Richard K. K. Yuen

Synthesis, structure–property relationships of polyphosphoramides with high char residues

A series of polyphosphoramides with high char residues were successfully synthesized using solution polycondensation and well characterized. The thermal properties and flammability were investigated by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and microscale combustion calorimeter (MCC). The evolved gases during decomposition were also analyzed using Fourier transform infrared coupled with the thermogravimetric analyzer (TG-IR) technique. The char residues of the polyphosphoramides were investigated by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) and Raman spectroscopy. The results showed that polyphosphoramides with sufficient molecular weights could be obtained, having high glass transition temperatures (Tgs), high thermal stabilities, as well as lower flammability depending on the diamines incorporated. The char residues showed much difference among each other. Interestingly, one sample containing an ether group in the backbone exhibited a honeycomb-like char morphology, associated with a high degree of graphitization.

Novel organic–inorganic flame retardants containing exfoliated graphene: preparation and their performance on the flame retardancy of epoxy resins

In this work, a simple and efficient approach for the preparation of organic–inorganic hybrids flame retardants (FRs-rGO), aiming at improving the flame retardant efficiency was presented. The reduced graphite oxide (rGO) was incorporated into the flame retardants matrix by in situ sol–gel process, resulting in the formation of organic–inorganic hybrids flame retardants containing exfoliated rGO. The TEM results of FRs-rGO hybrids revealed that the rGO was previously exfoliated in the phosphorus and silicon containing FRs. Subsequently, the flame retardant (FRs-rGO) was incorporated into epoxy resins (EP). The previous exfoliation of rGO in the FRs allows rGO to be intimately mixed with epoxy resins, which can be confirmed by the TEM results of FRs-rGO/EP nanocomposites. With the incorporation of 5 wt% of FRs-rGO into EP, satisfied flame retardant grade (V0) and the LOI as high as 29.5 were obtained. The char residues of the FRs-rGO/EP nanocomposites were significantly increased in air as well as nitrogen atmosphere. Moreover, the peak heat release rate (pHRR) value of FRs-rGO/EP was significantly reduced by 35%, and the glass transition temperature (Tg) of FRs-rGO/EP nanocomposites shifted to higher temperature, compared to those of neat EP. The flame retardancy strategy of FRs-rGO combines condensed phase and gas phase flame retardant strategies such as the nanocomposites technique, phosphorus–silicon synergism systems in the condensed phase and DOPO flame retardant systems in the gas phase. Moreover, the flame retardants containing exfoliated graphene (FRs-rGO) provided a novel method to prepare organic–inorganic hybrids flame retardants and the as-prepared flame retardants exhibited high flame retardant efficiency.

A hybrid neural network model for noisy data regression

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Influence of g-C3N4 nanosheets on thermal stability and mechanical properties of biopolymer electrolyte nanocomposite films: a novel investigation.

A series of sodium alginate (SA) nanocomposite films with different loading levels of graphitic-like carbon nitride (g-C3N4) were fabricated via the casting technique. The structure and morphology of nanocomposite films were investigated by X-ray powder diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and transmission electron microscopy. Thermogravimetric analysis results suggested that thermal stability of all the nanocomposite films was enhanced significantly, including initial thermal degradation temperature increased by 29.1 °C and half thermal degradation temperature improved by 118.2 °C. Mechanical properties characterized by tensile testing and dynamic mechanical analysis measurements were also reinforced remarkably. With addition of 6.0 wt % g-C3N4, the tensile strength of SA nanocomposite films was dramatically enhanced by 103%, while the Youngs modulus remarkably increased from 60 to 3540 MPa. Moreover, the storage modulus significantly improved by 34.5% was observed at loadings as low as 2.0 wt %. These enhancements were further investigated by means of differential scanning calorimetry and real time Fourier transform infrared spectra. A new perspective of balance was proposed to explain the improvement of those properties for the first time. At lower than 1.0 wt % loading, most of the g-C3N4 nanosheets were discrete in the SA matrix, resulting in improved thermal stability and mechanical properties; above 1.0 wt % and below 6.0 wt % content, the aggregation was present in SA host coupled with insufficient hydrogen bondings limiting the barrier for heat and leading to the earlier degradation and poor dispersion; at 6.0 wt % addition, the favorable balance was established with enhanced thermal and mechanical performances. However, the balance point of 2.0 wt % from dynamic mechanical analysis was due to combination of temperature and agglomeration. The work may contribute to a potential research approach for other nanocomposites.

