SnO2 is one of the most studied materials in gas sensing and is often used as benchmark for other metal oxide based gas sensors. Among the many strategies adopted to optimize its sensing properties, the fine tuning of the morphology in nanoparticles, nanowires, nanosheets and their eventual hierarchical organization has become an active field of research in the last years.

In this work, with the aim to have a more general and reliable picture of the state of the art, results published in literature in the last five years are systematically analyzed focusing on response intensities recorded with chemiresistors based on pure SnO2 for ethanol detection in dry air.

The statistics is discussed on the basis of descriptive morphological parameters such as the crystallite shape, which may be in the form of nanoparticles, nanowires, nanosheets, and their eventual hierarchical assembly, including fibers, spheres, hollow spheres.

Results indicate that no morphology clearly outperforms others, while a few individual sensors emerge as remarkable outliers with respect to the whole dataset. Interestingly, an appreciable number of outliers feature the morphology of the thick film traditionally employed in the field, i.e. a thick, random network of SnO2 nanoparticles. This result is interpreted in terms of the longer tradition of the thick film approach with respect those of nanowires/nanosheets and hierarchical structures. Such a longer experience may reasonably imply a more developed capability to effectively combine the many parameters underlying the sensing mechanism, which may counterbalance the advantages arising from the fine morphological tuning inherent in the more recent nanostructures.

Mixed nanocomposites combined both organic and inorganic compounds extend our capabilities to form nanostructured architectures of advanced functionality for different sensing applications. Such architectures can be used in sensors and systems of medical diagnostics, environmental monitoring etc, in particular, those based on the transducers of surface plasmon resonance. Usually when investigating bio- and chemospecific interactions typical approach is the immobilization at the sensor surface of the one reagent (receptor) and detection of its binding to other ones (analytes) one by one, which rest in the solution. However in real situation simultaneous multicomponent interactions are much more typical.

The formation of the complex nanocomposite based on the mix of polysaccharide and metal nanoparcicles can be a possible way to study multicomponent interactions in real conditions. Au-glycane nanocomposite was obtained by reduction of metal from HAuCl₄ salt under the base condition where glycane play a role of macromolecular reducer and stabilizer as was confirmed by absorption spectra of product demonstrating the presence of the wide band with the maximum near 560 nm specific for local surface plasmon excitation in gold nanostructures. As a model reaction the interaction of above mentioned nanocomposite with Tobacco Mosaic Virus (TMV) and specific antibodies was chosen. Untreated virus and virus, firstly pretreated by above mentioned nanocomposite, were then incubated with antibodies. Further observation of the complex immobilization to the sensor surface demonstrated that the complexation with the nano-composite changed the binding capabilities of the virus. Antiviral activity of the complex was also demonstrated in vivo at the Datura stramonium

Advanced Driver Assistance Systems are intelligent systems located inside vehicles to assist the main driver in a variety of ways. These systems may be used to provide useful traffic information, but may also be used to evaluate whether or not the driver is in physical conditions to drive.

Among other driver-related risk factors (e.g. drug intake or altered emotional state), alcohol abuse remains the dominant cause of fatal car accidents (about 25% of all road deaths in Europe).

For this reason, our research activity is aimed at the realization of an in-vehicle driver breath ethanol detection system.

To facilitate the large-scale implementation of these systems, the design of inexpensive, reliable and easy to prepare sensors is required. Iron oxide possesses all these characteristics, and it has been shown to have a remarkable sensing capacity for ethanol. For these reasons, it is an ideal candidate as a material to be used for alcohol level monitoring in ADAS systems.

In this work, a simple Pechini sol-gel process was used to prepare a stable water solution used to print a thin film on ceramic sensor substrates. The α-Fe₂O₃ thin film-based sensor obtained exhibits a highly selective sensing behaviour towards ethanol and a very stable response in time.

The Fe₂O₃-based material has been employed in a breath ethanol detection system experimental setup given the promising preliminary results.

Tests were performed by placing the ethanol sensor within the casing of a car upper steering column, for simulating driving position.

The main challenge in the development of this system is related to the complexity of breath composition and its high humidity content, coupled with the high dilution of breath reaching the sensor.

For this reason, it was necessary to install the ethanol sensor into an array that also contains humidity and CO2 sensors (the latter, a compact ND-IR sensor was employed as an internal standard).

Finally, through the simultaneous use of these three sensors, it was possible to differentiate the signal of a driver's breath before and after alcohol consumption.

