Hydrothermal Synthesis of Zinc Oxide Nanoparticles Using Different Chemical Reaction Stimulation Methods and Their Influence on Process Kinetics.

The aim of this work was to study the effect of the applied chemical reaction stimulation method on the morphology and structural properties of zinc oxide nanoparticles (ZnONPs). Various methods of chemical reaction induction were applied, including microwave, high potential, conventional resistance heater and autoclave-based methods. A novel, high potential-based ZnONPs synthesis method is herein proposed. Structural properties-phase purity, grain size-were examined with XRD methods, the specific surface area was determined using BET techniques and the morphology was examined using SEM. Based on the results, the microwave and autoclave syntheses allowed us to obtain the desired phase within a short period of time. The impulse-induced method is a promising alternative since it offers a non-equilibrium course of the synthesis process in an highly energy-efficient manner.

Fiber-Optic Multipoint Sensor System with Low Drift for the Long-Term Monitoring of High-Temperature Distributions in Chemical Reactors.

A low-drift fiber-optic sensor system, consisting of 24 regenerated fiber Bragg gratings (RFBG), equally distributed over a length of 2.3 m, is presented here. The sensor system can monitor spatially extended temperature profiles with a time resolution of 1 Hz at temperatures of up to 500 °C. The system is intended to be used in chemical reactors for both the control of the production ramp-up, where a fast time response is needed, as well as for production surveillance, where low sensor drifts over several years are required. The fiber-optic sensor system was installed in a pilot test reactor and was exposed to a constant temperature profile, with temperatures in the range of 150-500 °C for more than two years. During this period, the temperature profile was measured every three to five months and the fiber-optic temperature data were compared with data from a three-point thermocouple array and a calibrated single-point thermocouple. A very good agreement between all temperature measurements was found. The drift rates of the 24 RFBG sensor elements were determined by comparing the Bragg wavelengths at a precisely defined reference temperature near room temperature before and after the two-year deployment. They were found to be in the range of 0.0 K/a to 2.3 K/a, with an average value of 1.0 K/a. These low drift rates were achieved by a dedicated temperature treatment of the RFBGs during fabrication. Here, the demonstrated robustness, accuracy, and low drift characteristics show the potential of fiber-optic sensors for future industrial applications.

Extrusion-Based 3D Printing of Microfluidic Devices for Chemical and Biomedical Applications: A Topical Review.

One of the most widespread additive manufacturing (AM) technologies is fused deposition modelling (FDM), also known as fused filament fabrication (FFF) or extrusion-based AM. The main reasons for its success are low costs, very simple machine structure, and a wide variety of available materials. However, one of the main limitations of the process is its accuracy and finishing. In spite of this, FDM is finding more and more applications, including in the world of micro-components. In this world, one of the most interesting topics is represented by microfluidic reactors for chemical and biomedical applications. The present review focusses on this research topic from a process point of view, describing at first the platforms and materials and then deepening the most relevant applications.

Automatic feed phase identification in multivariate bioprocess profiles by sequential binary classification.

In this paper, we propose a new strategy for retrospective identification of feed phases from online sensor-data enriched feed profiles of an Escherichia Coli (E. coli) fed-batch fermentation process. In contrast to conventional (static), data-driven multi-class machine learning (ML), we exploit process knowledge in order to constrain our classification system yielding more parsimonious models compared to static ML approaches. In particular, we enforce unidirectionality on a set of binary, multivariate classifiers trained to discriminate between adjacent feed phases by linking the classifiers through a one-way switch. The switch is activated when the actual classifier output changes. As a consequence, the next binary classifier in the classifier chain is used for the discrimination between the next feed phase pair etc. We allow activation of the switch only after a predefined number of consecutive predictions of a transition event in order to prevent premature activation of the switch and undertake a sensitivity analysis regarding the optimal choice of the (time) lag parameter. From a complexity/parsimony perspective the benefit of our approach is three-fold: i) The multi-class learning task is broken down into binary subproblems which usually have simpler decision surfaces and tend to be less susceptible to the class-imbalance problem. ii) We exploit the fact that the process follows a rigid feed cycle structure (i.e. batch-feed-batch-feed) which allows us to focus on the subproblems involving phase transitions as they occur during the process while discarding off-transition classifiers and iii) only one binary classifier is active at the time which keeps effective model complexity low. We further use a combination of logistic regression and Lasso (i.e. regularized logistic regression, RLR) as a wrapper to extract the most relevant features for individual subproblems from the whole set of high-dimensional sensor data. We train different soft computing classifiers, including decision trees (DT), k-nearest neighbors (k-NN), support vector machines (SVM) and an own developed fuzzy classifier and compare our method with conventional multi-class ML. Our results show a remarkable out-performance of the here proposed method over static ML approaches in terms of accuracy and robustness. We achieved close to error free feed phase classification while reducing the misclassification rates in 17 out of 20 investigated test cases in the range between 39% and 98.2% depending on feature set and classifier architecture. Models trained on features based on selection by RLR significantly outperformed those trained on features suggested by experts and their predictive performance was considerably less affected by the choice of the lag parameter.

Magnetic Resonance Imaging and Velocity Mapping in Chemical Engineering Applications.

This review aims to illustrate the diversity of measurements that can be made using magnetic resonance techniques, which have the potential to provide insights into chemical engineering systems that cannot readily be achieved using any other method. Perhaps the most notable advantage in using magnetic resonance methods is that both chemistry and transport can be followed in three dimensions, in optically opaque systems, and without the need for tracers to be introduced into the system. Here we focus on hydrodynamics and, in particular, applications to rheology, pipe flow, and fixed-bed and gas-solid fluidized bed reactors. With increasing development of industrially relevant sample environments and undersampling data acquisition strategies that can reduce acquisition times to <1 s, magnetic resonance is finding increasing application in chemical engineering research.

Precise In Situ Modulation of Local Liquid Chemistry via Electron Irradiation in Nanoreactors Based on Graphene Liquid Cells.

A controlled electron-water radiolysis process is used to generate predictable concentrations of radical and ionic species in graphene liquid cells, allowing the concept of a nanoscale chemical reactor. A differential scanning technique is used to generate the desired time- and space-varying electron dose rate. Precise control of the local concentration of H2 , the dominant radiolysis species, is demonstrated experimentally at the nanometer scale.

Preparation and quantification of radioactive particles for tracking hydrodynamic behavior in multiphase reactors.

Radioactive particle tracking (RPT) has emerged as a promising and versatile technique that can provide rich information about a variety of multiphase flow systems. However, RPT is not an off-the-shelf technique, and thus, users must customize RPT for their applications. This paper presents a simple procedure for preparing radioactive tracer particles created via irradiation with neutrons from the TRIGA Mark II research reactor. The present study focuses on the performance evaluation of encapsulated gold and scandium particles for applications as individual radioactive tracer particles using qualitative and quantitative neutron activation analysis (NAA) and an X-ray microcomputed tomography (X-ray Micro-CT) scanner installed at the Malaysian Nuclear Agency.