No abstract

A novel strategy for sensitive detection of biomarkers using horseradish peroxidase (HRP)-functionalized silica nanoparticles as the label is presented. The enzyme-functionalized silica nanoparticles were fabricated by coimmobilization of HRP and alpha-fetoprotein antibody (anti-AFP, the secondary antibody, Ab2), a model protein, onto the surface of SiO(2) nanoparticles using gamma-glycidoxypropyltrimethoxysilane (GPMS) as the linkage. Through "sandwiched" immunoreaction, the enzyme-functionalized silica nanoparticle labels were brought close to the surface of gold substrates, as confirmed by the scanning electron microscopy (SEM) images. Enhanced detection sensitivity was achieved where the large surface area of SiO(2) nanoparticle carriers increased the amount of HRP bound per sandwiched immunoreaction. The electrochemical and chemiluminescence measurement showed 29.5- and 61-fold increases in detection signals, respectively, in comparison with the traditional sandwich immunoassay. The improved particle synthesis using a "seed-particle growth" route yielded particles of narrow size distribution, which allowed consistent loading of HRP and anti-AFP on each microsphere and ensured subsequent immunosensing possessed high sensitivity and reproducibility. This strategy was successfully demonstrated as a simple, cost-effective, specific, and potent method to detect AFP in practical samples.

The problem of a fair profit distribution for a multienterprise supply chain network is investigated in this paper. To implement this concept, we construct a multiproduct, multistage, and multiperiod production and distribution planning model to achieve multiple objectives such as maximizing the profit of each participant enterprise, the customer service level, and the safe inventory level and ensuring a fair profit distribution. A two-phase fuzzy decision-making method is proposed to attain a compromise solution among all participant companies of the supply chain. One numerical example is supplied, demonstrating that the proposed two-phase decision-making method can provide an improved compensatory solution for multiobjective optimization problems in a multienterprise supply chain network.

The enhanced sensitivity for detection of biomarkers based on CdTe quantum dot functionalized silica nanosphere labels can be achieved by an increase in CdTe QD loading per sandwiched immunoreaction.

An improved separation method is proposed for the separation of pyridine and water as compared to previous separation designs. By using n-propyl format or diisopropyl ether (DIPE) as solvent, a hybrid extraction−distillation system can be designed for this separation task. Because the main extraction column does not require steam, significant savings in the operating cost and also the total annual cost can be realized as compared to the previous designs based on heterogeneous azeotropic distillation. Considering the density differences in each stage of the extraction column and in the decanter, DIPE is a more practically favorable solvent for reaching equilibrium in each stage for liquid−liquid separation. Dynamic control of this hybrid extraction−distillation system is also investigated. It is found that a trade-off between optimal steady-state design and dynamic controllability needs to be made in order to properly reject feed flow rate and feed composition disturbances.

In this paper, a novel strategy for the synthesis of cost-effective flexible heat-exchange networks (HENs) that involves specified uncertainties in the source-stream temperatures and flow rates is presented. The problem is decomposed into three main iterative steps: (1) simultaneous HEN synthesis to attain a network configuration with a minimum total annual cost (TAC); (2) flexibility analysis to test whether the network obtained from the synthesis step is feasible in the full disturbance range; and (3) integer cuts to exclude disqualified network configurations, i.e., for those networks not passing the examination of the flexibility analysis, the integer cuts and/or some parameter points will be appended to narrow the search space used for further HEN synthesis. A few iterations of these three steps are required to secure the desirable results. In addition to the theoretical derivation, two examples are included to demonstrate the efficiency of the proposed strategy.

Water minimization in the process industry is becoming increasingly important as environmental legislation becomes increasingly stringent and the awareness of the impact of industrial activities on the environment increases. Much work has been done on water minimization in continuous processes as evidenced by the detailed reviews of Bagajewicz (2000) and Foo (2009). Although water minimization for batch processes (batch water network in short) has been ignored in the past, it is steadily gaining more attention in research. An overview of the developments and methodologies proposed for batch water network is presented. The methodologies for water minimization can roughly be divided into insight-based and mathematical techniques. The former always consists of a two-step approach (targeting and design) in synthesizing a batch water network that features the minimum freshwater and wastewater flows for a given production schedule. The mathematical techniques, on the other hand, may be categorized into two subsectors, that is, with and without scheduling consideration. In this review, various water minimization methodologies are discussed and comparisons are made among them. When necessary, they are illustrated through examples.

The three-mode (PID) controller is still widely used in chemical industries because it is robust and is easy to operate. Many tuning guidelines have been recommended for the PID controllers. These include the Ziegler-Nichols closed-loop cycling method (Zeigler and Nichols, 1942), the Cohen-Coon open-loop reaction curve method (Cohen and Coon, 1952), and many other model-based minimum error integral methods (Lopez et al., 1967). The Ziegler-Nichols continuous cycling procedure is usually criticized for requiring the controlled process to be forced to the level of marginal stability. On the other hand, the advantage of the Ziegler-Nichols tuning formulae is that it need not characterize the process by parametric models, the results of which are known to depend on testing conditions. Many algorithms have been presented to obtain the critical data (ultimate gain and period) under acceptable conditions (e.g., Krishnaswamy et al., 1987).Recently, Yuwana and Seborg (1982) proposed a simple online algorithm which used the closed-loop response and the Pade approximation of the dead-time element to evaluate the parameters of a first-order process model, then the critical data of the model are used for subsequent controller tuning by ZieglerNichols rules. Lee (1989) modified the identification algorithms by matching the dominate poles of the closed-loop model to the poles of observed process transfer function. This modification enables the method to be used for processes with large dead times such as were not comprehended in the original paper. However, the applicability of Lee's modification to underdamped processes has not been demonstrated.Instead of using a low-order parametric model in process characterization, this article proposes determining the process critical data directly from the closed-loop response during step set-point change under P control mode. The modified method is expected to have two advantages over the algorithms of Yuwana-Seborg and Lee:1) It can provide more robust process critical data under many different testing conditions.2) It is applicable to underdamped processes and processes with dominant dead times.Simulation examples are supplied to demonstrate the robustness and applicability of this identification method. The MethodConsider a single-input/single-output (SISO) feedback control system as shown in Figure 1. With P mode controller, it is assumed that the closed-loop system is underdamped if a feedback gain large enough is chosen. The typical response to step set-point change is shown in Figure 2. The response approximates that of an underdamped second-order system with some element of dead time, as in Eq. In these equations, A is the magnitude of input disturbance in set point, and H i s the overshoot. Note that the new steady state