Prediction of behaviour kinetics and toxicity of engineered nanomaterials in aqueous environment using neural networks
| dc.contributor.advisor | Musee, Ndeke | en_ZA |
| dc.contributor.author | Ondiaka, Mary Nelima | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering. | en_ZA |
| dc.date.accessioned | 2016-12-22T13:43:31Z | |
| dc.date.available | 2016-12-22T13:43:31Z | |
| dc.date.issued | 2016-12 | |
| dc.description | Thesis (PhD)--Stellenbosch University, 2016. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT: As the global market for engineered nanomaterials (ENMs) continues to grow, the release of ENMs into the environment expose fauna and flora to new diverse stressors. Consequently, a novel approach is required in the monitoring and mitigation of the pollution from ENMs. In this approach, modelling is likely to play a significant role in estimating the release, bioavailability, and toxicity of ENMs in the environment. Many laboratory tests have established the toxic effects associated with the acute and chronic exposure of various organisms to ENMs. However, most of the information generated from these tests and reported in the scientific literature is unstructured and uncertain. A further complication is that unlike with other pollutants, extrapolating experimental findings to assess the environmental risk of ENMs is difficult, owing to their diversity, lack of standardized test protocols and unknown allowable environmental concentrations. Dissimilar ENMs would require case-by-case risk evaluation, a process that would be expensive and time-consuming. Focusing on nTiO2 as a model ENM, and algae and Daphnia magna as indicator organisms, this dissertation presents learning from a database derived from information gathered from the scientific literature. More specifically, an ensemble model trained using multilayer perceptron neural network (MLP-NN) predicts mass coverage of organic adsorbates on nTiO2, as well as the hydrodynamic size of nTiO2 particles. These two response variables represent the behaviour kinetics of the particles in aquatic conditions. Also, the toxicity of the particles was predicted from selected characteristics of nTiO2 and assay water, as well as some biological factors. The neural network models could subsequently be interrogated to establish the effect of the various predictors of the behaviour of the particles associated with their environmental risk. This approach lays a foundation for the data mining of ENMs that would facilitate the use of available data to estimate the ecological impact of nTiO2, as well as other ENMs. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING: In die benadering, is dit waarskynlik dat modellering ‘n beduidende rol sal speel in die beraming van die vrystelling, biobeskikbaarheid en toksisiteit van INMe in die omgewing. Laboratoriumtoetse het die toksiese effekte bevestig wat gepaard gaan met die akute en chroniese blootstelling van organismes aan INMe. Meeste van die inligting wat deur die toetse gegenereer word en in die wetenskaplike literatuur gerapporteer word, is egter ongestruktureerd en onseker. ‘n Verdere komplikasie is dat anders as met ander besoedelingstowwe, is dit moeilik om vanaf eksperimentele bevindings te ekstrapoleer om die omgewingsrisiko van INMe te beraam, a.g.v. hulle diversiteit, gebrek aan gestandaardiseerde protokolle en onbekende toelaatbare omgewingskonsentrasies. Daphnia magna as indicator organisms Deur te fokus op nTiO2 as ‘n model-INM, en alge en Daphnia magna as indikatororganismes, bied hierdie proefskrif leer aan vanaf ‘n databasis wat afgelei is van inligting wat uit die wetenskaplike literatuur versamel is. Meer spesifiek, ‘n ensemble van veellaag-perseptron- neurale netwerke is gebruik om die akkumulasie van organiese adsorbate op nTiO2, asook die hidrodinamiese grootte van nTiO2 deeltjies te voorspel. Die twee responsveranderlikes is gebruik om die kinetiese gedrag van nTiO2 te benader. Daarby is die toksisiteit van die deeltjies ook voorspel vanaf geselekteerde eienskappe van nTiO2 en toetswater, sowel as ‘n aantal biologiese faktore. Die neurale netwerkmodelle kon gevolglik geïnterrogeer word om die effek van die onderskeie voorspellers op die gedrag van die deeltjies wat met omgewingsrisiko geassosieer word, vas te stel. Die benadering lê die fondament vir die bevordering van data-ontginning van INMe wat die gebruik van beskikbare data om die omgewingsimpak van nTiO2, sowel as ander INMe te beraam. | af_ZA |
| dc.format.extent | 256 pages : illustrations | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/100350 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Neural networks (Computer science) | en_ZA |
| dc.subject | Nanostructured materials | en_ZA |
| dc.subject | Bioavailability | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.title | Prediction of behaviour kinetics and toxicity of engineered nanomaterials in aqueous environment using neural networks | en_ZA |
| dc.type | Thesis | en_ZA |