Robert Edwards

Bio: Robert Edwards is an academic researcher from Liverpool John Moores University. The author has contributed to research in topics: Optical fiber & Fiber optic sensor. The author has an hindex of 8, co-authored 15 publications receiving 693 citations.

A novel technique for optical fiber pH sensing based on methylene blue adsorption

Abstract: A novel fiber optic pH sensor is reported. The sensor is based upon surfacic adsorption of methylene blue dye, producing absorption in the evanescent field surrounding the sensing fiber. The sensor is based upon a 200 /spl mu/m diameter Plastic Clad Silica (PCS) fiber which has its cladding removed in order to expose its core. The linear range of operation is between pH 3 and pH 9 and its response time is considerably shorter than indicator based probes which measure spectral changes of pH sensitive chemicals. Effects of measurand ionic strength are shown to be negligible which is a further important advantage of this sensor over other optical fiber pH sensors. The sensor design is sufficiently flexible to allow it to be configured in probe (reflective) or in-line (transmissive) form. The latter allows the application of time domain reflectometry technique which may be used to achieve multipoint (distributed) sensing. >

Assessment and modeling of the toxicity of organic chemicals to Chlorella vulgaris: development of a novel database.

Abstract: This study reports a database of toxicity values for 91 compounds assessed in a novel, rapid, and economical 15 min algal toxicity test. The toxicity data were measured using the unicellular green alga Chlorella vulgaris in an assay that determined the disappearance of fluorescein diacetate. The chemicals tested covered a wide range of physicochemical properties and mechanisms of action. Quantitative activity-activity relationships with the toxicity of the chemicals to other species (Tetrahymena pyriformis, Vibrio fischeri, and Pimephales promelas) showed strong relationships, although some differences resulting from different protocols were established. Quantitative structure-activity relationships (QSARs) were determined using linear [multiple linear regression (MLR)] and nonlinear [k-nearest neighbors (KNN)] methods. Three descriptors, accounting for hydrophobicity, electrophilicity, and a function of molecular size corrected for the presence of heteroatoms, were found to be important to model toxicity. The predictivity of MLR was compared to KNN using leave-one-out cross-validation and the simulation of an external test set. MLR demonstrated greater stability in validation. The results of this study showed that method selection in QSAR is task-dependent and it is inappropriate to resort to more complicated but less transparent methods, unless there are clear indications (e.g., inability of MLR to deal with the data set) for the need of such methods.

The importance of hydrophobicity and electrophilicity descriptors in mechanistically-based QSARs for toxicological endpoints

Abstract: Quantitative structure-activity relationship (QSAR) analysis of four toxicological data sets is described. The toxicological data include three data sets retrieved from the literature (the toxic and metabolic effects of 23 aliphatic alcohols on the perfused rat liver; the toxicity of 21 pyridines to mice; the lethality of 55 halogenated hydrocarbons to the mould Aspergillus nidulans ). In addition, the toxicity of 13 mono- and di-substituted nitrobenzenes in a 15 min assay using the alga Chlorella vulgaris was analysed. QSARs were developed successfully using descriptors to describe uptake in the organism (i.e. hydrophobicity as quantified by the logarithm of the octanol-water partition coefficient, log P ) and reactivity at the site of action (i.e. electrophilicity as quantified by the energy of the lowest unoccupied molecular orbital, E LUMO ). A further parameter describing molecular branching as also required to model the data for the aliphatic alcohols. The results demonstrate that mechanistically ba...

An Investigation Into the Mechanism by Which Synthetic Zeolites Reduce Labile Metal Concentrations in Soils

Abstract: The addition of synthetic zeolites and similar materials to metal contaminated soils has been shown to reduce soil phytotoxicity and to improve the quality of plant growth on such amended soils. To gain an understanding of the mechanism by which the phytotoxicity of contaminated soils is reduced when treated with synthetic zeolites, sequential extraction procedures and soil solution techniques have been used to identify changes associated with metal speciation in amended soils. Sequential extraction data and changes in soil solution composition are presented for three different contaminated soils, amended with three synthetic zeolites (P, 4A and Y) at concentrations of 0.5%, 1% and 5% w/w, or lime at 1%. The soils were collected from the site of a metal refinery, an old lead zinc mine spoil tip and from a field which had been treated with sewage sludge. After incubation of the zeolite treated soils for between one and three months, results showed a reduction in the metal content of the ammonium acetate fraction between 42% and 70%, depending on soil, zeolite and rate of addition, compared with the unamended soils. In addition, soil solution experiments indicated that synthetic zeolite amendments were more efficient at reducing metal content than comparable lime treatment. The mechanism by which synthetic zeolites reduce metal bioavailability in contaminated soils is discussed and compared to other amendments.

The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

Abstract: This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y-scrambling, (2) multiple leave-many-out cross-validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models.