An H2S sensor working in the range of 0–50 ppm was successfully developed using a sandwich-type CuO-SnO2 film. The sensor was evaluated against the 4-S sensor selection criteria (high sensitivity, selectivity, stability, and suitability) crucial for identifying a commercially viable sensor device. Strong modulation in the random p–n nano-heterojunctions between p-type CuO and n-type SnO2 properties owing to the exposure to H2S was found to be responsible for the observed niche performance. Remarkably high sensor response (SR) values of 1500 and 4760 towards 10 ppm and 50 ppm H2S, respectively, were achieved. This enhanced performance is facilitated by the formation of metallic CuS, releasing a large number of free electrons to the SnO2 host matrix. Along with high selectivity, the long-term measurements performed over a period of 2 months with variation of < 5

This study reports the development of silver-doped zinc oxide (Ag–doped ZnO) thin film synthesized via chemical bath deposition method at room temperature for H2S gas sensing application. The structural, morphological and optical properties of the Ag–doped ZnO film were analyzed using X–ray diffraction (XRD), scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX) and UV–Visible spectroscopy (UV–Vis). Ag doping significantly enhanced the gas sensing response and lowered the optimal operating temperature to 200 ℃, compared to the undoped ZnO film prepared under identical conditions. The Ag–doped ZnO film exhibited a nearly linear response to H2S concentrations ranging from 5 to 50 ppm at 200 ℃. These findings highlight the potential of Ag–doped ZnO thin films as cost-effective and scalable candidates for toxic gas detection in environmental monitoring applications.

This study presents the development of a high-performance chemiresistive hydrogen sulphide (H₂S) gas sensor realized using a hybrid physical vapor deposition (PVD) technique. In particular, the sensor fabrication process integrates two complementary PVD methods; namely Direct Current (DC) sputtering for tin (Sn) deposition and thermal evaporation for copper (Cu) deposition, forming a heterostructured sensing layer that exhibited enhanced gas adsorption and charge transfer properties. The developed sensor exhibited a sensor response (SR) of 22,000 (Ra/Rg) towards 500 ppm of H2S with a rapid response and recovery times of 1 and 6 min, respectively. The sensor performance was evaluated against the 4-S sensor selection or Ramgir criteria to check its feasibility for commercial deployment. Identical response kinetics observed over multiple cycles or repeated high-concentration H₂S exposure (500 ppm) further establishes the excellent repeatability and reproducibility thereby assuring reliability of the developed sensors. Further, the long-term operational stability measurements assessed over a period of six months indicated that the sensor consistently maintained a SR of 2200 towards 10 ppm H2S with negligible degradation. Selectivity studies revealed that the sensor exhibited a significantly higher response toward H₂S compared to other interfering gases, including methane (CH₄), chlorine (Cl₂), ammonia (NH₃), and ethylene (C₂H₄). The synergistic combination of Sn and Cu, deposited via the dual PVD approach, significantly enhances the sensor’s sensitivity, selectivity, and durability. The fulfilment of the 4-S sensor selection criteria makes the sensor a promising candidate for realizing a device suitable for industrial and environmental H₂S monitoring applications.

The growing need for efficient and sustainable energy storage technologies is accelerating progress in the industry. Manganese dioxide (MnO2) is a common substitution for energy storage simply due to its exceptional electrochemical characteristics like high theoretical capacitance, cost-effectiveness and environmental sustainability. Despite of its potential, difficulties such as low electrical conductivity, have hampered its use in energy storage systems. This study examines advancements in MnO2-based energy storage technology, including creative tactics and breakthroughs. Recent breakthroughs in energy storage technologies such as supercapacitors, batteries, and hybrid devices are also examined. Further research directions include scalability and cost reduction i.e., cost-effective and scalable synthesis methods. Improving cycling stability and performance over time MnO2-based electrodes under real-world settings is also crucial for practical use. Sustainable growth demands addressing the environmental effect of MnO2 production, disposal and developing eco-friendly alternatives. The article focuses on continuing research to fully realize the potential of MnO2 and future directions to enhance these technologies.

