of 1.51 µM. The electrochemical response of the sensor showed remarkable changes before and after amsacrine binding, indicating the successful imprinting of amsacrine in the MIP film. The sensor displayed excellent selectivity for amsacrine in the presence of interfering substances, and it exhibited good stability and reproducibility.

This study presents a novel molecular imprinting electrochemical sensor design using a composite of BC and AgNPs as a platform for MIP film immobilization. The incorporation of BC-AgNPs improved the sensor’s electrochemical properties, leading to enhanced sensitivity and selectivity for amsacrine detection. The successful imprinting of amsacrine in the MIP film contributes to the sensor's specificity. The sensor's ability to detect amsacrine in a concentration range relevant to anticancer therapy and its excellent performance in complex sample matrices add significant value to the field of electrochemical sensing for pharmaceutical analysis.

Detecting O₂ gas in a confined space at room temperature is particularly important to monitor the work process of precision equipment. This study aims to propose a miniaturized, low-cost, mass-scale produced O₂ sensor operating around 30°C.

The O₂ sensor based on lanthanum fluoride (LaF₃) solid electrolyte thin film was developed using MEMS technology. The principle of the sensor was a galvanic cell H₂O, O₂, Pt | LaF₃ | Sn, SnF₂ |, in which the Sn film was prepared by magnetron sputtering, and the LaF₃ film was prepared by thermal resistance evaporation.

Through pretreatments, the sensor’s response signal to 40% oxygen concentration was enhanced from 1.9 mV to 46.0 mV at 30°C and 97.0% RH. Tests at temperatures from 30°C to 50°C and humidity from 32.4% RH to 97.0% RH indicated that the output electromotive force (EMF) has a linear relationship with the logarithm of the oxygen concentration. The sensitivity of the sensor increases with an increase in both humidity and temperature in the couple mode, and the EMF of the sensor follows well with the Nernst equation at different temperatures and humidity.

This research could be applied to monitor the oxygen concentration below 25% in confined spaces at room temperature safely without a power supply.

The relationship between temperature and humidity coupling and the response of the sensor was obtained. The nano-film material was integrated with the MEMS process. It is expected to be practically applied in the future.

Regular monitoring of bacteria, especially Escherichia coli, in wastewater is crucial to ensure the maintenance of public health. Amperometric detection proves to be a fast, sensitive and economically viable solution for E. coli enumeration. This paper reported a prototype amperometric sensor based on PANI-ZnO-NiO nanocomposite thin films prepared by sol–gel method and irradiated with gamma ray. The purpose of this study is to investigate the sensor performance of PANI-ZnO-NiO nanocomposite thin films to detect E. coli in water.

The films were varied with different compositions of ZnO and NiO by using the formula PANI-(ZnO)_1-x-(NiO)_x, with x = 0.2, 0.4, 0.6 and 0.8. PANI-ZnO-NiO nanocomposite thin films were characterized by using X-ray diffraction (XRD) and atomic force microscopy (AFM) to study the crystallinity and surface morphology of the films. The sensor performance was conducted using the current–voltage (I-V) measurement by testing the films in clean water and E. coli solution.

XRD diffractograms show the peaks of ZnO (1 0 0) and NiO (1 0 2). AFM analysis shows the surface roughness, and the grain size of PANI-ZnO-NiO thin films decreases when the concentration ratios of NiO increased. I-V curves show the difference in current flow, where the current in E. coli solution is higher than the clean water.

PANI-(ZnO)_1-x-(NiO)_x nanocomposite thin film with the highest concentration of ZnO performed the highest sensitivity among the other concentrations, which can be used to indicate the presence of E. coli bacteria in water.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

Various approaches have been made to alter the vibration sensing properties of zinc oxide (ZnO) films to achieve high sensitivity. This paper aims to report the experimental study of the fabrication of precursor molar ratio concentration varied ZnO nanostructures grown on rigid substrates using the refresh hydrothermal method. The effect of these fabricated ZnO nanostructures-based vibration sensors was experimentally investigated using a vibration sensing setup.

ZnO nanostructures have been grown using low temperature assisted refresh hydrothermal method with different precursor molar concentrations 0.025 M (R1), 0.075 M (R2) and 0.125 M (R3). Poly 3,4-ethylenedioxythiophene polystyrene sulfonate, a p-type material is spun coated on the grown ZnO nanostructures. Structural analysis reveals the increased intensity of the (002) plane and better c-axis orientation of the R2 and R3 sample comparatively. Morphological examination shows the changes in the grown nanostructures upon increasing the precursor molar concentration. The optical band gap value decreases from 3.11 eV to 3.08 eV as the precursor molar concentration is increased. Photoconductivity study confirms the formation of a p-n junction with less turn-on voltage for all the fabricated devices. A less internal resistance of 0.37 kΩ was obtained from Nyquist analysis for R2 compared with the other two fabricated samples. Vibration testing experimentation showed an improved output voltage of the R2 sample (2.61 V at 9 Hz resonant frequency and 2.90 V for 1 g acceleration) comparatively. This also gave an increased sensitivity of 4.68 V/g confirming its better performance when compared to the other fabricated two samples.

