Chemical Reactor Research Articles

Systematic analysis of mixing and segregation patterns of binary mixtures in fluidised beds for multi-functional processes

Fluidized beds are increasingly used in renewable energy and chemical production due to their versatility in handling different solids for multi-functional industrial applications. The diversity in size and density of solid particles impacts fluidization, influencing mixing and segregation behaviours critical for optimizing chemical processes and reactor design. This study investigates the expansion and segregation behaviours of mixed Geldart group powders in binary systems, simulating polydispersed beds with different materials and catalysts. By applying a modified Cheung equation and an adapted Gibilaro-Rowe model, the study analyzes segregation behaviours of Geldart Group A and B materials at varying mixing rates and gas flow velocities. Results show a good match between experimental data and model predictions. Using novel non-invasive X-ray imaging, the study provides real-time analysis of mixing and segregation at different fluidization regimes and temperatures. These findings aid in designing and optimizing advanced thermochemical conversion technologies, enhancing process efficiency and resilience.

Numerical computations of MHD mixed convection flow of Bingham fluid in a porous square chamber with a wavy cylinder

This work examines the behavior of Bingham fluids in porous media under magnetohydrodynamic (MHD) conditions, addressing a significant issue in fluid dynamics and heat transport. The authors study mixed convection in a porous square chamber with twisted cylinders using COMSOL Multiphysics and finite element techniques. Compared to other research, this one offers a more complicated and realistic model since it integrates Bingham fluid flow and MHD effects in a porous chamber with wavy cylinders. Plotting of streamlines, isotherms, and Nusselt numbers is done for several parameters: Numbers Reynolds (1, 5, 10, 50), Hartmann (0, 25, 50, 100), Bingham (0, 1, 5, 10), Darcy (10−4, 10−3, 10−2, 10−1), and Grashof (102,103,104,105) are among those assigned numbers. According to findings, larger Bn values promote thermal stratification and decrease flow mobility, which results in dominating conductive heat transmission. The nusselt number falls with Ha and Bn but rises with Re, Da, and Gr. The study aims to provide useful insights into several industrial disciplines, such as nuclear power plant architecture and petroleum engineering, by enhancing heat transfer in non-Newtonian fluids under magnetic fields. It also boosts the efficiency of chemical reactors and filters. Based on our findings, for lower Re = 1, the lowest value of Numean is 2.602, while at Re = 50, its values rise to 6.425.

An efficient fourth-order convergent scheme based on half-step spline function for two-point mixed boundary value problems

The present work aims to introduce a novel numerical method for solving second-order two-point mixed boundary value problems. Mixed boundary value problems occur in various scientific fields, including quantum mechanics, fluid dynamics, and chemical reactor theory. The proposed method provides a highly accurate, fourth-order convergent numerical solution, achieved by implementing a compact finite difference method based on non-polynomial spline in tension approximations. Notably, the discretisation involves the use of half-step grid points, eliminating the need to modify the method at singularities while dealing with singular boundary value problems. The article also includes a comprehensive convergence analysis that demonstrates the theoretical order of convergence of the method. Additionally, the results obtained from solving six diverse mixed boundary value problems, including Burger's equation and Bratu-type equation, are compared to showcase the superiority and effectiveness of the proposed method.

Online Three-Dimensional Fuzzy Reinforcement Learning Modeling for Nonlinear Distributed Parameter Systems

Distributed parameter systems (DPSs) frequently appear in industrial manufacturing processes, with complex characteristics such as time–space coupling, nonlinearity, infinite dimension, uncertainty and so on, which is full of challenges to the modeling of the system. At present, most DPS modeling methods are offline. When the internal parameters or external environment of DPS change, the offline model is incapable of accurately representing the dynamic attributes of the real system. Establishing an online model for DPS that accurately reflects the real-time dynamics of the system is very important. In this paper, the idea of reinforcement learning is creatively integrated into the three-dimensional (3D) fuzzy model and a reinforcement learning-based 3D fuzzy modeling method is proposed. The agent improves the strategy by continuously interacting with the environment, so that the 3D fuzzy model can adaptively establish the online model from scratch. Specifically, this paper combines the deterministic strategy gradient reinforcement learning algorithm based on an actor critic framework with a 3D fuzzy system. The actor function and critic function are represented by two 3D fuzzy systems and the critic function and actor function are updated alternately. The critic function uses a TD (0) target and is updated via the semi-gradient method; the actor function is updated by using the chain derivation rule on the behavior value function and the actor function is the established DPS online model. Since DPS modeling is a continuous problem, this paper proposes a TD (0) target based on average reward, which can effectively realize online modeling. The suggested methodology is implemented on a three-zone rapid thermal chemical vapor deposition reactor system and the simulation results demonstrate the efficacy of the methodology.

