Functional materials simulation
Zeolites are well defined structures containing elements such as aluminum, silicon and oxygen in their framework. Their unique three dimensional structures contain micro sized pores which allow cations to be trapped inside. Zeolites have good adsorption, ion exchange and catalysis properties which e...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2016
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| Online Access: | http://hdl.handle.net/10356/66386 |
| _version_ | 1826113322498392064 |
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| author | Koh, Kenn Kyon Xyn |
| author2 | Su Haibin |
| author_facet | Su Haibin Koh, Kenn Kyon Xyn |
| author_sort | Koh, Kenn Kyon Xyn |
| collection | NTU |
| description | Zeolites are well defined structures containing elements such as aluminum, silicon and oxygen in their framework. Their unique three dimensional structures contain micro sized pores which allow cations to be trapped inside. Zeolites have good adsorption, ion exchange and catalysis properties which enable zeolites to be used in a wide range of applications such as waste water treatment, chemical catalysis and cleaning agents due to their cage-like structure. New zeolites are still being discovered and there are still millions of hypothetical zeolites to be proven. Characterization of such zeolites involves instrumental analysis involving X-ray diffraction and scanning electron microscopy to establish the lattice parameters of the unit cell and involves cross referencing to zeolite structures of known framework types. However with the large amount of framework types discovered, comparison with each individual framework type will take a long time thus it is required that the time taken involved be reduced such that new zeolites takes lesser time to be listed in the International Zeolite Association database.
By using machine learning algorithms in the WEKA software, a framework type predictor is built to perform prediction on zeolites with unknown framework type to narrow down the number of zeolites to cross reference when determining whether a synthesized zeolite is a new or reoccurring zeolite. The prediction models built using various classifiers produced highly accurate predictions on a training set with accuracy ranging from 98.53% to 100%. This high accuracy allowed us to identify the zeolite ZSM-5 Calcined as MFI framework type and Beta Polymorph A as BEA framework type correctly. Zeolites such as ETS-10 and PKU-16 had framework types not listed and had no similar framework types present in the database were also identified due to the varying predictions across individual models. |
| first_indexed | 2024-10-01T03:21:18Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/66386 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:21:18Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/663862023-03-04T15:43:24Z Functional materials simulation Koh, Kenn Kyon Xyn Su Haibin School of Materials Science and Engineering DRNTU::Engineering Zeolites are well defined structures containing elements such as aluminum, silicon and oxygen in their framework. Their unique three dimensional structures contain micro sized pores which allow cations to be trapped inside. Zeolites have good adsorption, ion exchange and catalysis properties which enable zeolites to be used in a wide range of applications such as waste water treatment, chemical catalysis and cleaning agents due to their cage-like structure. New zeolites are still being discovered and there are still millions of hypothetical zeolites to be proven. Characterization of such zeolites involves instrumental analysis involving X-ray diffraction and scanning electron microscopy to establish the lattice parameters of the unit cell and involves cross referencing to zeolite structures of known framework types. However with the large amount of framework types discovered, comparison with each individual framework type will take a long time thus it is required that the time taken involved be reduced such that new zeolites takes lesser time to be listed in the International Zeolite Association database. By using machine learning algorithms in the WEKA software, a framework type predictor is built to perform prediction on zeolites with unknown framework type to narrow down the number of zeolites to cross reference when determining whether a synthesized zeolite is a new or reoccurring zeolite. The prediction models built using various classifiers produced highly accurate predictions on a training set with accuracy ranging from 98.53% to 100%. This high accuracy allowed us to identify the zeolite ZSM-5 Calcined as MFI framework type and Beta Polymorph A as BEA framework type correctly. Zeolites such as ETS-10 and PKU-16 had framework types not listed and had no similar framework types present in the database were also identified due to the varying predictions across individual models. Bachelor of Engineering (Materials Engineering) 2016-03-31T07:26:56Z 2016-03-31T07:26:56Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/66386 en Nanyang Technological University 37 p. application/pdf |
| spellingShingle | DRNTU::Engineering Koh, Kenn Kyon Xyn Functional materials simulation |
| title | Functional materials simulation |
| title_full | Functional materials simulation |
| title_fullStr | Functional materials simulation |
| title_full_unstemmed | Functional materials simulation |
| title_short | Functional materials simulation |
| title_sort | functional materials simulation |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/66386 |
| work_keys_str_mv | AT kohkennkyonxyn functionalmaterialssimulation |