Pickup velocity of nanoparticles

This paper represents the first systematic study of the pneumatic conveying of nanoparticles. The minimum pickup velocity, Upu, of six nanoparticle species of different materials (i.e., silicon dioxide (SiO2), aluminum oxide (Al2O3) and titanium dioxide (TiO2)) and surfaces (i.e., apolar and polar) were determined by the weight loss method. Specifically, the weight loss method involves measuring the mass loss from the particle sample at various superficial gas velocities (U), and the Upu is the U value at which mass loss is zero. Nanoparticles were picked up as agglomerates rather than individually. Results show that (a) due to relative lack of hydrogen bonding, apolar nanoparticles have higher mass loss values at the same velocities, mass loss curves with accentuated S-shaped profiles, and lower Upu values; (b) among the three species, SiO2, which has the lowest Hamaker coefficient, exhibited the greatest discrepancy between apolar and polar surfaces with respect to both mass loss curves and Upu values; (c) Umf,polar/Umf,apolar was between 1 – 3.5 times that of Upu,polar/Upu,apolar due to greater extents of hydrogen bonding associated with Umf ; (d) Upu values are at least an order-of-magnitude lower than that expected from the well-acknowledged Upu correlation (1) due to agglomeration; (e) although nanoparticles should be categorized as Zone III (1) (or Geldart Group C (2)), the nanoparticles, and primary and complex agglomerates agree more with the Zone I (or Geldart Group B) correlation (Figure 1, whereby Rep* and Ar are the particle Reynolds number and Archimedes number, respectively (1)). In view of the importance of surface polarity on the pneumatic conveying of nanoparticles, more studies are on-going to further understand such surface effects. Please click Additional Files below to see the full abstract

An investigation into the influence of particle properties and operating conditions on gas-solid flow phenomena

Horizontal pneumatic conveying and fluidization are two important gas-solid flow operations employed in wide-ranging industries from agro-processing to pharmaceutical and oil and gas. Despite being employed in industry for decades, they are still largely run on rules-of-thumb and experience rather than scientific principles. This research effort endeavors to contribute to their physical understanding by analyzing the effects of particle properties and varying operating conditions on these phenomena. This thesis is divided into three parts focusing on horizontal pneumatic conveying, fluidization and reactor modeling respectively. Part I deals with the minimum pickup velocity (Upu), defined as the minimum gas velocity required to initiate motion of a particle initially at rest, a very important parameter in horizontal pneumatic conveying. In this section, firstly, the effect of continuous particle size distribution (PSD) and particle shape on Upu is studied, where the involvement of inter-particle momentum transfer and particle rotation and lift is revealed. Secondly, the effect of particle diameter, density and shape on Upu is studied by investigating binary mixtures. Thirdly, the Upu of nanoparticles is reported for the first time and behavioral differences between polar and apolar nanoparticles are noted. Finally, the knowledge gap in Upu at the nano- and micro- scales is bridged by investigating particles from diameters 5 nm to 110,000 nm and the Three-zone Model developed for the micro-scale is modified to incorporate nanoparticles. Part II discusses the minimum fluidization velocity (Umf), and differential pressure signals and radial mass flux distribution in circulating fluidized beds (CFBs). Specific studies include the following: (i) a comparative analysis of the predictions of over a hundred empirical correlations to determine Umf with respect to Geldart Groups A, B and D, and bed voidage and particle sphericity; and (ii) investigating the effects of particle properties, namely, particle diameter and density, and varying operating conditions, namely, superficial gas velocity and overall mass flux on differential pressure signals (via Discrete Fourier Transform and Wavelet Decomposition) and the radial mass flux behavior of particles in a pilot-scale CFB riser. Part III attempts numerical modeling of a carbon-dioxide (CO2) methanation bubbling fluidized bed reactor. The effects of operating conditions like the temperature, pressure, and inlet feed ratio, and hydrodynamic parameters like the inlet feed rate, superficial gas velocity, particle diameter, particle density, particle sphericity and the bed voidage at minimum fluidization on methane production by the Sabatier reaction are studied through a heterogeneous bubbling fluidized bed reactor model. The physical understanding on horizontal pneumatic conveying and fluidization presented in this thesis is expected to contribute to the database and aid future researchers as well as designers of systems employing these operations.Doctor of Philosophy (SCBE

Evaluation of correlations for minimum fluidization velocity (Umf) in gas-solid fluidization