Ternary graphene–CoFe2O4/CdS nanohybrids: preparation and application as recyclable photocatalysts

Graphene (Gr)-based binary Gr–CoFe2O4 and Gr–CdS or ternary Gr–CoFe2O4/CdS nanohybrids were prepared via a facile solvothermal strategy. It was encouraging to find that the ternary Gr–CoFe2O4/CdS nanohybrids exhibited the highest photocatalytic degradation ability (80%) among all the photocatalysts. The significant enhancement in photodegradation under 40 W daylight lamp irradiation was attributed to graphene acting as a “bridge”, where electrons generated from CoFe2O4 were transferred to CdS by graphene and finally led to separation of electrons and holes. Interestingly, neat CoFe2O4 resulted in increasing concentration of methylene blue (MB) as the irradiation time increased. The phenomenon was ascribed to adsorption of MB molecules on CoFe2O4 in the dark and desorption from the photocatalyst during irradiation, confirmed by our ingenious experiment. Digital photos of the Gr–CoFe2O4/CdS hybrids in an external magnetic field indicated that the ternary photocatalyst could be easily separated from aqueous solution. The recycle measurements of the photocatalyst revealed that the ternary nanohybrids exhibited acceptable photocatalytic stability due to unstable decoration. This work would provide a new insight into the construction of visible light-responsive and magnetic separable photocatalysts with high performances.

Novel CuCo2O4/graphitic carbon nitride nanohybrids: Highly effective catalysts for reducing CO generation and fire hazards of thermoplastic polyurethane nanocomposites

Novel spinel copper cobaltate (CuCo2O4)/graphitic carbon nitride (g-C3N4) (named C-CuCo2O4) nanohybrids with different weight ratios of g-C3N4 to CuCo2O4 were successfully synthesized via a facile hydrothermal method. Then the nanohybrids were added into the thermoplastic polyurethane (TPU) matrix to prepare TPU nanocomposites using a master batch-melt compounding approach. Morphological analysis indicated that CuCo2O4 nanoparticles were uniformly distributed on g-C3N4 nanosheets. Thermal analysis revealed that C-CuCo2O4-7 (proportion of g-C3N4 to CuCo2O4 of 93/7) was an optimal nanohybrid for the properties improvement of TPU. Incorporation of C-CuCo2O4-7 into TPU led to significant improvements in the onset decomposition temperature, temperature at maximal mass loss rate and char yields. The heat release rate and total heat release of TPU/C-CuCo2O4-7 decreased by 37% and 31.3%, respectively, compared with those of pure TPU. Furthermore, the amounts of pyrolysis gaseous products, including combustible volatiles and carbon monoxide (CO), were remarkably reduced, whereas, non-flammable gas (carbon dioxide) increased. Excellent dispersion of C-CuCo2O4-7 in TPU host was achieved, due to the synergistic effect between g-C3N4 and CuCo2O4. Enhancements in the thermal stability and flame retardancy were attributed to the explanations that g-C3N4 nanosheets showed the physical barrier effect and catalytic nitrogen monoxide (NO) decomposition, and that CuCo2O4 catalyzes the reaction of CO with NO and increased char residues.

A novel artificial neural network fire model for prediction of thermal interface location in single compartment fire