Influenza A virus belongs to the Orthomyxoviridae family and to date is one of the most important pathogens causing acute respiratory infections, such as the recent pandemic of 2009. Hemagglutinin (HA) is one of the surface proteins of the virus that allow it to interact with cellular molecules. Due to the reason that is the most abundant protein in the virus capsule, make it the best target in the detection of Influenza A H1N1 virus through biosensing devices. Our aim is to develop an electrochemical biosensor to detect H1 by modifying carbon screen printed electrodes with gold nanoparticles (size of 10-20 nm) and further functionalization with monoclonal antibodies specific to this protein. The electrodes were characterized by the means of cyclic voltammetry, differential pulse voltammetry and electrochemical impedance spectroscopy. The electrode is coupled to a 3D printed droplet guidance microfluidic system to ensure homogeneous distribution across the electrode. Our preliminary results suggest that the selected monoclonal antibodies have acceptable affinity and bind effectively to the H1 protein and that the electrodes have a wide potential window in the presence of Fe³⁺/Fe²⁺cyanide. In the future, we will continue to develop this biosensor in hope that it will be commercialized and be common in medical procedures during flu seasons and future influenza pandemics.

Himanthalia elongata (HE), commonly named sea spaghetti, is a brown alga from the order Fucales, mainly found in the North Atlantic Ocean and in the Baltic and North Seas. This algae is currently used in numerous functional applications in the food, pharmaceutical and nutraceutical industries, due to its biological properties such as antioxidants, anti-inflammatory, antimicorbial and anticancer and other health benefits. These effects are reported to be attributed to the high content of nutrients and their secondary metabolites such as phenolic compounds. In this context, the objective of this study is to optimize the microwave-assisted extraction method (MAE) to recover phenolic compounds and flavonoids, taking into account three extraction parameters: the ethanol concentration in water (0-100%), the extraction time (3-25 minutes) and the pressure (2-20 bar). In order to verify the total phenolic content (TPC) and the total flavonoid content (TFC), two biological tests were carried out based on the radical scavenging activity 2,2-diphenyl-1-picrylhydrazyl (DPPH) and the antioxidant capacity trolox equivalent (TEAC). Subsequently, the optimization was carried out by response surface methodology (RSM) considering the performance (Y) of each fraction. Finally, the optimized MAE parameters were fixed at 16.01 ± 4.80 min, 20 ± 0.50 bar and 0% of ethanol. These results show that water is an ideal environmenally friendly solvent for the extraction of bioactive HE compounds with some beneficts from a industrially point of view due to its nature. Furthermore, the resulting RSM was a successful model to establish the optimal conditions of MAE and a suitable methodology to maximize the content of polyphenols and total flavonoids, as well as the antioxidant capacity and the SM extraction performance.

Tetracycline (TC) is a broad-spectrum antibiotic that has been widely used for numerous infection treatment due to its strong inhibitory effect on pathogenic microorganisms, low toxicity and low-cost. However, the abuse of TC may cause its residues in foods, such as meat and milk. Intake of these TC-contaminated foods could promote bacterial resistance to antibiotics. In this work, we report a simple and low-cost nanoprobe method with high selectivity and sensitivity for TC detection. The nanoprobe was developed by chelating europium (III) ion (Eu³⁺) onto the surface of silica nanoparticles (SiNPs-Eu³⁺). The β-diketone configuration of TC can further coordinate with surface Eu³⁺ steadily, then absorb and transfer the excitation energy to Eu³⁺ via “antenna effect” upon UV light irradiation. The SiNPs-Eu³⁺ nanoprobe was weakly luminescent in buffer solution. In the presence of TC, a strong emission at 615 nm was observed upon the excitation at 390 nm. This SiNPs-Eu³⁺ nanoprobe was featured with a wide linear range (5 nM - 10 µM), high sensitivity (LoD = 1 nM), quick response (30 min), allowing it to be used for TC detection in real-world samples.

High temperature gas sensors are mainly designed to solve gas detection and monitoring problems with high operating temperature environment, such as gas turbine, nuclear power plants and automobile internal combustion engine emission. Cost effective metal oxide based gas sensors operate mostly at temperatures <400 °C. There are only few reports in literature focusing on their gas sensing above 400 °C. Titanium dioxide (TiO₂) is one of them to be capable of operating at and above 600 °C. However, TiO₂ is a high resistive n-type semiconductor with relatively poor conductivity for sensing oxidative gases such as nitrogen dioxide (NO₂). This disadvantage was previously reported to be overcome through addition of low valence dopant atoms which alter the electronic structure. Another strategy is to use catalytically doped perovskite based titanium compounds such as BaTiO₃. In this work, we report synthesis of Co and Ni doped TiO₂, Rh-doped BaTiO₃ by co-precipitation method and demonstrate gas sensing ability above 500 °C. Our results yield that Co-doping of TiO₂ promotes p-type behavior exhibiting good sensing properties to NO₂ while Ni-doping displays the maintenance of n-type behavior and better H₂-sensing properties at 600 °C. More interestingly, Rh-doped BaTiO₃ shows excellent NO sensing properties even at 900 °C

