Micrometer-sized nanoporous tin dioxide spheres for gas sensing

We report the synthesis of mesoporous tin dioxide (SnO₂) materials with well-defined particle morphology. The products consist of uniform spheres with a diameter of 5 μm. The spheres are hierarchically porous with two distinct pore modes of 5.0 nm and 52 nm, respectively. This special porosity is the result of a synthesis procedure which involves a ‘hard templating’ (nanocasting) process. The product forms an approximately homogeneous monolayer of spheres on a sensor substrate and shows promising response to methane gas with low cross-sensitivity to water. The structural properties and gas-sensing performance are compared with a mesoporous SnO₂ material without defined morphology, prepared by a ‘soft templating’ procedure.     

Modification of the oxygen diffusivity in limiting current oxygen sensors

Effect of sacrificial carbon on microstructure of protective layer and sensing properties of limiting current oxygen sensors were studied. Graphite and carbon nanofiber with different concentrations were examined as sacrificial layer. Therefore, several YSZ-based electrochemical gas sensors were fabricated with dissimilarity in the diffusivity of the layer covering their measuring electrodes.Gas sensors were tested as potentiometric and amperometric devices under O₂-N₂ mixtures, and synthetic gases similar to the exhaust gas mixtures from combustion engines. Sensors with low diffusivity (low concentration of sacrificial material) exhibit more abrupt and O₂-sensitive potentiometric responses. Indeed, less O₂-linear amperometric responses and higher response times are their characteristics. Sensors with high diffusion in their layers (high concentration of sacrificial material) show a less abrupt step-drop potentiometric response but a more linear O₂ dependence for the amperometric response and, in general, lower response times.Results suggest that the configuration of the diffusive layer shall be controlled in terms of the expected performance of the sensor, as an amperometric or potentiometric device. The controlling parameters are discussed and given in this study.     

High sensitive and selective formaldehyde sensors based on nanoparticle-assembled ZnO micro-octahedrons synthesized by homogeneous precipitation method

Nanoparticle-assembled ZnO micro-octahedrons were synthesized by a facile homogeneous precipitation method. The ZnO micro-octahedrons are hexagonal wurtzite with high crystallinity. Abundant structure defects were confirmed on ZnO surface by photoluminescence. Gas sensors based on the ZnO micro-octahedrons exhibited high response, selectivity and stability to 1–1000 ppm formaldehyde at 400 °C. Especially, even 1 ppm formaldehyde could be detected with high response (S = 22.7). It is of interest to point out that formaldehyde could be easily distinguished from ethanol or acetaldehyde with a selectivity of about 3. The high formaldehyde response is mainly attributed to the synergistic effect of high contents of electron donor defects (Zn_i and V_O) and highly active oxygen species (O²⁻) on the ZnO surface.     

Novel e-nose for the discrimination of volatile organic biomarkers with an array of carbon nanotubes (CNT) conductive polymer nanocomposites (CPC) sensors

We report the original design of a new type of electronic nose (e-nose) consisting of only five sensors made of hierarchically structured conductive polymer nanocomposites (CPC). Each sensor benefits from both the exceptional electrical properties of carbon nanotubes (CNT) used to build the conductive architecture and the spray layer by layer (sLbL) assembly technique, which provides the transducers with a highly specific 3D surface structure. Excellent sensitivity and selectivity were obtained by optimizing the amount of CNT with five different polymer matrices: poly(caprolactone) (PCL), poly(lactic acid) (PLA), poly(carbonate) (PC), poly(methyl methacrylate) (PMMA) and a biobased polyester (BPR). The ability of the resulting e-nose to detect nine organic solvent vapours (isopropanol, tetrahydrofuran, dichloromethane, n-heptane, cyclohexane, methanol, ethanol, water and toluene), as well as biomarkers for lung cancer detection in breath analysis, has been demonstrated. Principal component analysis (PCA) proved to be an excellent pattern recognition tool to separate vapour clusters.     

Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

The effect of DNA probe distribution on the reliability of label-free biosensors

As the design of label-free DNA biosensors matures, and their sizes reduced to enhance their sensitivity, not much has been researched about the variations in the received signal with the positioning of the probes on the sensitive surface. We approach this issue computationally in this paper. By adopting the finite-element model on a three-dimensional biological field-effect transistor (BioFET) slice, and running Monte-Carlo simulations on the positions of the DNA molecules, we extract the expected variations in the signal. Then, we show that signal-to-noise (SNR) ratio can be low enough to hinder the functionality of the device, placing a limitation on how low the sensitivity of a sensor of a certain size can be.     

Hydrogen sensor based on Pd-functionalized film bulk acoustic resonator

We present an application of the Pd-functionalized film bulk acoustic resonator for hydrogen detection. The resonator consisted of an AlN piezoelectric stack and a Bragg reflector has a working resonance near 2.23 GHz and a high performance. A 50 nm thick Pd film was coated on the top electrode as a specific layer to capture hydrogen. The absorption of hydrogen can trigger changes of elastic properties of the Pd layer, which affects the resonance frequency of the resonator. The experimental results show that the Pd-functionalized film bulk acoustic resonator can yield a rapid, sensitive, reversible and reproducible response to hydrogen in the concentrations from 0.3% to 2%. The advantages of this sensor, including the simple fabrication process, ease of detection method, rapid response and high sensitivity and working at room temperature, make this promising in the early alarm of hydrogen leaking.     

Screen-printed carbon electrode for choline based on MnO2 nanoparticles and choline oxidase/polyelectrolyte layers

This paper presents the amperometric biosensor that determines choline and cholinesterase activity using a screen printed graphite electrode. In order to detect H₂O₂ we have blanket modified the electrode material with manganese dioxide nanoparticles layer. Using layer-by-layer technique on the developed hydrogen peroxide sensitive electrode surface choline oxidase was incorporated into the interpolyelectrolyte nanofilm. Its ability to serve as a detector of choline in bulk analysis and cholinesterase assay was investigated. We examined the interferences from red-ox species and heavy metals in the blood and in the environmental sample matrixes. The sensor exhibited a linear increase of the amperometric signal at the concentration of choline ranging from 1.3 × 10⁻⁷ to 1.0 × 10⁻⁴ M, with a detection limit (evaluated as 3σ) of 130 nM and a sensitivity of 103 mA M⁻¹ cm⁻² under optimized potential applied (480 mV vs. Ag/AgCl). The biosensor retained its activity for more than 10 consecutive measurements and kept 75% of initial activity for three weeks of storage at 4 °C. The R.S.D. was determined as 1.9% for a choline concentration of 10⁻⁴ M (n = 10) with a typical response time of about 10 s. The developed choline biosensor was applied for butyrylcholinesterase assay showing a detection limit of 5 pM (3σ). We used the biosensor to develop the cholinesterase inhibitor assay. Detection limit for chlorpyrifos was estimated as 50 pM.     

Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.     

SELM: Semi-supervised ELM with application in sparse calibrated location estimation

Indoor location estimation based on Wi-Fi has attracted more and more attention from both research and industry fields. It brings two significant challenges. One is requiring a vast amount of labeled calibration data. The other is real-time training and testing for location estimation task. Traditional machine learning methods cannot get high performance in both aspects. This paper proposed a novel semi-supervised learning method SELM (semi-supervised extreme learning machine) and applied it to sparse calibrated location estimation. There are two advantages of the proposed SELM. First, it employs graph Laplacian regularization to import large number of unlabeled samples which can dramatically reduce labeled calibration samples. Second, it inherits the good property of ELM on extreme training and testing speed. Comparative experiments show that with same number of labeled samples, our method outperforms original ELM and back propagation (BP) network, especially in the case that the calibration data is very sparse.