Electrostatic forces induce poly(vinyl alcohol)-protected copper nanoparticles to form copper/poly(vinyl alcohol) nanocables via electrospinning

Copper/poly(vinyl alcohol) (PVA) nanocables have been successfully obtained by electrospinning a PVA-protected copper nanoparticle solution. The molar ratio of copper ions to PVA (in terms of VA repeating units) plays an important role in the formation of copper/PVA nanocables. The average diameter of the copper cores and PVA shells is about 100 and 400 nm, respectively. The structures of the copper/PVA nanocables are characterized by transmission electron microscopy (TEM) and their formation is confirmed by scanning electron microscopy (SEM).

Performance analysis of two advanced controllers for polystyrene polymerization in batch reactor

The performance of two advanced model based non-linear controllers is analyzed for the optimal setpoint tracking of free radical polymerization of styrene in batch reactors. Artificial neural network-based model predictive controller (NN-MPC) and generic model controller (GMC) are both applied for controlling the system. The recently developed hybrid model [1] as well as available literature models are utilized in the control study. The optimal minimum temperature profiles are determined based on Hamiltonian maximum principle. Different types of disturbances are artificially generated to examine the stability and robustness of the controllers. The experimental studies reveal that the performance of NN-MPC is superior over that of GMC.

General synthesis of two-dimensional patterned conducting polymer-nanobowl sheet via chemical polymerization

A general method for the generation of two-dimensional (2D) ordered, large-area, and liftable conducting polymer-nanobowl sheet has been demonstrated via chemical polymerization for the first time. The sheet is made using the monolayer self-assembled from polystyrene (PS) spheres at the aqueous/air interface as template, followed by depositing conducting polymer on the part of PS monolayer submerging in the aqueous phase via chemical polymerization, and core extraction. During the process of polymerization, no substrate is required, which caused the as-prepared patterned conducting polymer sheet can be easily lifted-off and deposited, in full size, on any flat substrate. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Fourier transform infrared (FTIR) spectrum were used to characterize the products

Simultaneous polymerization and crosslinking for the synthesis of molecular-level graphene oxide-polyacryl amide-CeOx composites

Synthesis of molecular-level multiple-component composites are particularly challenging due to the lack of direct bonding among different components. In this study, molecular-level graphene oxide (GO)-polyacryl amide (PAM)-CeOx composites were successfully synthesized, using the simultaneous polymerization and crosslinking strategy. Attenuated total reflection Fourier transform infrared (ATR-FTIR) and nuclear magnetic resonance (NMR) techniques confirmed that polyacryl amide (PAM) chains were successfully grafted onto the surface of GO. X-ray photoelectron spectroscopic (XPS) and X-ray diffraction (XRD) analyses further revealed the characteristic signals of cerium elements and CeO2 phase respectively. Scanning electron microscopy (SEM) showed that the surface morphology of the GO-PAM-CeOx composites was substantially thicker and rougher than those of the original GO. Further exploration of the reaction mechanism clearly demonstrate the existence of strong chelating interaction among PAM chains and Ce(IV) ions. In particular, the polymerization of acryl amide monomers and the crosslinking reaction between PAM and Ce(IV) or Ce(III) ions were realized simultaneously, leading to the final formation of molecular-level GO-PAM-CeOx composites. Moreover, the as-synthesized GO-PAM-CeOx composites were capable of effectively decomposing Rhodamine B under simulated sunlight, making it a potential candidate as a new photo catalyst. To sum up, this report demonstrates the potential utility of simultaneous polymerization and crosslinking method for the synthesis of other multiple-component composites at molecular-level.

Prediction interval-based neural network modelling of polystyrene polymerization reactor - a new perspective of data-based modelling

In this paper, prediction interval (PI)-based modelling techniques are introduced and applied to capture the nonlinear dynamics of a polystyrene batch reactor system. Traditional NN models are developed using experimental datasets with and without disturbances. Simulation results indicate that traditional NNs cannot properly handle disturbances in reactor data and demonstrate a poor forecasting performance, with an average MAPE of 22% in the presence of disturbances. The lower upper bound estimation (LUBE) method is applied for the construction of PIs to quantify uncertainties associated with forecasts. The simulated annealing optimization technique is employed to adjust NN parameters for minimization of an innovative PI-based cost function. The simulation results reveal that the LUBE method generates quality PIs without requiring prohibitive computations. As both calibration and sharpness of PIs are practically and theoretically satisfactory, the constructed PIs can be used as part of the decision-making and control process of polymerization reactors. © 2014 The Institution of Chemical Engineers.

Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

Performance analysis of three advanced controllers for polymerization batch reactor: an experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC. © 2013 The Institution of Chemical Engineers.

RAFT controlled synthesis of graphene/polymer hydrogel with enhanced mechanical property for pH-controlled drug release

A pH-sensitive, mechanically strong and thermally stable graphene/poly (acrylic acid) (graphene/PAA) hydrogel was prepared via reversible addition fragmentation transfer (RAFT) polymerizations in the presence of a cross-linking agent. The RAFT agent was covalently coupled onto graphene basal planes via an esterification reaction, with benzoic acid functionalities pre-attached on graphene with its aryl diazonium salt precursor. AFM and SEM analysis revealed the successful preparation of single layered graphene sheets and graphene/polymer hydrogels with pH controlled porous structures. Attenuated total reflection infrared (ATR-IR) and thermogravimetric analyzer (TGA) verified the successful stepwise preparation of graphene/PAA hydrogel. This graphene/PAA hydrogel was pH-sensitive and more mechanically elastic than the PAA hydrogel prepared without graphene. The pH sensitivity of the hydrogel was further utilized for controlled drug release. Doxorubicin was chosen as a model drug and loaded into the hydrogels. The drug loading and release experiment indicated that this hydrogel can be used to efficiently control drug release in the intestine environment (pH = 7.4), better than release in a more acidic environment.© 2013 Elsevier Ltd. All rights reserved.

Amine enrichment of thin-film composite membranes via low pressure plasma polymerization for antimicrobial adhesion

Thin-film composite membranes, primarily based on poly(amide) (PA) semipermeable materials, are nowadays the dominant technology used in pressure driven water desalination systems. Despite offering superior water permeation and salt selectivity, their surface properties, such as their charge and roughness, cannot be extensively tuned due to the intrinsic fabrication process of the membranes by interfacial polymerization. The alteration of these properties would lead to a better control of the materials surface zeta potential, which is critical to finely tune selectivity and enhance the membrane materials stability when exposed to complex industrial waste streams. Low pressure plasma was employed to introduce amine functionalities onto the PA surface of commercially available thin-film composite (TFC) membranes. Morphological changes after plasma polymerization were analyzed by SEM and AFM, and average surface roughness decreased by 29%. Amine enrichment provided isoelectric point changes from pH 3.7 to 5.2 for 5 to 15 min of plasma polymerization time. Synchrotron FTIR mappings of the amine-modified surface indicated the addition of a discrete 60 nm film to the PA layer. Furthermore, metal affinity was confirmed by the enhanced binding of silver to the modified surface, supported by an increased antimicrobial functionality with demonstrable elimination of E. coli growth. Essential salt rejection was shown minimally compromised for faster polymerization processes. Plasma polymerization is therefore a viable route to producing functional amine enriched thin-film composite PA membrane surfaces.