Effect of incorporation of sensitizers namely palladium (Pd) and platinum (Pt) on the gas-sensing behaviour of zinc oxide (ZnO) nanowires has been studied. The specificity achieved is further studied and demonstrated for its efficacy towards the simultaneous detection of multiple gases employing the developed sensors in an electronic nose configuration. Incorporation of salt solutions containing the desired sensitizer concentration in the starting reaction mixture of hydrothermal growth has been effectively used to achieve heterostructure ZnO nanowires. Pd and Pt gets incorporated as PdO and metallic Pt, in the host matrix resulting in the formation of random heterojunctions namely p–n junction and Schottky junctions. Consequently, an increase in the work function as studied using Kelvin probe studies is observed. Utilizing statistical implements namely principal component analysis (PCA) and hierarchical cluster analysis (HCA) the discrimination of three gases namely H2, H2S and NO2 has been successfully accomplished. 3D PCA discriminates the three gases successfully with first three components exhibiting a percentage of variance of 42.32, 33.26 and 24.20%, respectively. A reasonable discrimination of H2, H2S and NO2, grouped into three clusters as evident from HCA dendrograms, was achieved using utilizing Ward’s method and Euclidian distance metric approach.

Owing to the adverse effect of lead (Pb) on almost every organ of the body and predominantly, the nervous system, a sensitive and selective detection of the lead below the permissible limit is highly desirable. In the present work, an electrochemical lead sensor based on graphene oxide-chitosan (GO-CS) biocomposite, surface functionalized with silver nanoparticles has been demonstrated. The GO-CS biocomposite formation is ascribed to the hydrogen bonding and electrostatic interaction between the oxygen-containing functional groups of GO and chitosan. The amide and carboxyl groups present in GO-CS helps in the reduction of Ag2+ and anchoring of the silver nanoparticles (Ag0) on the surface. This eventually resulted in the enhanced electrochemical activity and charge transfer thereby facilitating selective chemisorption of Pb(II) on GO-CS-Ag. The sensor exhibited superior response with limit of detection (LOD) of 0.15 ppb and a linear detection range from 0.2 to 40 ppb (MRL = 10 ppb). Importantly, the sensor is highly selective towards Pb(II) with selectivity factor ranging from 5 to 8, as compared with other heavy metal ions. The developed sensor has been tested and validated for lead detection in contaminated water samples wherein the results were found to be in agreement with those from atomic absorption spectroscopy (AAS) analysis.

We report the design and development of simple and economical Arduino-based four-channel data acquisition system with digital temperature control that is suitable for sensor parameter optimization involved in the search of appropriate chemiresistive gas sensing material for a particular gas. The developed system is capable of simultaneously capturing the data as well as controlling the desired operating temperature (up to 300 °C) of four sensing elements. This is achieved by employing sixteen analog and digital channels of the Arduino nano microcontroller. The sensing circuit is capable of recording the resistance changes from 20 kΩ to 5 MΩ at a rate of 15 kHz. The developed system has been tested rigorously, validated and demonstrated for its effectiveness towards investigating the gas sensing properties of chemiresistive NO2 sensors realized using ZnO nanowires. The present simple and low-cost alternative finds its application for developing the target specific chemiresistive sensors in particular for investigating the gas sensing properties of metal oxide semiconductors.

ZnO is one of the best functional materials owing to the ability it provides to tune its structure and morphology with corresponding enhancement in the desired properties. For gas sensing, in particular, its response towards a particular gas can easily be tailored by the careful choice of sensitizers and its concentration. In the present paper, we report the formation and modulation of n-n heterostructure interface between ZnO and In2O3, as an effective way to achieve enhanced response characteristics towards NO2 gas. Herein, modification of the ZnO nanowire sensor surface with 3wt.% (10nm) of ‘In’ resulted in a significant improvement in the sensing properties. The developed sensor film exhibited a response of 13 towards 4 ppm of NO2 at 180°C with response and recovery times of 35 and 250s, respectively. The observed enhanced NO2 sensing property is mainly attributed to the formation and modulation of the interface property between the n-n heterostructure. Importantly, the obtained results are analogous to the approach of modulation of interface properties like p-n and Schottky junctions on the sensor surface to achieve improved sensor responses. Our results, thus establishes the present method as a universal approach to achieve tailored sensor response characteristics.