Entropy generation outcomes in peristalsis of Carreau–Yasuda nanofluid flow with activation energy

AbstractThis paper focuses on the study of activation energy and entropy generation in the peristaltic flow of a Carreau–Yasuda nanofluid. Peristaltic transport, coupled with entropy generation and activation energy in a curved geometry, has significant applications in biomedical and industrial processes involving non‐Newtonian fluids. Blood flow in the arteries, the movement of chyme in the gastrointestinal tract, and peristaltic motion replicate the natural muscular contractions that drive fluid flow in the physiological systems. The integration of entropy generation allows for the evaluation of energy efficiency and the analysis of losses due to viscous dissipation. Activation energy plays a crucial role in processes such as drug delivery, where temperature‐sensitive reactions or biochemical changes affect fluid behavior. In industrial applications, like peristaltic pumps in chemical reactors or polymer processing, the consideration of entropy and activation energy aids in optimizing thermal management and reaction rates. The mathematical model is developed under these assumptions, and the governing equations are numerically solved using the NDSolve function in Mathematica. Graphical results show that higher activation energy and concentration reduce the reaction rate, while the Bejan number and entropy generation increase with a larger Brinkman number. Velocity and temperature increase under slip conditions, whereas concentration decreases, and a stronger radial magnetic field reduces both velocity and the size of the trapped bolus.

Non-Spherical Cavitation Bubbles: A Review

Cavitation is a phase-change phenomenon from the liquid to the gas phase due to an increased flow velocity. As it causes severe erosion and noise, it is harmful to hydraulic machinery such as pumps, valves, and screw propellers. However, it can be utilized for water treatment, in chemical reactors, and as a mechanical surface treatment, as radicals and impacts at the point of cavitation bubble collapse can be utilized. Mechanical surface treatment using cavitation impacts is called “cavitation peening”. Cavitation peening causes less pollution because it uses water to treat the mechanical surface. In addition, cavitation peening improves on traditional methods in terms of fatigue strength and the working life of parts in the automobile, aerospace, and medical fields. As cavitation bubbles are utilized in cavitation peening, the study of cavitation bubbles has significant value in improving this new technique. To achieve this, many numerical analyses combined with field experiments have been carried out to measure the stress caused by bubble collapse and rebound, especially when collapse occurs near a solid boundary. Understanding the mechanics of bubble collapse can help to avoid unnecessary surface damage, enabling more accurate surface preparation, and improving the stability of cavitation peening. The present study introduces three cavitation bubble types: single, cloud, and vortex cavitation bubbles. In addition, the critical parameters, governing equations, and high-speed camera images of these three cavitation bubble types are introduced to support a broader understanding of the collapse mechanism and characteristics of cavitation bubbles. Then, the results of the numerical and experimental analyses of non-spherical cavitation bubbles are summarized.

Design rules for catalysis in single-particle plasmonic nanogap reactors with precisely aligned molecular monolayers

Plasmonic nanostructures can both drive and interrogate light-driven catalytic reactions. Sensitive detection of reaction pathways is achieved by confining optical fields near the active surface. However, effective control of the reaction kinetics remains a challenge to utilize nanostructure constructs as efficient chemical reactors. Here we present a nanoreactor construct exhibiting high catalytic and optical efficiencies, based on a nanoparticle-on-mirror (NPoM) platform. We observe and track pathways of the Pd-catalysed C-C coupling reaction of molecules within a set of nanogaps presenting different chemical surfaces. Atomic monolayer coatings of Pd on the different Au facets enable tuning of the reaction kinetics of surface-bound molecules. Systematic analysis shows the catalytic efficiency of NPoM-based nanoreactors greatly improves on platforms based on aggregated nanoparticles. More importantly, we show Pd monolayers on the nanoparticle or on the mirror play significantly different roles in the surface reaction kinetics. Our data provides clear evidence for catalytic dependencies on molecular configuration in well-defined nanostructures. Such nanoreactor constructs therefore yield clearer design rules for plasmonic catalysis.

