Developing (Quantitative Structure Property Relationships) QSPR Techniques to Predict the Char Formation of Polybenzoxazines

This study uses the Molecular Operating Environment software (MOE) to generate models to calculate the char yield of polybenzoxazines (PBz). A series of benzoxazine (Bz) monomers were constructed to which a variety of parameters relating to the structure (e.g., water accessible surface, negative van der Waals surface area and hydrophobic volume, etc.) were obtained and a quantitative structure property relationships (QSPR) model was generated. The model was used to generate data for new Bz monomers with desired properties and a comparison was made of predictions based on the QSPR model with the experimental data. This study shows the quality of predictive models and confirms how useful computational screening is prior to synthesis

Developing (Quantitative Structure Property Relationships) QSPR Techniques to Predict the Char Formation of Polybenzoxazines

This study uses the Molecular Operating Environment software (MOE) to generate models to calculate the char yield of polybenzoxazines (PBz). A series of benzoxazine (Bz) monomers were constructed to which a variety of parameters relating to the structure (e.g., water accessible surface, negative van der Waals surface area and hydrophobic volume, etc.) were obtained and a quantitative structure property relationships (QSPR) model was generated. The model was used to generate data for new Bz monomers with desired properties and a comparison was made of predictions based on the QSPR model with the experimental data. This study shows the quality of predictive models and confirms how useful computational screening is prior to synthesis

Prediction and experimental validation of the char yield of crosslinked polybenzoxazines.

This study uses Molecular Operating Environment (MOE) to generate models to calculate the char yield of polybenzoxazines. A series of benzoxazine monomers were constructed to which a variety of parameters relating to the structure (e.g. water accessible surface, negative van der Waals surface area and hydrophobic volume, etc.) were obtained and a quantitative structure property relationships (QSPR) model was generated. The model was used to generate data for a new benzoxazine monomer and a comparison was made of predictions based on the QSPR models with the experimental data. This study shows the quality of predictive models and confirms how useful computational screening is prior to synthesis. In order to do that, the QSPR models were tested over a series of internal and external validation tests to explore their internal and external predictivity, prior to experimental validations which were performed later and reported in Chapter 7. The internal and external validations found out that the discrepancy in the general model (GM) which was initially thought to be a drawback to the model’s performance was actually not, as it does not compromise the model’s prediction accuracy, both internally and externally. The validation process also found that one of the structure-specific models, Ph-M (aniline-based benzoxazines) is externally predictive whilst another structure-specific model, the Ace-M (acetylenic-based polybenzoxazines) is not internally and externally predictive due to the too small training set that affects its predictivity performance. An acetylenic-based polybenzoxazine, poly(BA-apa) and a benzylamine-based polybenzoxazine, poly(BO-ba) have been successfully synthesised in this work. Both materials have been characterised using Fourier Transform – Infra Red Spectroscopy (FT-IR), Nuclear Magnetic Resonance (NMR) spectroscopy (both 1H and 13C) and Liquid Chromatography-Mass Spectrometry (LC-MS) to confirm their structures. These materials were analysed using Differential Scanning Calorimetry (DSC) to study their polymerisation behaviour and were later cured and taken further to Thermogravimetric Analysis (TGA) in order to investigate their thermal properties and the amount of char yield formed upon heating at 800 oC under an inert (nitrogen) atmosphere – which then will be used for experimental validation of the QSPR models. The study of DSC thermograms showed that both polymers exhibit a distinct polymerisation behaviour e.g. BA-apa went through two polymerisation reactions simultaneously (the oxazine ring opening polymerisation and the acetylene addition reaction) whilst BO-ba only polymerised via the ring opening reaction from the oxazine rings. It was also found that BA-apa has a lower polymerisation activation energy, consistent to its lower polymerisation temperature in comparison to the BO-ba. TGA analysis revealed that poly(BO-ba) formed an average of 44.35 % char yield and poly(BA-apa) on the other hand formed approximately 10 % higher char which is 56.28 %. The analysis also discovered that poly(BA-apa) synthesised in this work formed 15 % less char yield than previously reported in the literature (56.28 % vs. 71 %1) due to the shorter curing schedule. The final QSPR validation which is the experimental validation found that the char yield of poly(BO-ba) was predicted very well within 5-7 % error by both GM model and Ph-M. Ace-M which was reported earlier as not internally and externally predictive, has made a nearly accurate prediction towards the char yield of poly(BA-apa), close to the literature value of 71 %. The GM model has also made a close prediction to the Ace-M model, but these predictions deviated 15-17 % from the experimental poly(BA-apa) char yield measured in this work