Work function measurement using Kelvin probe method has been demonstrated as an effective and novel approach towards detection of NH3 and NO2 gases using ZnO–NiO based nanocomposites. For this the nanocomposites were synthesised in different compositions using the solvothermal method. Formation of ZnO–NiO nanocomposites was confirmed using XRD and EDS studies. It is found that the nanoparticle morphology of NiO changes with different percentages of Zn addition. The work function of the sensing film was found to decrease and increase upon exposure to NH3 (1.51) and NO2 (1.18) gases owing to the reducing and oxidising nature of the test gases. Of the different composites, Zn0.75Ni0.25O exhibited highest sensor response towards the test gases. The increased response is attributed to the nanostructured morphology of the nanocomposite and the formation and collapse of the p-n heterojunction formed between p-type NiO and n-type ZnO. Besides, incorporation of NiO enhances the oxygen adsorption on the sensor surface assigned to the Ni2+ ions getting readily oxidised to Ni3+. Our results clearly suggest that the work function measurements could also be used as an effective way for NO2 and NH3 detection.

This research gives a valuable insight for understanding the effect of doping on morphology and rGO func-tionalization on electrochemical sensing. Firstly, hydrothermal method was used to synthesize pure SnO2, cobalt (Co) doped and iron (Fe) doped SnO2 nanocomposites. Doping of Co/Fe into SnO2, gradually attract spherical nanoparticles together to form a three-dimension cube-like structure. Considering the potential of these morphological changes, their heavy metal ions (HMIs) sensing properties have been further investigated. The results revealed that the type of dopants (Co/Fe) and their morphologies have a significant impact on the HMIs sensing performance. 1 wt% doping of Co and Fe into SnO2 shows highest selectivity (-2.3 - 8.4 (Co doping);-2-13 (Fe doping)) and sensitivity (-1.4 & mu;A/ppb (Co doping);-2.6 & mu;A/ppb (Fe doping)) toward cadmium (Cd (II)) and chromium (Cr (VI)) ions respectively as compared to other HMIs. Furthermore, reduced graphene oxide (rGO) functionalized Co doped and Fe doped SnO2 nanocomposites were synthesized using ultrasonication method. The rGO functionalized metal doped nanocomposites improve HMIs sensing performance than pure and metal doped SnO2 nanocomposites. According to electrochemical studies, the synthesis of nanocomposites with strong electrocatalytic activity and enhanced active surface area is primarily responsible for improved sensor response.

In the present era of artificial intelligence (AI), the real-time measurements using electronic skin, electronic tongue, and in particular, electronic noses are considered to be a pillar or backbone of data generation and corresponding analysis crucial for various industrial applications. The use of nano-e-noses has gained critical significance in health and environmental safety applications. They are the artificial olfaction systems that employ the multiple sensor arrays integrated with the various AI and machine learning algorithms. The current revolution in the e-noses has a major contribution to point of care devices, wireless sensors in robotics, wearable electronics, and self-powered devices. It is known that, for commercial-grade devices, the fulfillment of the 4-S (Sensitivity, Selectivity, Stability, and Suitability) criteria is the prerequisite. This can be achieved by engineering nanomaterials and patterned devices. Hence, reviewing the progress and current state of the art is very crucial for designing the industrial roadmap. This chapter provides a brief discussion on various nanomaterials and techniques used for developing nano e-noses. Nanomaterials have a high surface area to volume ratios and the ability to tune the physicochemical properties up to the atomic and molecular levels. In addition, the alignment of these nanomaterials in nanopatterns has defined and enabled a new class of electronic configuration called lab-on-a-chip or nanodevices. The overview of the concepts of nano-e-noses, nanomaterials wealth, corresponding fabrication tools, and state of the art is provided in detail. A brief discussion on the various statistical tools used in data analysis for successful discrimination of the output signals is also provided. The major emphasis has been provided to discuss the recent advances in the field citing some of the recent works of literature.

A depletion region formed at the junction of two materials depends on the charge carrier concentration and the barrier height. The depletion width can be controlled by the overlayer thickness. Modification of the depletion width after gas exposure is mainly contributing to the sensor response. The maximum effect of gas molecules can be realized when the depletion width becomes comparable to the Debye length. Consequently, the selection of an appropriate overlayer thickness is very crucial. In the present work, chemiresistive gas sensing characteristics of ZnO nanowire (NW) films modified with different thicknesses of reduced graphene oxide (rGO) have been studied. The hybrid films showed selective outstanding NO2 response as compared to pure ZnO NW films. At an optimum rGO thickness of similar to 15.3 nm, the response toward NO2 (with a minimum detection limit of 400 ppb) was found to be the highest that is nearly three times higher than that of pure ZnO nanowire films. A synergetic balance of the depletion width at the ZnO/rGO interface with the Debye length is attributed to the improved selective NO2 response of hybrid films.