Photoconductivity study confirms the formation of a p-n junction with less turn-on voltage for all the fabricated devices. A less internal resistance of 0.37 kΩ was calculated from the Nyquist plot. Vibration testing experimentation proves an increased sensitivity of 4.68 V/g confirming its better performance when compared to the other fabricated two samples.

Vibration testing experimentation proves an increased sensitivity of 4.68 V/g for R2 confirming its better performance when compared to the other fabricated two samples.

In this study, an attempt has been made to collect the research that has been done on the construction and design of the H₂O₂ sensor. So far, many efforts have been made to quickly and sensitively determine H₂O₂ concentration based on different analytical principles. In this study, the importance of H₂O₂, its applications in various industries, especially the food industry, and the importance of measuring it with different techniques, especially portable sensors and on-site analysis, have been investigated and studied.

Hydrogen peroxide (H₂O₂) is a very simple molecule in nature, but due to its strong oxidizing and reducing properties, it has been widely used in the pharmaceutical, medical, environmental, mining, textile, paper, food production and chemical industries. Sensitive, rapid and continuous detection of H₂O₂ is of great importance in many systems for product quality control, health care, medical diagnostics, food safety and environmental protection.

Various methods have been developed and applied for the analysis of H₂O₂, such as fluorescence, colorimetry and electrochemistry, among them, the electrochemical technique due to its advantages in simple instrumentation, easy miniaturization, sensitivity and selectivity.

Monitoring the H₂O₂ concentration level is of practical importance for academic and industrial purposes. Edible oils are prone to oxidation during processing and storage, which may adversely affect oil quality and human health. Determination of peroxide value (PV) of edible oils is essential because PV is one of the most common quality parameters for monitoring lipid oxidation and oil quality control. The development of cheap, simple, fast, sensitive and selective H₂O₂ sensors is essential.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.

This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.

The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.

This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this research is to identify and investigate some natural dyes with halochromic properties for potential use as food spoilage indicators to reduce waste and curve the negative effects of food borne diseases.

Exactly 10 potential dye-yielding plants were selected based on their colour (mostly purple, red, maroon and pink). Solvent extraction was used to extract the dyes and pH differential method was used to determine the concentrations of anthocyanin in the extracted dyes. Different concentrations of hydrochloric acid and sodium hydroxide (0.1 M, 1 M and 2 M) in drops and in excess as acidic and basic solution, respectively, were used to test the halochromicity of the extracted dyes. Methyl red (a synthetic dye) was used as a reference standard/control. The pH of the dyes was recorded before and after addition of both NaOH and HCl solutions.

Five out of the 10 dyes extracted (labelled as dye A–E for Ti plant (green Cordyline fruticosa), coleus (Coleus blumei), paper flower (Bougainvillea glabra), painted nettle (Palisandra coleus) and purple heart (Setcresea purpurea), respectively, were found to be halochromic (even at low doses) by changing its colour when exposed to both acidic and basic solutions. While other dyes labelled F–J for red acalypha (Acalypha wilkesiana), golden shower (Cassia fistula), golden dew drop (Duranta repens), wild sage (Lantana camara var Aculeata) and pink oleander (Apocynaceae Nerium oleander), respectively, were either completely insensitive to the solutions in drops, slightly sensitive at high doses or the colour change is insignificant. Although some dyes were found to be more sensitive than others but in most cases, the colour changes in halochromic dyes were more stable in acidic conditions than in basic making it more sensitive to the basic than the acidic solution with the exception of dye A and E (to some extent) which was sensitive to both acidic and basic solution. The anthocyanin contents of dye A–J were found to be between the range of 2.28–10.35 mg/l with dye E having the lowest and dye J with the highest anthocyanin concentration, respectively. The initial pH of all the dyes falls within the range of 4.8–7.3 with most found within the acidic range.

Halochromic dye research studies are still at the infancy stage in developing world despite the vast available and abundant potential natural halochromic dye-yielding plants. The study explored this area of research and gives an opportunity for the development of smart packaging for pH-sensitive foods using natural dyes as an alternative to conventional synthetic dyes to reduce cost and also curve the negative effect of synthetic dyes as well as food borne diseases.