Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.     

A Methodology for Estimating Kinetic Parameters and Reactivity Ratios of Multi‐site Type Catalysts Using Polymerization,Fractionation, and Spectroscopic Techniques

This work describes a polymer reaction engineering framework for understanding how catalyst kinetic parameters affect the microstructure of polyolefins made with single‐ or multi‐site catalysts. Moreover, a methodology for deconvolution and kinetic parameters estimation is presented to estimate the reactivity ratios of multi‐site catalysts based on the combination of polymerization, fractionation, and spectroscopic techniques, namely, gel permeation chromatography‐IR and carbon‐13 nuclear magnetic resonance spectroscopy. The methodology capabilities are then demonstrated and validated using a case study simulated via a Monte Carlo model including random noise in order to better represent experimental result uncertainties. The methodology can reverse engineer experimental results and estimate all relevant reaction performance parameters.     

Molecular structure and properties of click hydrogels with controlled dangling end defect

In this study, controlled amount of dangling ends is introduced to the two series of poly(ethylene glycol)‐based hydrogel networks with three and four crosslinking functionality by using click chemistry. The structure of the gels with regulated defect percentage is confirmed by comparing the results of low‐field NMR characterization and Monte Carlo simulation. The mechanical properties of these gels were characterized by tensile stress–strain behaviors of the gels, and the results are analyzed by Gent model and Mooney–Rivlin model. The shear modulus of the swollen gels is found to be dependent on the functionality of the network, and decreases with the defect percentage. Furthermore, the value of shear modulus well obeys the Phantom model for all the gels with varied percentage of the defects. The maximum extension ratio, obtained from the fitting of Gent model, is also found to be dependent on the functionality of the network, and does not change with the defect percentage, except at very high defect percentage. The value of the maximum extension ratio is between that predicted from Phantom model and the Affine model. This indicates that at the large deformation, the fluctuation of the crosslinking points is suppressed for some extend but still exists. Polymer volume fractions at various defect percentages obtained from prediction of Flory–Rehner model are found to be in well agreement with the swelling experiment. All these results indicate that click chemistry is a powerful method to regulate the network structure and mechanical properties of the gels. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016 , 54, 1227–1236     

建模思想在化学原理教学中的运用

通过竞争反应与海水晒盐2个案例,阐述了在化学原理教学过程中运用建模思想,可以使复杂的过程变得简洁、抽象,从而明晰现象背后的原理、规律。     

Rapid Diagnosis of Type II Diabetes Using Fourier Transform Mid-Infrared Attenuated Total Reflection Spectroscopy Combined with Support Vector Machine

Type II diabetes was diagnosed by Fourier transform mid-infrared (FTMIR) attenuated total reflection (ATR) spectroscopy in combination with support vector machine (SVM). Spectra of serum samples from 65 patients with clinical confirmed type II diabetes mellitus and 55 healthy volunteers were acquired using ATR-FTMIR and were first pretreated by three pretreatments (Savitzky–Golay smoothing, multiple scattering correction, and wavelet transforms algorithms) to reduce the interfering information before establishing the SVM models. The parameters of SVM (penalty factor C and kernel function parameter gamma) were optimized to improve the generalization abilities of the models. A grid search method (GS), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were used to find out the optimal parameter values. The results showed that the maximum accuracies were 95.74, 97.87, and 89.36% for the optimized GS, GA, and PSO algorithms. The maximum sensitivities were 96, 100, and 92, and the maximum specificity were 95.45, 95.45, and 86.36%, respectively. The results indicated that the accuracy of type II diabetes was improved using the GS, GA, and PSO algorithms for optimizing the SVM parameters. The GA was found to be slightly better than the GS and PSO. The results of the experiment confirmed that the combination of the ATR-FTMIR spectroscopy and SVM was able to rapidly and accurately diagnose type II diabetes without reagents.     

The Effect of Residence Time Distribution on the Slurry‐Phase Catalytic Ethylene Polymerization: An Experimental and Computational Study

This work is focused on the development and validation of a model accounting for the impact of the reactor residence time distribution in well‐stirred slurry‐phase catalytic polymerization of ethylene. Particle growth and morphology are described through the Multigrain model, adopting a two‐site model for the catalyst and a conventional kinetic scheme. Particle size distribution and polymer properties (average molecular weights and polydispersity) are computed as a function of particle size through a segregated model, assuming that neither breakage nor aggregation occur. Reactors are modeled by means of fundamental mass conservation equations. The model is applied to a system constituted by a series of two ideal continuous stirred tank reactors, where the synthesis of polyethylene with bimodal molecular weight distribution is performed, employing the initial catalyst size distribution as the only adjustable parameter. The model provides insights at the single particle scale for each specific size, thus highlighting the inhomogeneity which arises from the synergic effects of chemical kinetics and residence time distributions in both reactors. The satisfactory agreement between model results and experimental data, in terms of particle size distribution and average molecular weights, confirmed the suitability of the model and underlying assumptions.     

Classification of auxin plant hormones by interaction property similarity indices

Although auxins were the first type of plant hormone to be identified, little is known about the molecular mechanism of this important class of plant hormones. We present a classification of a set of about 50 compounds with measured auxin activities, according to their interaction properties. Four classes of compounds were defined: strongly active, weakly active with weak antiauxin behaviour, inactive and inhibitory. All compounds were modeled in two low-energy conformations, P and T, so as to obtain the best match to the planar and tilted conformations, respectively, of indole 3-acetic acid. Each set of conformers was superimposed separately using several different alignment schemes. Molecular interaction energy fields were computed for each molecule with five different chemical probes and then compared by computing similarity indices. Similarity analysis showed that the classes are on average distinguishable, with better differentiation achieved for the T conformers than the P conformers. This indicates that the T conformation might be the active one. Further, a screening was developed which could distinguish compounds with auxin activity from inactive compounds and most antiauxins using the T conformers. The classifications rationalize ambiguities in activity data found in the literature and should be of value in predicting the activities of new plant growth substances and herbicides.     

Fundamental Molecular Weight Distribution of RAFT Polymers

The molecular weight distribution formed in an ideal reversible addition‐fragmentation chain transfer (RAFT)‐mediated radical polymerization is considered theoretically. In this polymerization, the addition to the RAFT agent is reversible, and the active period on the same chain could be repeated, via the two‐armed intermediate, with probability 1/2. This possible repetition is accounted for by introducing a new concept, the overall active/dormant periods. With this method, the apparent functional form of the molecular weight distribution (MWD) reduces to that proposed for the ideal living radical polymers (Tobita, Macromol. Theory Simul. 2006 , 15, 12). The repetition results in a broader MWD than without the repetition. The formulae for the average molecular weights formed in batch and a continuous stirred tank reactor are also presented.