The minimum fluidization velocity (Umf), defined as the superficial gas velocity at which the drag force of the upward moving gas becomes equal to the weight of the particles in the bed, is one of the most important parameters associated with a fluidized bed system. Specifically, it is the point at which all the particles become suspended. Unsurprisingly, more than a hundred correlations have sprouted since 1950 to enable the prediction of the Umf value. However, discrepancies among the predictions are significant, which limits the utility of each correlation. Accordingly, this study attempts to provide a comprehensive comparison of the Umf values predicted by the correlations available, which are classified into four types depending on the form of the equation and applied to more popular Geldart Groups A, B and D particles. The following observations are highlighted: (i) discrepancies among Umf predictions are presumably attributed to the empirical data-fitting based on limited experimental datasets rather than physical understanding; (ii) correlations involving an empirical coefficient as an exponent exhibit greater discrepancies (up to 6 orders-of-magnitude) in Umf predictions than those without; (iii) predictions for Geldart Group A particles displayed greater discrepancies across categories, due to a lack of understanding of cohesive forces associated with Group A particles; (iv) correlations involving voidage (εmf) and sphericity (φ) exhibit more unphysical trends than those without, presumably due to a limited range of εmf and φ experimentally assessed, hence the inclusion of these two parameters increased the errors associated with these correlations. A mechanistically based correlation may be still intractable at this point, so recommendations are made for future studies on improving the prediction of Umf.NRF (Natl Research Foundation, S’pore

A Multi-Space Approach to Zero-Shot Object Detection

Object detection has been at the forefront for higher level vision tasks such as scene understanding and contextual reasoning. Therefore, solving object detection for a large number of visual categories is paramount. Zero-Shot Object Detection (ZSD) – where training data is not available for some of the target classes – provides semantic scalability to object detection and reduces dependence on large amount of annotations, thus enabling a large number of applications in real-life scenarios. In this paper, we propose a novel multi-space approach to solve ZSD where we combine predictions obtained in two different search spaces. We learn the projection of visual features of proposals to the semantic embedding space and class labels in the semantic embedding space to visual space. We predict similarity scores in the individual spaces and combine them. We present promising results on two datasets, PASCAL VOC and MS COCO. We further discuss the problem of hubness and show that our approach alleviates hubness with a performance superior to previously proposed methods

Pre-deposited dynamic membrane filtration - a review

A dynamic membrane (DM) is a layer of particles deposited via permeation drag onto a conventional membrane, such that the deposited particles act as a secondary membrane that minimizes fouling of the primary membrane to lower transmembrane pressures (TMP) and enable higher permeate fluxes. Since the first DM was created in 1966 at the Oak Ridge National Laboratory, numerous studies have reported synthesis of DMs using various materials and explored their abilities to perform reverse osmosis (RO), nanofiltration (NF), ultrafiltration (UF) and microfiltration (MF). DMs are classified into two categories, namely, (i) self-formed, whereby the feed constituents form the DM; and (ii) pre-deposited, whereby the DM is formed by a layer of particles other than the feed prior to introduction of the feed. This paper endeavors to present a comprehensive review of the state-of-the-art on the latter. Key materials used as DMs, their formation and various factors influencing it, regeneration of DMs and modifications to DM systems for performance enhancement are discussed. The role of DMs in preventing fouling in the primary membrane (PM) is explained. The applications of DMs in four major areas, namely, salt and organic solute rejection, treatment of industrial effluents, treatment of water and wastewater, and oily-wastewater treatment are reviewed. Furthermore, technical and economic advantages of DMs over conventional processes are considered, and challenges in current DM research are discussed. Finally, directions for future research are suggested.National Research Foundation (NRF)Public Utilities Board (PUB)This research grant was supported by the Singapore National Research Foundation under its Environment and Water Research Program and administered by PUB, Singapore’s National Water Agency (grant number: 1601-CRPW-T20). The Singapore Membrane Technology Center, Nanyang Environment and Water Research Institute, Nanyang Technological University is supported by the Economic Development Board of Singapore

A Multi-Space Approach to Zero-Shot Object Detection

Object detection has been at the forefront for higher level vision tasks such as scene understanding and contextual reasoning. Therefore, solving object detection for a large number of visual categories is paramount. Zero-Shot Object Detection (ZSD) - where training data is not available for some of the target classes - provides semantic scalability to object detection and reduces dependence on large amount of annotations, thus enabling a large number of applications in real-life scenarios. In this paper, we propose a novel multi-space approach to solve ZSD where we combine predictions obtained in two different search spaces. We learn the projection of visual features of proposals to the semantic embedding space and class labels in the semantic embedding space to visual space. We predict similarity scores in the individual spaces and combine them. We present promising results on two datasets, PASCAL VOC and MS COCO. We further discuss the problem of hubness and show that our approach alleviates hubness with a performance superior to previously proposed methods