Developing (Quantitative Structure Property Relationships) QSPR Techniques to Predict the Char Formation of Polybenzoxazines

This study uses the Molecular Operating Environment software (MOE) to generate models to calculate the char yield of polybenzoxazines (PBz). A series of benzoxazine (Bz) monomers were constructed to which a variety of parameters relating to the structure (e.g., water accessible surface, negative van der Waals surface area and hydrophobic volume, etc.) were obtained and a quantitative structure property relationships (QSPR) model was generated. The model was used to generate data for new Bz monomers with desired properties and a comparison was made of predictions based on the QSPR model with the experimental data. This study shows the quality of predictive models and confirms how useful computational screening is prior to synthesis

Prediction of the char formation of polybenzoxazines: The effect of heterogeneities in the crosslinked network to the prediction accuracy in quantitative structure-properties relationship (QSPR) model

Molecular Operating Environment (MOE) software has great potential when combined with the Quantitative Structure-Property Relationship (QSPR) approach, and was proven to be useful to make good prediction models for series of polybenzoxazines [1–3]. However, the effect of heterogeneities in the crosslinked network to the prediction accuracy is yet to be tested. It was found that polybenzoxazines with polymerisable functional group (e.g. acetylene-based benzoxazines) form up to 40% higher char yield compared to their analogue polybenzoxazines due to the contribution of the polymerisable functional group (e.g. ethynyl triple bond) in the cross-linked network. In order to investigate the effect of the inconsistent cross-linking network, a data set consisting of thirty-three benzoxazines containing various structures of benzoxazines was subdivided into two smaller data sets based on their functional group, either benzoxazines with polymerisable functional group (acetylene-based benzoxazines set (Ace-M)) or non-polymerisable functional group (aniline-based benzoxazines (Ani-M)). Char yield predictions for the polybenzoxazines for these data sets (Ace-M and Ani-M) were compared with the larger thirty-three polybenzoxazines data set (GM) to investigate the effect of the inconsistency in crosslink network on the quality of prediction afforded by the model. Prediction performed by Ace-M and Ani-M were found to be more accurate when compared with the GM with total prediction error of 3.15% from both models compared to the GM (4.81%). Ace-M and Ani-M are each better at predicting the char yields of similar polybenzoxazines (i.e. one model is specific for a polymerisable functional group; the other for non-polymerisable functional group), but GM is more practical as it has greater ‘general’ utility and is applicable to numerous structures. The error shown by GM is considerably small and therefore it is still a good option for prediction and should not be underestimated

A Comparison between the Online Prognostic Tool PREDICT and myBeST for Women with Breast Cancer in Malaysia

The PREDICT breast cancer is a well-known online calculator to estimate survival probability. We developed a new prognostic model, myBeST, due to the PREDICT tool’s limitations when applied to our patients. This study aims to compare the performance of the two models for women with breast cancer in Malaysia. A total of 532 stage I to III patient records who underwent surgical treatment were analysed. They were diagnosed between 2012 and 2016 in seven centres. We obtained baseline predictors and survival outcomes by reviewing patients’ medical records. We compare PREDICT and myBeST tools’ discriminant performance using receiver-operating characteristic (ROC) analysis. The five-year observed survival was 80.3% (95% CI: 77.0, 83.7). For this cohort, the median five-year survival probabilities estimated by PREDICT and myBeST were 85.8% and 82.6%, respectively. The area under the ROC curve for five-year survival by myBeST was 0.78 (95% CI: 0.73, 0.82) and for PREDICT was 0.75 (95% CI: 0.70, 0.80). Both tools show good performance, with myBeST marginally outperforms PREDICT discriminant performance. Thus, the new prognostic model is perhaps more suitable for women with breast cancer in Malaysia