Barium Radium Sulfate Crystallization Kinetics with Lab-on-a-Chip and Computer Vision

In an article published in the journal Nature, researchers explored the crystallization kinetics of solid solutions composed of barium sulfate (BaSO4) and radium sulfate (RaSO4) ((Ba,Ra)SO4) solid solutions, common scales in subsurface energy applications.

Raman spectroscopic analysis of (Ba,Ra)SO4 crystals. (a) Raman spectra of a typical crystal that precipitated in the microfluidic reactor and standard spectra of commercial BaSO4 and SrSO4 and the microfluidic reactor vessel; (b) an example of a pseudo rhombohedral crystal of (Ba,Ra)SO4 on which further Raman measurements were done to reconstruct its (c) 3D geometry. Image Credit: https://www.nature.com/articles/s41598-024-59888-6
Raman spectroscopic analysis of (Ba,Ra)SO4 crystals. (a) Raman spectra of a typical crystal that precipitated in the microfluidic reactor and standard spectra of commercial BaSO4 and SrSO4 and the microfluidic reactor vessel; (b) an example of a pseudo rhombohedral crystal of (Ba,Ra)SO4 on which further Raman measurements were done to reconstruct its (c) 3D geometry. Image Credit: https://www.nature.com/articles/s41598-024-59888-6

Overcoming challenges posed by radium's high radioactivity, a lab-on-a-chip experiment coupled with computer vision (CV) was developed to study crystal growth rates. Through statistical analysis and confocal Raman spectroscopy, insights revealed that {210} faces grow twice as fast as {001} faces. The crystal growth rate of (Ba0.5Ra0.5)SO4 followed a second-order reaction with a kinetic constant of (1.23 ± 0.09) × 10−10 mol m−2 s−1.

Background

Radium, a radioactive byproduct of uranium and thorium decay, forms (Ba,Ra)SO4 solid solutions due to its affinity for BaSO4. These solid solutions are problematic scales encountered in various subsurface energy-related applications, including conventional oil and gas extraction, hydraulic fracturing, and geothermal systems. While considerable research has focused on the thermodynamic properties of (Ba,Ra)SO4, studies quantifying its precipitation kinetics are scarce. This gap arises from the challenges associated with working with radium, which requires specialized equipment and safety precautions due to its high radioactivity.

Microfluidics and lab-on-a-chip technologies offer promising solutions by enabling experiments with high concentrations of radium while minimizing radiation exposure. However, current evaluation methods for microfluidic experiments often require extensive manual labor and time-consuming processes, hindering their efficiency.

This paper aimed to bridge these gaps by employing microfluidic experiments monitored by time-resolved microscopy coupled with Raman spectroscopy and CV techniques. By leveraging these advanced tools, the authors investigated the crystal growth rate of (Ba,Ra)SO4 solid solutions under various saturation conditions.

The development of a CV pipeline facilitated the automated analysis of crystal growth from two-dimensional (2D) optical images, providing valuable insights into the precipitation kinetics of (Ba,Ra)SO4. This approach represented a significant step towards automated radio-geochemical lab-on-chip platforms, enhancing efficiency and advancing our understanding of radium-containing solid solutions.

Experimental Setup and Methodology

In this research, a microfluidic experimental setup was utilized to investigate the crystallization kinetics of (Ba,Ra)SO4 solid solutions, which were commonly encountered in various subsurface energy-related applications due to radium's affinity for barite. A stock solution containing 0.0375 mM of radium-226 (226Ra) was prepared, and the radium activity was quantified via gamma spectrometry.

Barium chloride (BaCl2) and sodium sulfate (Na2SO4) solutions were mixed at varying concentrations and injected into the microfluidic mixer. The flow field in the mixer was simulated using computational fluid dynamics, and the initial saturation ratio was calculated. Time-lapse optical microscopy captured the nucleation and growth of crystals over three hours, with micrographs collected at regular intervals. Raman spectroscopy and three-dimensional (3D) tomographs were used to analyze single crystals, providing insights into their morphology and growth patterns.

Additionally, density functional theory (DFT) calculations were performed to simulate Raman spectra of relevant compounds. A CV pipeline was developed to automate crystal analysis from 2D optical images, including preprocessing steps to reduce noise and identify crystal shapes. Geometric filters were applied to match 2D shapes with 3D morphologies, and crystal volumes were calculated using computer-aided design (CAD) software.

The growth rate of crystals was determined based on experimental and theoretical considerations. Overall, this comprehensive approach enabled the determination of the crystal growth rate of (Ba,Ra)SO4 solid solutions, shedding light on their precipitation kinetics and offering valuable insights for future research in radio-geochemistry.

Findings and Analysis

The authors delved into the comprehensive evaluation of solution chemistry and stoichiometric saturation functions within the context of crystallization dynamics. Through a series of experiments denoted as A, B, and C, a microfluidic laminar mixing reactor facilitated the controlled crystallization of (Ba,Ra)SO4, with varying concentrations of Na2SO4. Utilizing computational fluid dynamics and geochemical speciation calculations, the researchers precisely tracked solute concentrations and saturation indices along the microfluidic channel, shedding light on the nucleation and growth mechanisms.

The experiments revealed laminar flow conditions, ensuring precise control over mixing dynamics, crucial for studying crystal nucleation and growth. Concentration gradients across the reactor influenced the saturation state, with notable variations observed along the mixing zone. This insight was pivotal in understanding the thermodynamically stable solid solution compositions. The crystallization process resulted in the formation of euhedral-shaped crystals, predominantly exhibiting flattened tabular, orthorhombic bipyramidal, and pseudo-rhombohedral habits.

Raman spectroscopic analysis provided invaluable data for determining the solid solution composition, offering insights into crystal growth dynamics and morphology. A sophisticated CV pipeline was developed to analyze crystal habits, track growth rates, and quantify precipitation kinetics. Through meticulous image processing and 3D modeling, the volume and surface area of growing crystals were accurately determined, enabling precise assessment of precipitation rates.

Furthermore, the determination of the kinetic constant for (Ba0.5Ra0.5)SO4 crystallization provided essential parameters for modeling crystal growth kinetics. Comparison with existing experimental data underscored the reliability and consistency of the findings, highlighting the potential implications for groundwater remediation strategies. These findings illuminated the complex interplay between solution chemistry, fluid dynamics, and crystal growth kinetics, offering valuable insights into the fundamental processes governing mineral precipitation in engineered and natural systems.

Conclusion

In conclusion, the researchers advanced the understanding of the crystallization kinetics of (Ba,Ra)SO4 solid solutions, crucial in subsurface energy applications. Through innovative microfluidic experiments and CV techniques, insights into crystal growth rates were gained, overcoming challenges posed by radium's radioactivity. The developed methodologies offered promising avenues for optimizing wastewater treatment and enhancing the accuracy of reactive transport models.

Future endeavors will focus on automation and expanding applications to higher temperatures. Overall, this work underscored the potential of microfluidics and CV in radiochemistry, paving the way for more efficient and comprehensive studies in this field.

Journal reference:
Soham Nandi

Written by

Soham Nandi

Soham Nandi is a technical writer based in Memari, India. His academic background is in Computer Science Engineering, specializing in Artificial Intelligence and Machine learning. He has extensive experience in Data Analytics, Machine Learning, and Python. He has worked on group projects that required the implementation of Computer Vision, Image Classification, and App Development.

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