Thomas Huddle

Understanding bottom-up continuous hydrothermal synthesis of nanoparticles using empirical measurement and computational simulation

Continuous hydrothermal synthesis was highlighted in a recent review as an enabling technology for the production of nanoparticles. In recent years, it has been shown to be a suitable reaction medium for the synthesis of a wide range of nanomaterials. Many single and complex nanomaterials such as metals, metal oxides, doped oxides, carbonates, sulfides, hydroxides, phosphates, and metal organic frameworks can be formed using continuous hydrothermal synthesis techniques. This work presents a methodology to characterize continuous hydrothermal flow systems both experimentally and numerically, and to determine the scalability of a counter current supercritical water reactor for the large scale production (>1,000 T·year–1) of nanomaterials. Experiments were performed using a purpose-built continuous flow rig, featuring an injection loop on a metal salt feed line, which allowed the injection of a chromophoric tracer. At the system outlet, the tracer was detected using UV/Vis absorption, which could be used to measure the residence time distribution within the reactor volume. Computational fluid dynamics (CFD) calculations were also conducted using a modeled geometry to represent the experimental apparatus. The performance of the CFD model was tested against experimental data, verifying that the CFD model accurately predicted the nucleation and growth of the nanomaterials inside the reactor.

Bayesian Deconvolution of Vessel Residence Time Distribution

Abstract Residence time distribution (RTD) within vessels is a critical aspect for the design and operation of continuous flow technologies, such as hydrothermal synthesis of nanomaterials. RTD affects product characteristics, such as particle size distribution. Tracer techniques allow measurement of RTD, but often cannot be used on an individual vessel in multiple vessel systems due to unsuitable exit flow conditions. However, RTD can be measured indirectly by removal of this vessel from the system and deconvoluting the resulting detected tracer profile from the original trace of the entire system. This paper presents three models for deconvolution of RTD: BAY an application of the Lucy-Richardson iterative algorithm using Bayes’ Theorem, LSQ an adaptation of a least squares error approach and FFT a Fast Fourier Transform. These techniques do not require any assumption about the form of the RTD. The three models are all accurate in theoretical tests with no simulated measurement error. For scenarios with simulated measurement error in the convoluted distribution, the FFT and BAY models are both very accurate. The LSQ model is the least suitable and the output is very noisy; smoothing functions can produce smooth curves, but the resulting RTD is less accurate than the other models. In experimental tests the BAY and FFT models produce near identical results which are very accurate. Both models run quickly, but in real time control the runtime for BAY would have to be considered further. BAY does not require any filtering or smoothing here, and so potentially there are applications where it might be more useful than FFT.