Emissions and Flame Stability Assessment of Hydrogen Addition and Air Dilution in a Micro Gas Turbine Combustor Using 0D/1D Modeling by Chemical Reactor Network

Abstract Hydrogen emerges as a promising fuel for clean and sustain- able electricity production when utilized in micro gas turbines (mGTs). However, some challenges linked to hydrogen usage must be overcome as its high reactivity can lead to increased NOx emissions and flame instabilities. Literature suggests that combustion air humidification and/or exhaust gas recirculation (EGR) may be methods to reduce this reactivity. However, these measures are limited due to the small operating range of the mGT combustor. In this context, a 20 kWth mGT combustor is investigated un- der various inlet conditions to assess the effect of EGR towards increased flame stability and emission control when operating under hydrogen-enriched methane firing, using a Chemical Re- action Network (CRN) model. The CRN modeling is a fast and low computational complexity tool to model combustion perfor- mance and emissions by representing complex reactive flow fields through idealized reactor models, leading to reduced computa- tional costs. This study examines partial load conditions, fuel compositions, and the effect of combustion air dilution through EGR. The CRN model of the combustion process is designed based on combustion zones extracted from the main flow fields with similar thermo-chemical states from CFD, while emission predictions are experimentally validated for different operating points. Predicted NOx and CO emissions by the proposed CRN model show an acceptable agreement with the experimental data at high power loads. However, its accuracy diminishes for loads below 70% due to the variability in model tuning parameters across different loads, whereas the

A Parametric Study on NOx Emissions From Ammonia Containing Product Gas in Rich–Quench–Lean Combustion

Abstract Due to climate change, there has been an increasing demand for fuels that can accelerate the transition away from fossil fuels to clean energy. Humidified product gas obtained from gasifying biomass is emerging as a promising candidate to replace natural gas, as it is composed of a gaseous mixture of hydrogen, steam, carbon monoxide, and methane. However, the gasification process releases ammonia and other nitrogen bearing compounds into the product gas, resulting in substantial increases in nitric oxides, NOx, in the exhaust. As such, there has been a recent push to understand the underlying chemical kinetics that drive NOx formation in order to optimize gas turbines to mitigate emissions at the source. In this study, a simplified chemical reactor network (CRN) model for a gas turbine rich–quench–lean (RQL) combustor was developed in cantera. The following parameters were investigated in this study: equivalence ratio of the primary section, overall equivalence ratio, steam dilution, postflame residence time, and recirculation from the postquench region to the primary section. Additionally, a benchmark CRN representing a lean burner (LB) is also developed. Results of the CRN model suggest that, when comparing to LB, a RQL type combustor delivers up to a 75% reduction in emissions. Additionally, it was found that, for both the LB and RQL combustors, an overall lean to stoichiometric equivalence ratio is well suited to reduce emissions in highly humidified fuels, while for moderately humidified fuels it is preferable to operate in an overall slightly rich equivalence ratio. The difference observed is mainly due to the fact that, at high humidification and lean conditions, the temperature is favorable for the conversion of ammonia to nitrogen, while, at moderate humidification and rich conditions, NO reacts with ammonia in the reburn process. Finally, it is suggested that the incorporation of recirculation from the secondary section to the primary section of the RQL burner results in a broader low emission region, due to more favorable conditions for ammonia conversion to nitrogen in the primary section.

Enhancing heat and mass transfer in MHD tetra hybrid nanofluid on solar collector plate through fractal operator analysis

Improved solar heat transfer methods are needed for the widespread use of solar energy. Due of their exceptional thermal characteristics, nanofluids have received a great deal of interest in this field. This research examines the use of tetra hybrid nanofluids in solar applications, with an emphasis on heat and mass transport properties. Due to their advantageous thermal characteristics, these nanofluids are well-suited for solar power production and other thermal applications. Accurate results for velocity, concentration, and temperature profiles are produced thanks to the model's use of fractal fractional derivatives and the Crank-Nicholson method for obtaining numerical solutions. In order to learn how different variables affect heat transport, parametric experiments are performed. The results show that tetra hybrid nanofluids greatly improve heat transfer performance over standard nanofluids. This improvement helps flat-plate solar collectors absorb more sunlight and increase their overall efficiency. The rate of mass transfer between phases may be quantified in the design and research of chemical reactors using the Sherwood number, an important quantity in chemical engineering. Its value is shown in a number of processes, including extraction, distillation, and catalytic reactions.