Urbanization is causing people to live close to chicken houses. Their large number leads to a deterioration of air quality, which in turn leads to an increase in complaints from the population. In order to counteract the adverse effects of chicken farming, malodorous air from poultry farms needs to be characterized using appropriate tools. This would give an idea of the degree and source of pollution in order to reduce the impact on the environment. This study aimed to test the ability of the developed e-nose based on six gas sensors to analyze odorous emissions from three poultry farms located in Meknes (Morocco) and Berlin (Germany). This pilot study was also carried out on odorous air samples in one week at different times of the day. Pattern recognition methods such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Support Vector Machines (SVM), and Discriminant Function Analysis (DFA) were used to process the dataset. Moreover, the gas sensors' sensitivity towards hydrogen sulfide, ammonia, and ethanol was also investigated. The finding results reveal that the developed system is able to differentiate the volume fractions of the analyzed gases. Furthermore, the relative humidity values have an effect of less than 1.6% on the gas sensor responses when the relative humidity increases from 15% to 67%. Data processing, using PCA, HCA, and SVM, shows clear discrimination between the odorous air samples collected from the three chicken farms, without any overlap with clean air. The same trend is obtained between odorous air samples collected at different days and times in a poultry farm using DFA and SVMs methods. From the relevant results, it can be concluded that the developed artificial sensory system can clearly classify and assess odorous air from poultry farms.

In recent years, wastewater treatment plants (WWTPs) have been identified as important emitters of microparticles into aquatic systems. These include preservatives from cosmetic products, such as Triclosan (TCS). It is therefore desirable to develop simple and portable tools for wastewater analysis. In this context, this paper highlights TCS analysis in wastewater samples, using an electronic nose (e-nose) and a molecularly imprinted polymer (MIP) sensor. The wastewater samples are collected at the Kasba site (Meknes, Morocco). First, qualitative analysis was performed using the e-nose based on an array of 6 semi-conductors gas sensors. Wastewater samples spiked with TCS at different concentrations (0.1-1000 pg/mL) were exposed to the e-nose. As result, the conductances increase with increasing TCS concentrations. Thus, the e-nose could classify wastewater samples depending on their TCS concentrations. Furthermore, using radar plots, a clear variation between these samples spiked with different TCS concentrations was remarked. Second, an electrochemical sensor based on MIP was developed for the quantitative detection of TCS in order to validate the outcomes from e-nose. The electrochemical MIP sensor was fabricated by polymerizing polyacrylamide onto a screen-printed gold electrode (Au-SPE). Under optimal conditions, the MIP sensor exhibits proportional responses to TCS concentrations in the same range as the e-nose of 0.1-1000 pg/mL with a detection limit of 0.23 pg/mL. The MIP sensor was applied for TCS detection in the same wastewater samples as e-nose with the recovery rates in the range of 99-121 % demonstrating the excellent trueness and reliability of the developed sensor device. In the aim of correlating results from the e-nose and the MIP sensor, a partial least squares (PLS) prediction model was established with a regression correlation coefficient R = 0.98. Correspondingly, both electro-analytical devices could be viable tools for monitoring cosmetic product residues in wastewater matrices.

Simultaneous detection of codeine (COD) and diclofenac (DCF) is of crucial importance as their overconsumption has recently been widely noticed. For this purpose, a biomimetic sensor (bi-MIP) is devised, and an electronic tongue is used to analyze water samples containing these drugs. Advantageous properties of a conductive polymer (methacrylic acid) and metallic nanoparticles (silver nanoparticles) were exploited to develop the bi-MIP sensor. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and differential pulse voltammetry (DPV) were used as electrochemical characterization techniques. The proposed bi-MIP sensor seems to be a promising tool for the analysis of drugs in water samples. The limits of detection for DCF and COD took individually were 0.01 µg/mL and 0.16 µg/mL, respectively. Due to cross-reactivity, a Voltammetric Electronic-tongue (VE-tongue) was used to perform simultaneous detection of both drugs in mineral water samples. Its responses were processed by principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs). First, all samples were well classified according to their enrichment type, with a success score of 78.90% for PCA, 89.99% for DFA, and 99.50% for SVMs. Secondly, the VE-tongue was able to differentiate samples containing both analytes at different concentrations. The results of this study reflect on the one hand the feasibility of simultaneous detection of two target analytes via a biomimetic sensor. On the other hand, they demonstrate the ability of a multi-sensor to classify water samples according to their type and level of contamination.