Biomedical subjects

Adam K Wanekaya

Publications and source records attributed to Adam K Wanekaya.

3 recordsLinked to original sources

PubMed2005

Nanostructured polyamic acid membranes as novel electrode materials.

This paper describes a new approach for the preparation of polyamic acid (PAA) composites containing Ag and Au nanoparticles. The composite film of PAA and metal particles were obtained upon electrodeposition of a PAA solution containing gold or silver salts with subsequent thermal treatment, while imidization to polyimide is prevented. The structural characterization of the films is provided by 1H NMR and Fourier transform infrared spectroscopy (FTIR), while the presence of metallic nanoparticles within the polymeric matrix was confirmed by scanning electron microscopy (SEM), cyclic voltammetry (CV), energy-dispersive X-ray analysis (EDX), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). This approach utilizes the unique reactivity of PAA by preventing the cyclization of the reactive soluble intermediate into polyimides at low temperature to design polymer-assisted nanostructured materials. The ability to prevent the cyclization process should enable the design of a new class of electrode materials by use of thermal reduction and/or electrodeposition.

Benzene Derivatives↗

PubMed2002

Pressure assisted chelating extraction: a novel technique for digesting metals in solid matrices.

This work describes a novel technique for the digestion of metals in solid matrices. The technique is called pressure assisted chelating extraction (PACE). In a typical procedure, a solid sample is placed in a stainless steel cell and is mixed with appropriate chelating agents. Using a programmed sequence of temperature, static time, pressure and thermal equilibration available in ASE 200, the metal is removed under moderate temperature (up to 200 degrees C) and pressure (up to 3000 psi). PACE achieves metal recovery that is equivalent to that of wet digestion techniques and also provides for a clean and safe operation by substituting the strong acids commonly used during wet digestion with chelating agents. It uses less solvents and significantly less time (minutes vs. hours) for metal digestion. PACE has been validated using certified standard reference materials (SRMs) including industrial sludge, buffalo river sediments and coal fly ash. The total time required to remove metals was approximately 20 min. Results show that the PACE system provides an ideal platform for efficient, rapid, and safe metal digestion. Good agreement between measured and reference values for Pb, Mn, and Cu were found with recoveries averaging between 80 and 101% and a relative standard deviation of less than 5%. This approach may provide an alternative digestion technique for environmental samples, alloys, biological materials and samples of geological importance. The potential advantage offered lies in non-destruction of the sample, automation and the exclusion of concentrated mineral acids during the digestion procedure.

Chelating Agents↗

PubMedDate not supplied

Detection and classification of organophosphate nerve agent simulants using support vector machines with multiarray sensors.

The need for rapid and accurate detection systems is expanding and the utilization of cross-reactive sensor arrays to detect chemical warfare agents in conjunction with novel computational techniques may prove to be a potential solution to this challenge. We have investigated the detection, prediction, and classification of various organophosphate (OP) nerve agent simulants using sensor arrays with a novel learning scheme known as support vector machines (SVMs). The OPs tested include parathion, malathion, dichlorvos, trichlorfon, paraoxon, and diazinon. A new data reduction software program was written in MATLAB V. 6.1 to extract steady-state and kinetic data from the sensor arrays. The program also creates training sets by mixing and randomly sorting any combination of data categories into both positive and negative cases. The resulting signals were fed into SVM software for "pairwise" and "one" vs all classification. Experimental results for this new paradigm show a significant increase in classification accuracy when compared to artificial neural networks (ANNs). Three kernels, the S2000, the polynomial, and the Gaussian radial basis function (RBF), were tested and compared to the ANN. The following measures of performance were considered in the pairwise classification: receiver operating curve (ROC) Az indices, specificities, and positive predictive values (PPVs). The ROC Az) values, specifities, and PPVs increases ranged from 5% to 25%, 108% to 204%, and 13% to 54%, respectively, in all OP pairs studied when compared to the ANN baseline. Dichlorvos, trichlorfon, and paraoxon were perfectly predicted. Positive prediction for malathion was 95%.

Journal Article↗