Unmodified silver nanoparticles for rapid analysis of the organophosphorus pesticide, dipterex, often found in different waters

A novel, uncomplicated and rapid method of analysis for organophosphorus (OP) pesticides was researched and developed using the important, common OP, dipterex, as a typical example. The basis of the method involved the citrate-capped silver nanoparticles (citrate-capped AgNPs) and Acetylthiocholine (ATCh). The latter compound can be catalyzed by Acetylcholinesterase (AChE) to form thiocholine (TCh), which induces the aggregation of AgNPs. Correspondingly, the color of AgNPs in solution changes from bright yellow to pink, and the UV–vis characteristic absorption peak of AgNPs at about 400 nm decreases; simultaneously, a new absorption band appears at about 520 nm. Irreversible inhibition of AChE activity caused by dipterex, prevents the aggregation of AgNPs. Thus, a UV–vis spectrophotometric method was developed for the analysis of dipterex. The absorbance ratio A396 nm/A520 nm was found to be linearly related to the concentration of dipterex in the range of 0.25–37.5 ng mL−1 with a detection limit of 0.18 ng mL−1. This method was used successfully to analyse dipterex in spiked, different water samples.

Eye-safe UV stand-off Raman spectroscopy for explosive detection in the field

This project focused on maximising the detection range of an eye-safe stand-off Raman system for use in detecting explosives. Investigation of the effect on detection range through differing laser parameters in this thesis provided optimal laser settings to achieve the largest possible detection range of explosives, while still remaining under the eye-safe limit.

Pt(II) bipyridyl complexes bearing substituted fluorenyl motif on the bipyridyl and acetylide ligands : synthesis, photophysics, and reverse saturable absorption

A series of Pt(II) diimine complexes bearing benzothiazolylfluorenyl (BTZ-F8), diphenylaminofluorenyl (NPh2- F8), or naphthalimidylfluorenyl (NI-F8) motifs on the bipyridyl or acetylide ligands (Pt-4−Pt-8), (i.e., {4,4′-bis[7-R1-F8-(≡)n-]bpy}Pt(7- R2-F8- ≡ -)2, where F8 = 9,9′-di(2-ethylhexyl)fluorene, bpy = 2,2′- bipyridine, Pt-4: R1 = R2 = BTZ, n = 0; Pt-5: R1 = BTZ, R2 = NI, n = 0; Pt-6: R1 = R2 = BTZ, n = 1; Pt-7: R1 = BTZ, R2 = NPh2, n = 1; Pt- 8: R1 = NPh2, R2 = BTZ, n = 1) were synthesized. Their ground-state and excited-state properties and reverse saturable absorption performances were systematically investigated. The influence of these motifs on the photophysics of the complexes was investigated by spectroscopic methods and simulated by time-dependent density functional theory (TDDFT). The intense absorption bands below 410 nm for these complexes is assigned to predominantly 1π,π* transitions localized on either the bipyridine or the acetylide ligands; while the broad low-energy absorption bands between 420 and 575 nm are attributed to essentially 1MLCT (metal-to-ligand charge transfer)/ 1LLCT (ligand-to-ligand charge transfer) transitions, likely mixed with some 1ILCT (intraligand charge transfer) transition for Pt-4−Pt-7, and predominantly 1ILCT transition admixing with minor 1MLCT/1LLCT characters for Pt-8. The different substituents on the acetylide and bipyridyl ligands, and the degrees of π-conjugation in the bipyridyl ligand influence both the 1π,π* and charge transfer transitions pronouncedly. All complexes are emissive at room temperature. Upon excitation at their respective absorption band maxima, Pt-4, Pt-6, and Pt-8 exhibit acetylide ligand localized 1π,π* fluorescence and 3MLCT/3LLCT phosphorescence in CH2Cl2, while Pt-5 manifests 1ILCT fluorescence and 3ILCT phosphorescence. However, only 1LLCT fluorescence was observed for Pt-7 at room temperature. The nanosecond transient absorption study was carried out for Pt-4−Pt-8 in CH3CN. Except for Pt-7 that contains NPh2 at the acetylide ligands, Pt-4−Pt-6 and Pt-8 all exhibit weak to moderate excited-state absorption in the visible spectral region. Reverse saturable absorption (RSA) of these complexes was demonstrated at 532 nm using 4.1 ns laser pulses in a 2 mm cuvette. The strength of RSA follows this trend: Pt-4 > Pt-5 > Pt-7 > Pt-6 > Pt-8. Incorporation of electron-donating substituent NPh2 on the bipyridyl ligand significantly decreases the RSA, while shorter π-conjugation in the bipyridyl ligand increases the RSA. Therefore, the substituent at either the acetylide ligands or the bipyridyl ligand could affect the singlet and triplet excited-state characteristics significantly, which strongly influences the RSA efficiency.

Binding properties of peptidic affinity ligands for plasmid DNA capture and detection

Peptides constructed from α-helical subunits of the Lac repressor protein (LacI) were designed then tailored to achieve particular binding kinetics and dissociation constants for plasmid DNA purification and detection. Surface plasmon resonance was employed for quantification and characterization of the binding of double stranded Escherichia coli plasmid DNA (pUC19) via the lac operon (lacO) to "biomimics" of the DNA binding domain of LacI. Equilibrium dissociation constants (K D), association (k a), and dissociation rates (k d) for the interaction between a suite of peptide sequences and pUC19 were determined. K D values measured for the binding of pUC19 to the 47mer, 27mer, 16mer, and 14mer peptides were 8.8 ± 1.3 × 10 -10 M, 7.2 ± 0.6 × 10 -10 M, 4.5 ± 0.5 × 10 -8 M, and 6.2 ± 0.9 × 10 -6 M, respectively. These findings show that affinity peptides, composed of subunits from a naturally occurring operon-repressor interaction, can be designed to achieve binding characteristics suitable for affinity chromatography and biosensor devices.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Green-fluorescent protein facilitates rapid in vivo detection of genetically transformed plant cells

Early detection of plant transformation events is necessary for the rapid establishment and optimization of plant transformation protocols. We have assessed modified versions of the green fluorescent protein (GFP) from Aequorea victoria as early reporters of plant transformation using a dissecting fluorescence microscope with appropriate filters. Gfp-expressing cells from four different plant species (sugarcane, maize, lettuce, and tobacco) were readily distinguished, following either Agrobacterium-mediated or particle bombardment-mediated transformation. The identification of gfp-expressing sugarcane cells allowed for the elimination of a high proportion of non-expressing explants and also enabled visual selection of dividing transgenic cells, an early step in the generation of transgenic organisms. The recovery of transgenic cell clusters was streamlined by the ability to visualize gfp-expressing tissues in vitro.

Application of anomaly technique in wind turbine bearing fault detection

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.