Evaluation of a reduced-pressure chemical ion reactor utilizing adduct ionization for the detection of gaseous organic and inorganic species

Abstract. Volatile organic compounds (VOCs) and volatile inorganic compounds (VICs) provide critical information across many scientific fields including atmospheric chemistry and soil and biological processes. Chemical ionization (CI) mass spectrometry has become a powerful tool for tracking these chemically complex and temporally variable compounds in a variety of laboratory and field environments. It is particularly powerful with time-of-flight mass spectrometers, which can measure hundreds of compounds in a fraction of a second and have enabled entirely new branches of VOC and/or VIC research in atmospheric and biological chemistry. To accurately describe each step of these chemical, physical, and biological processes, measurements across the entire range of gaseous products is crucial. Recently, chemically comprehensive gas-phase measurements have been performed using many CI mass spectrometers deployed in parallel, each utilizing a different ionization method to cover a broad range of compounds. Here we introduce the recently developed Vocus AIM (adduct ionization mechanism) ion–molecule reactor (IMR), which samples trace vapors in air and ionizes them via chemical ionization at medium pressures. The Vocus AIM supports the use of many different reagent ions of positive and negative polarity and is largely independent of changes in the sample humidity. Within the present study, we present the performance and explore the capabilities of the Vocus AIM using various chemical ionization schemes, including chloride (Cl−), bromide (Br−), iodide (I−), nitrate (NO3-), benzene cations (C6H6+), acetone dimers ((C3H6O)2H+), and ammonium (NH4+) reagent ions, primarily in laboratory and flow tube experiments. We report the technical characteristics and operational principles, and compare its performance in terms of time response, humidity dependence, and sensitivity to that of previous chemical ionization approaches. This work demonstrates the benefits of the Vocus AIM reactor, which provides a versatile platform to characterize VOCs and VICs in real time at trace concentrations.

Deep learning assisted characterization of bubble behavior in a gas-solid fluidized bed with binary particle mixtures

Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, DeepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds.

Enhanced Transverse Dispersion in 3D-Printed Logpile Structures: A Comparative Analysis of Stacking Configurations

Three-dimensionally printed logpile structures have demonstrated the tunability of the transverse dispersion behavior, which is relevant in the context of chemical reactor design. The current modeling study aims to further investigate the trade-offs in such structures, extending the range of geometries investigated and addressing the limitations associated with the pseudo-2D nature of previous experiments. The relative transverse dispersion coefficient and pressure drop were determined using computational fluid dynamics simulations in OpenFOAM for structures with different stacking configurations, porosities and scaling of the structures’ unit cell along the secondary transverse axis. The latter could not be varied in previous experiments, but the current results demonstrate that this limitation suppresses vortex shedding in structures with high porosity. These vortices significantly enhance the transverse dispersion. By using a staggered stacking configuration on both transverse axes, an earlier onset of this phenomenon could be realized. Importantly, operation in this regime could be achieved without an equivalent increase in pressure drop, offering a favorable operating trade-off. The findings demonstrate that at low Reynolds numbers, the studied structures consistently outperform randomly packed beds of spheres, highlighting their potential for chemical process intensification.

A novel highly sensitive test reaction for micromixing: Acid‐base neutralization and alkaline hydrolysis of ethyl oxalate

AbstractMicromixing in chemical reactors can be characterized through test reactions that are sensitive to mixing. A new pair of parallel competitive reactions, including acid–base neutralization and diethyl oxalate hydrolysis, is proposed in this work. It has clear principles and high sensitivity to micromixing with quantitative accuracy and operational simplicity. The measurement results obtained from stopped‐flow spectra show that the alkaline hydrolysis of diethyl oxalate follows second‐order kinetics, and the rate constant conforms to the Arrhenius equation k2 = 2.331 × 108 exp(−26.92 × 103/RT) (L/mol/s). The estimated half‐life of hydrolysis is approximately 3 × 10−4 s under the selected concentration combinations, which provides significant advantages for the micromixing assessment in the strong turbulent fluid environment. In the same stirred tank, the critical feed time of new test reaction is shorter than that of the Villermaux–Dushman reaction. Overall, this work provides practical ideas for screening other desired esters for fast hydrolysis to construct more test reactions for micromixing.

