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1. Experimental and computational investigation of mixing dynamics in millifluidic jet mixing reactors

   Aimiuwu, Godstand; Khan, Faiz; Mualen, David; Tang, Wei-Ting; Brunelli, Nicholas A; Paulson, Joel A; Winter, Jessica O; Wyslouzil; Barbara (July 2025, Chemical engineering journal)

   Rapid mixing is a critical step in many nanoparticle syntheses that can impact the ability to scale production from bench to industrial levels. This study combines experimental and computational approaches to characterize mixing dynamics in crossflow jet mixing reactors (JMRs) with millimeter-scale internal dimensions. The Villermaux-Dushman reaction system is used to quantify experimental mixing times across different reactor sizes and flow rates. Complementary computational fluid dynamics (CFD) simulations assess changes in the state of the flow and estimate mixing times under varying operating conditions. Mixing times derived from CFD results agree well with the experimental results for mixing indices between 0.95 and 0.98. To demonstrate the impact of mixing on nanoparticle formation, we synthesize polybutylacrylate-b-polyacrylic acid (PBA-PAA) block co-polymer nanoparticles, confirming the existence of a critical flow rate beyond which particle size stabilizes. Additionally, we produce polylactic acid-co-glycolic acid (PLGA) nanoparticles incorporating a hydrophobic dye, achieving an average particle size below 300 nm at a throughput of ∼ 1.3 kg/day. These results provide insights into optimizing JMRs for high-throughput, reproducible nanoparticle synthesis, bridging the gap between benchtop and industrial-scale production. 

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2. Experimental and computational investigation of mixing dynamics in millifluidic jet mixing reactors

   https://doi.org/10.1016/j.cej.2025.164078  

   Aimiuwu, Godstand; Khan, Faiz; Mualen, David; Tang, Wei-Ting; Brunelli, Nicholas A; Paulson, Joel A; Winter, Jessica O; Wyslouzil, Barbara (July 2025, Chemical Engineering Journal)

   Rapid mixing is a critical step in many nanoparticle syntheses that can impact the ability to scale production from bench to industrial levels. This study combines experimental and computational approaches to characterize mixing dynamics in crossflow jet mixing reactors (JMRs) with millimeter-scale internal dimensions. The Villermaux-Dushman reaction system is used to quantify experimental mixing times across different reactor sizes and flow rates. Complementary computational fluid dynamics (CFD) simulations assess changes in the state of the flow and estimate mixing times under varying operating conditions. Mixing times derived from CFD results agree well with the experimental results for mixing indices between 0.95 and 0.98. To demonstrate the impact of mixing on nanoparticle formation, we synthesize polybutylacrylate-b-polyacrylic acid (PBA-PAA) block co-polymer nanoparticles, confirming the existence of a critical flow rate beyond which particle size stabilizes. Additionally, we produce polylactic acid-co-glycolic acid (PLGA) nanoparticles incorporating a hydrophobic dye, achieving an average particle size below 300 nm at a throughput of ~1.3 kg/day. These results provide insights into optimizing JMRs for high-throughput, reproducible nanoparticle synthesis, bridging the gap between benchtop and industrial-scale production. 

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3. Mixing Time Prediction in a Ladle Furnace

   https://doi.org/10.3390/met14050518  

   Guo, Xipeng; Liu, Yun; Jojo-Cunningham, Yasmeen; Silaen, Armin; Walla, Nicholas; Zhou, Chenn (May 2024, Metals)

   This paper presents a study on the effectiveness of two turbulence models, the large eddy simulation (LES) model and the k-ε turbulence model, in predicting mixing time within a ladle furnace using the computational fluid dynamics (CFD) technique. The CFD model was developed based on a downscaled water ladle from an industrial ladle. Corresponding experiments were conducted to provide insights into the flow field, which were used for the validation of CFD simulations. The correlation between the flow structure and turbulence kinetic energy in relation to mixing time was investigated. Flow field results indicated that both turbulence models aligned well with time-averaged velocity data from the experiments. However, the LES model not only offered a closer match in magnitude but also provided a more detailed representation of turbulence eddies. With respect to predicting mixing time, increased flow rates resulted in extended mixing times in both turbulence models. However, the LES model consistently projected longer mixing times due to its capability to capture a more intricate distribution of turbulence eddies. 

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4. Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset

   https://doi.org/10.5281/zenodo.6304194  

   Jacovic, Marko; Rivas, Xaime Rey (January 2022, Zenodo)

   {"Abstract":["Data files were used in support of the research paper titled \u201cMitigating RF Jamming Attacks at the Physical Layer with Machine Learning<\/em>" which has been submitted to the IET Communications journal.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAll data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'<\/p>\n\n'ModeSelect.py': class used to defined the antenna state selection algorithm<\/li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna<\/li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning<\/li>'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only<\/li><\/ul>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAuthors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar\nContact: krd26@drexel.edu<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nTop-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nclassifier_training: files used for training classifiers that are integrated into SDR platform<\/p>\n\n'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)<\/li>'classTrain.py' is the main parser for training the classifiers<\/li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'<\/li><\/ul>\n\npost_processing_classifier: contains logs of online classifier outputs and processing script<\/p>\n\n'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario<\/li>'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'<\/li><\/ul>\n\npost_processing_mgen: contains MGEN receiver logs and parser<\/p>\n\n'configs' contains JSON files to be used with parser for each experiment<\/li>'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'<\/li><\/ul>\n\nray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.<\/p>\n\nDirectory contains detailed 'readme.txt' for understanding.<\/li>Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently. \n\tS. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.<\/li><\/ul>\n\t<\/li><\/ul>\n\nresults: contains results obtained from study<\/p>\n\n'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'<\/li>'mitigation_performance' contains figures generated by 'post_processing_mgen.'<\/li>'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'<\/li><\/ul>\n\ntuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study<\/p>\n\n'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.<\/li>'data_summary.txt'this file contains the summaries from all the CSV files for convenience.<\/li><\/ul>"]} 

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5. Local Bayesian Dirichlet mixing of imperfect models

   https://doi.org/10.1038/s41598-023-46568-0  

   Kejzlar, Vojtech; Neufcourt, Léo; Nazarewicz, Witold (December 2023, Scientific Reports)

   Abstract To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations. 

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