Organic matter removal from a membrane bioreactor effluent for reverse osmosis fouling mitigation by microgranular adsorptive filtration system

In this study, a prototype microgranular adsorptive filtration (μGAF) system was constructed employing a 7-bore ceramic membrane as the primary membrane and either heated aluminum oxide particles (HAOPs) or powdered activated carbon (PAC) as the pre-deposited dynamic membrane (DM). The system was used to pre-treat membrane bioreactor (MBR) effluent from a full-scale MBR-reverse osmosis (RO) water reclamation plant. The downstream RO performance and membrane fouling potential of the treated effluent were then assessed. The results indicated that: (i) although PAC removed more overall EfOM than HAOPs did, HAOPs were more effective in removing biopolymers such as polysaccharides and proteins, (ii) HAOPs virtually eliminated fouling of the primary ceramic membrane, whereas considerable fouling (much of it irreversible) occurred when the feed was pretreated with PAC, (iii) HAOPs removed more than 90% of the phosphorus and fluoride from the feed, but PAC removed negligible amounts of these contaminants, and (iv) HAOPs-treated effluent resulted in only a 43% decline in RO permeate water flux over 5 d of continuous filtration, as opposed to 62% flux decline for untreated or PAC-treated effluent. This study thus demonstrates the effectiveness of the HAOPs-based μGAF process as a pre-treatment for improving downstream RO recovery.Economic Development Board (EDB)National Research Foundation (NRF)Submitted/Accepted versionThis research grant was supported by the Singapore National Research Foundation under its Environment and Water Research Program and administered by PUB, Singapore’s National Water Agency (grant number: 1601-CRPW-T20). The Singapore Membrane Technology Center, Nanyang Environment and Water Research Institute, Nanyang Technological University is supported by the Economic Development Board of Singapore

A review of membrane fouling by proteins in ultrafiltration and microfiltration

Fouling of ultrafiltration (UF) and microfiltration (MF) membranes by proteins is a major challenge in the bioprocessing and dairy industries, as well as in surface and wastewater treatment applications. This review attempts at presenting a comprehensive state-of-the-art understanding on protein fouling of membranes. Effects of operating conditions, along with properties of proteins and membranes, are discussed. Various tools and techniques used to characterize and monitor fouling are described. Different mitigation techniques and cleaning methods used are also presented. Two main factors have been identified as playing important roles in governing protein fouling, namely, ratio of protein size to membrane pore size and interfacial interactions (i.e., protein-protein and protein-membrane). Some directions for future research are suggested: (1) explore a wider range of proteins and their mixtures with respect to their fouling tendencies; and (2) create a comprehensive dataset that can be used to develop machine-learning models to enhance both predictive capabilities and mechanistic understanding.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)This study was supported by A*STAR (Singapore) Advanced Manufacturing and Engineering (AME) under its Pharma Innovation Programme Singapore (PIPS) (A20B3a0070); A*STAR (Singapore) Advanced Manufacturing and Engineering (AME) under its Individual Research Grant (IRG) program (A2083c0049); the Singapore Ministry of Education Academic Research Tier 1 Grant (2019-T1-002-065; RG100/ 19) and the Singapore Ministry of Education Academic Research Tier 2 Grant (MOE-MOET2EP10120-0001)

Synthesis of 2D perovskite crystals via progressive transformation of quantum well thickness

International audienceTwo-dimensional (2D) multilayered halide perovskites have emerged as a platform for understanding organic–inorganic interactions, tuning quantum confinement effects and realizing efficient and durable optoelectronic devices. However, reproducibly synthesizing 2D perovskite crystals with a perovskite-layer thickness (quantum well thickness, n-value) >2 using existing crystal growth methods is challenging. Here we demonstrate a synthetic method, termed kinetically controlled space confinement, for the growth of phase-pure Ruddlesden–Popper and Dion–Jacobson 2D perovskites. Phase-pure growth is achieved by progressively increasing the temperature (for a fixed time) or the crystallization time (at a fixed temperature), which allows for control of the crystallization kinetics. In s it u p ho toluminescence spectroscopy and imaging suggest that the controlled increase in n-value (from lower to higher values of n = 4, 5 and 6) occurs due to intercalation of excess precursor ions. Based on 250 experimental data sets, phase diagrams for both Ruddlesden–Popper and Dion–Jacobson perovskites have been constructed to predict the growth of 2D phases with specific n-values, facilitating the production of 2D perovskite crystals with desired layer thickness