We report the utilization of ZnO nanowires (NWs)-based e-nose towards successful discrimination of binary gaseous mixture comprising H 2 S and NO 2 gases. In particular, analysis of individual components in the binary mixture of gases has been carried out using different pattern recognition algorithms (PRA) or models. Of these, principal component analysis (PCA) indicated a successful discrimination of the gases. The maximum variance of three principal components were found to be 95.89, 3.53, and 0.56%, respectively. To cross validate the results, hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) studies have also been performed. Herein, by estimating the probability of the classes, an accurate prediction of the gases with minimal misclassification was achieved. Thus, using sequential application of the three basic PRAs on the data repository, a successful discrimination of the individual component of the binary mixture of gases was accomplished.

The threat of heavy metal ions (HMIs) in aqueous media to public health is gaining its concern all around the world. Thus, to measure the concentration, lower than the regulated level, synthesis of MoO 3 nanostructures using hydrothermal method was carried out. In this study, MoO 3 was synthesized from an aqueous solution containing ammonium heptamolybdate and different concentrations (0.1 and 0.3 M) of polyvinylpyrrolidone (PVP). Here, PVP stabilizes the particles and prevents them from agglomeration. X-ray diffraction analysis revealed an orthorhombic structure. Field emission scanning electron microscopy revealed that various concentrations of PVP are responsible for morphological modifications. A structure resembling a plate-like morphology were observed at 0.3-M PVP. According to UV–Vis spectroscopy, the bandgap energy is directly proportional to PVP concentration. Electrochemical analysis was used to evaluate the capabilities of synthesized nanostructures for HMIs detection. According to electrochemical experiments, the linear detection range for Pb (II) is 0.02–90 ppb. The higher surface area induced by the presence of PVP improved the material sensitivity by ~ 2 times as compared to material synthesized without PVP. Additionally, synthesized MoO 3 showed good selectivity toward Pb (II) than other HMIs. According to electrochemical studies, this research gives a valuable insight for understanding the effect of morphology on HMIs detection.

Heavy metal ions (HMIs) are known to cause severe damages to the human body and ecological environment. And considering the current alarming situation, it is crucial to develop a rapid, sensitive, robust, economical and convenient method for their detection. Screen printed electrochemical technology contributes greatly to this task, and has achieved global attention. It enabled the mass transmission rate and demonstrated ability to control the chemical nature of the measure media. Besides, the technique offers advantages like linear output, quick response, high selectivity, sensitivity and stability along with low power requirement and high signal-to-noise ratio. Recently, the performance of SPEs has been improved employing the most effective and promising method of the incorporation of different nanomaterials into SPEs. Especially, in electrochemical sensors, the incorporation of nanomaterials has gained extensive attention for HMIs detection as it exhibits outstanding features like broad electrochemical window, large surface area, high conductivity, selectivity and stability. The present review focuses on the recent progress in the field of screen-printed electrochemical sensors for HMIs detection using nanomaterials. Different fabrication methods of SPEs and their utilization for real sample analysis of HMIs using various nanomaterials have been extensively discussed. Additionally, advancement made in this field is also discussed taking help of the recent literature.

A high-performance ZnO nanowire-based e-nose using a multiple sensor array comprising four sensors has been demonstrated for its ability to successfully identify and classify four gases, namely CO2, H2S, NH3 , and NO2, from their mixture. For this, a combination of algorithms, namely principal component analysis (PCA), linear discriminant analysis (LDA), a support vector machine (SVM), and decision forest (DF) have been systematically utilized. A data repository was created using response curves, and the five extracted variables, namely sensor response, response time, area under the response curve, response slope, and concentration of gases. PCA analysis indicated clustering of the gases, having a variance of 66.39%, 33.01%, and 0.05% for the first three components, respectively. LDA has been successfully used to classify the gases, employing the approach of maximization of between-class variance. The SVM classifier with a radial basis function (RBF) kernel model showed the highest accuracy of 98.13% and 82.50% on the training data and the validation data, respectively. The DF classifier generated a tree in which four toxic gases were successfully classified. The confusion matrix showed a 0% out of box estimate error rate for the training dataset and a 2.5% test error rate for the validation dataset. The SVM and the DF classifier models demonstrated an excellent classification accuracy.