Thermal interface is the boundary between the hot and cold gases layers in a compartment fire. The height of the interface depends predominantly on the mass of air entrained into the fire plume. However, the analytical determination of the air mass flow rate is complicated since it is highly nonlinear in nature. Currently, computer models including zone models and field models can be applied to predict fire phenomena effectively. In the zone model computation, the compartment on fire is commonly divided into two layers to which conservation equations are applied to evaluate the fire behaviour. However, the locations of the fire bed and the openings are ignored in the computation. Computational fluid dynamics techniques may be employed, but a major shortcoming is the requirement for extensive computational resources and lengthy computational time. A unique, new and novel artificial neural network (ANN) model, denoted as GRNNFA, is developed for predicting parameters in compartment fires and is an extremely fast alternative approach. The GRNNFA model is capable of capturing the nonlinear system behaviour by training the network using relevant historical data. Since noise is usually embedded in most of the collected fire data, traditional ANN models (e.g. feed-forward multi-layer-perceptron, general regression neural network, radial basis function, etc.) are unable to separate the embedded noise from the genuine characteristics of the system during the course of network training. The GRNNFA has been developed particularly for processing noisy fire data. The model was applied to predict the location of the thermal interface in a single compartment fire and compared with the experiments conducted by Steckler et al. (Flow induced by fire in a compartment, NBSIR 82-2520, National Bureau of Standards, Washington, DC, 1982). The results show that the GRNNFA fire model can predict the location of the thermal interface with up to 94.5% accuracy and minimum computational times and resources. The trained GRNNFA model was also applied to rapidly determine the height of the thermal interface with different locations of fire on the compartment floor and different widths of the opening against field model predictions. Among the five test cases, four of them were predicted well within the minimum error range of the experiment results. It also demonstrated that the prediction accuracy is related to the amount of knowledge provided for network training.

Silicon nanoparticle decorated graphene composites: preparation and their reinforcement on the fire safety and mechanical properties of polyurea

Reduced graphene oxide (rGO) was decorated with organic/inorganic nanoparticles through an in situ sol–gel process with various thicknesses. The presence of organic/inorganic nanoparticles made the rGO lipophilic, as evidenced by the good dispersion of the nanoparticles–rGO in dimethyl formamide solvent (DMF). The thickness of the nanoparticles–rGO could be varied by adjusting the amount of the silicane additive, as evidenced by the AFM results. The nanoparticles–rGO was then incorporated into polyurea in different ratios via in situ polymerization and the property enhancement of the nanocomposites was investigated. The TEM morphological study showed that, due to the good interfacial interaction between the nanoparticles–rGO and polyurea, nanoparticles–rGO was dispersed well in the polyurea matrix. Compared with the rGO, the nanoparticles could significantly improve the thermal stability and thermal conductivity of polyurea, implying that the good dispersion of rGO and the functional groups on the surface of rGO had a significant effect on the thermal stability and thermal conductivity of polyurea. The peak heat release rate (pHRR) of nanoparticles–rGO/polyurea nanocomposites was significantly reduced, which indicated that the combustible gas releasing rate of polyurea was reduced. Moreover, the storage modulus and tensile strength of the nanocomposites with 0.2 wt% have been enhanced by about 60% and 110% in comparison with those of neat polyurea, respectively. This simple and effective approach, decorating the rGO with organic/inorganic nanoparticles, is believed to offer possibilities for broadening the graphene applications in the polymer materials and make it possible to decorate the graphene with other functional groups and vary the aspect ratio of decorated graphene according to its application.

On numerical comparison of enclosure fire in a multi-compartment building

This paper reports a validation study of a CFD simulation for an enclosure fire in a single level multi-room building. Model predictions are compared against measured data of Luo and Beck (Fire Safety J 23 (1994) 413). The CFD-based fire model focuses on the use of laminar flamelet approach to account for the combustion of fire. Global radiation in the multi-compartment building is evaluated though the discrete ordinates method (Combust. Sci. Technol. 59 (1988) 321). Soot model proposed by Syed et al. (Proceedings of the 23rd Symposium on Combustion, The Combustion Institute, 1990. p. 1533) that accounts for the essential physical processes of nucleation, coagulation, surface growth and oxidation is utilised to predict the soot formation and burnout. The presence of soot augmenting the global radiative heat exchange was considered. Overall, our results are in good agreement with the experimental data of Luo and Becks and also consistent with their numerical results.

On modelling combustion, radiation and soot processes in compartment fires

Abstract A Reynolds-Averaging-Navier–Stokes Computational-Fluid-Dynamics-based fire model is developed to solve a turbulent buoyant fire in a single-, two- and multi-compartment structure. The model is evaluated as part of a complete prediction procedure involving the modelling of the simultaneously occurring flow, convection, combustion, soot generation and burnout and radiation phenomena. Computational results are compared against available experimental data. Proper handling of the fire chemistry through combustion models such as eddy break-up and laminar flamelet is important to modelling compartment fires. Thermal radiation plays a significant role too. Soot radiation has shown to significantly improve the accuracy of the model predictions.