PH-induced morphological reversible transition from microparticles to vesicles for effective bacteria entrapment

Polymer particles with stimuli-responsive properties offer promising applications in healthcare, chemical reactors, development of artificial cells and organelles, as well as in the entrapment of bacteria. In this study, a novel biocompatible, biodegradable, and pH-responsive diblock copolymer based on polylactide (PLA) and poly(2-(diisopropylamino)ethyl methacrylate) (PDPA) was synthesized via a metal-free one-pot/simultaneous ring-opening polymerization (ROP) and reversible addition-fragmentation chain transfer (RAFT) approach (ROP/RAFT). This copolymer was then employed to produce highly monodisperse microparticles using microfluidics droplet generation technique. Utilizing confocal microscopy imaging, a reversible pH-induced morphological transition from microparticles to vesicle-like structures was observed at pH 5.1. The reversible shift in morphology from microparticles to giant vesicles (Vs) in acidic environments is triggered by the protonation of amino groups of the PDPA block, rendering vesicle surfaces positively charged − an advantageous feature for attracting and engulfing negatively charged bacteria. Initial validation involved electrostatic interactions with negatively charged latex resin beads followed by assessing interaction capabilities with gram-negative bacteria, Escherichia coli (E. coli). Additionally, the reversible morphological transition of microparticles-to-vesicles was employed to study drug release at different pHs. This approach proven to be a promising strategy for targeted drug delivery and bacteria entrapment using smart pH-responsive microparticles.

Pure copper extrusion additive manufacturing of lattice structures for enabling enhanced thermal efficiency in hydrogen production

The high geometrical complexity allowed by Additive Manufacturing (AM) of pure copper can support the development of more efficient catalytic supports that can enable the design of efficient reactors for key processes for energy transition, i.e., hydrogen production with methane steam reforming or CO2 conversion to synthetic methane, thanks to the enhanced thermal conductivity of the catalytic support. Low relative density lattice geometries with open cell copper geometries can be adopted as catalyst supports in the new generation chemical reactors to improve the heat transfer. This work presents a manufacturability study about pure copper lattice filters produced via metal Extrusion AM i.e., via feedstock 3D printing and sintering. The results show that feasible parts design can be reached and produced and that heat-exchange performances can be increased with respect to conventional supports, as confirmed by lab-scale heat transfer tests. A specific metrological assessment based on CTscan data is implemented on the printed parts to qualify them and to ensure a good thermal coupling between the printed filters and the heat exchanger main tube elements.

Chaotic mixing coupled electromagnetic heating in a tubular reactor

To address the need for enhanced biochemical reactions and overcome the shortcomings associated with passive and active mixer fabrication, reactor designs must utilize the coupled mixing concept to address the lack of insufficient heat and mass transfer through notable alterations of the physical properties. This study introduces a tubular microwave reactor based on chaotic flow dynamics, attempting to combine the concept of active and passive mixing to stimulate the development of eddy and secondary flows through electromagnetic fields and geometric disturbance enhance heat and mass transfer. Experiments and simulation analysis validate the validity of the prediction model, and reveal the fluid flow, mixing, electromagnetic and thermal characteristics and their synergistic mechanism, and shows the potential to enhance the mass and heat transfer performance at low Reynolds number, which provides a new idea for the design of chemical reactors and biological analysis.

Application of continuous stirring tank reactor for controllable synthesis of Cu7S4 nanocrystals

As a typical model for a chemical reactor in industry, the continuous stirring tank reactor has been widely used in waste-water treatment, industrial catalysis, and biological fermentation as well as great application prospects in the synthesis of nanomaterials. In this report, a convenient lab-scale continuous stirring tank reactor was assembled and utilized to synthesize Cu7S4 nanocrystals by injecting cupric bromide and sulfur solution in the presence of ascorbic acid and polyethyleneimine at 60 °C. The morphology control of Cu7S4 nanocrystals was accomplished by simply adjusting the agitation speed. Cu7S4 nanofibers with an average diameter of 48 ± 4.42 nm and a length of several micrometers were obtained at 80 rpm. Cu7S4 nanoplates with a thickness of 89 ± 13.56 nm and a plane size of 103 ± 16.47 nm were synthesized at 90 rpm, and the size of the nanoplates was regulated by continuously increasing the agitation speed from 90 rpm to 1000 rpm. Furthermore, the influences of two additional conditions (the mean residence time and the concentration of the feed solution) on the morphology of Cu7S4 nanocrystals, were also investigated. Therefore, these results could facilitate the understanding of the behaviors of CSTR in the synthesis of Cu7S4 nanocrystals and could also provide an important reference for the continuous synthesis of other nanoparticles.

Numerical analysis of unsteady free convection of Al2O3 inside a tubular reactor under the influences of exothermic reaction, and inclined MHD as an application to chemical reactor

This paper delves into the numerical investigation of aluminum oxide–water nanofluid thermal and dynamic performances under the influences of magnetic field application and chemical reaction, utilizing the Finite Element Method within a circular enclosure containing three inner tubes, as an application to the heat exchanging phenomenon between the reactive shell of the cavity and the surface of the triple tubes. Various governing parameters were studied for their interaction on the nanofluid flow and heat transmission rate within the proposed geometry, including the Rayleigh parameter (103≤Ra≤105), Hartmann parameter (0≤Ha≤61), nanoparticles concentration (0≤∅≤6×10-2), magnetic rotational angle (0o≤γ≤90o), and Frank-Kamenetskii parameter (0≤Fk≤3). The results indicated that raising Ra from 103 to 105 results in expediting the nanofluid velocity by 10.62 % and 100 % respectively as well as raising the total heat transfer efficiency. The nanofluid speed was also increased by 28.57 % when Fk has to further increase to a value of 3. When there was no exothermic activity present, the rate of heat transmission was at its lowest, and it was greater when the Fk value was 3. Similarly, there were discernible impacts in various areas of the geometry as the Ha number intensified and the Nuavg decreased. Improvements in local and mean Nusselt parameters are observed when the concentration of nanoparticles is increased, suggesting better heat transfer, achieving an increase by 7 % in the average Nusselt number. This research emphasizes the importance of nanoparticle concentration in raising the medium’s rates of heat transmission, contributing to advancements in energy storage development.

Polycrystalline inclusion-free growth of thick, lattice-matched SiGeC with high substitutional Carbon concentrations up to 2%

Lattice-matched SiGeC with ~20% of Ge was grown at 550°C, 10 Torr on a 300 mm Si substrate in an industrial standard Reduced Pressure Chemical Vapor Deposition reactor using commercially available Si2H6/GeH4/SiH3CH3 precursors. Thicknesses exceeding 250 nm resulted in cluster formation in SiGeC with [C]Sub~2.0% and [C]Int~0.4%, with a surface density up to 6.13x106 µm-2. Room temperature photoluminescence measurements showed that the clusters were non-radiative defects and Transmission Electron Microscopy, using the ASTAR system, proved they were polycrystalline with random orientation. Cluster-free growth was achieved for 1 µm thick SiGeC with [C]Sub~1.0% and no interstitial carbon atoms, however, without lattice-matched growth on a Si substrate. Injecting HCl during a co-flow process resulted in a smooth surface with a RMS roughness of 0.23 nm and a reduction of [C]Intfrom ~0.7% down to ~0.1%, close to the detection limit of X-ray Photoelectron Spectroscopy at 0.1%. This led to a significant cluster surface density reduction down to 1.15x104 µm-2. The injection of HCl caused an increase in the Ge concentration from 19.7% up to 28.9% and a non-lattice-matched growth. A Cyclic Deposition Etch (CDE) process resulted in no [C]Int reduction, but significantly reduced the cluster surface density down to 4.60x104 µm-2. CDE-grown SiGeC was lattice-matched to the Si substrate and yielded a smooth surface with a RMS roughness of 0.22 nm. This enabled the growth of high crystalline quality, thick, lattice-matched layers on a 300 mm Si substrate with material properties beyond pure Si.