Poonoosamy, J.* ; Kaspor, A.* ; Schreinemachers, C.* ; Bosbach, D.* ; Cheong, O.* ; Kowalski, P.M.* ; Obaied, A.*
    
    
        
A radiochemical lab-on-a-chip paired with computer vision to unlock the crystallization kinetics of (Ba,Ra)SO4.
    
    
        
    
    
        
        Sci. Rep. 14, 12 (2024)
    
    
    
      
      
	
	    (Ra,Ba)SO4 solid solutions are commonly encountered as problematic scales in subsurface energy-related applications, e.g., geothermal systems, hydraulic fracturing, conventional oil and gas, etc. Despite its relevance, its crystallization kinetics were never determined because of radium (226), high radioactivity (3.7 × 1010 Bq g−1), and utilization in contemporary research, therefore constrained to trace amounts (< 10−8 M) with the composition of BaxRa1-xSO4 commonly restricted to x > 0.99. What if lab-on-a-chip technology could create new opportunities, enabling the study of highly radioactive radium beyond traces to access new information? In this work, we developed a lab-on-a-chip experiment paired with computer vision to evaluate the crystal growth rate of (Ba,Ra)SO4 solid solutions. The computer vision algorithm enhances experimental throughput, yielding robust statistical insights and further advancing the efficiency of such experiments. The 3D analysis results of the precipitated crystals using confocal Raman spectroscopy suggested that {210} faces grew twice as fast as {001} faces, mirroring a common observation reported for pure barite. The crystal growth rate of (Ba0.5Ra0.5)SO4 follows a second-order reaction with a kinetic constant equal to (1.23 ± 0.09) × 10−10 mol m−2 s−1.
	
	
	    
	
       
      
	
	    
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        Publication type
        Article: Journal article
    
 
    
        Document type
        Scientific Article
    
 
    
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        Keywords
        Computer Vision ; Crystal Growth ; Microfluidics ; Ra-bearing Barite ; Solid Solutions; Solid-solution; Crystal-structure; Growth-kinetics; Porous-medium; Radium; Barite; Recrystallization; Coprecipitation; Thermodynamics; Precipitation
    
 
    
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        Language
        english
    
 
    
        Publication Year
        2024
    
 
    
        Prepublished in Year
        0
    
 
    
        HGF-reported in Year
        2024
    
 
    
    
        ISSN (print) / ISBN
        2045-2322
    
 
    
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        2045-2322
    
 
    
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	    Volume: 14,  
	    Issue: 1,  
	    Pages: 12 
	    Article Number: ,  
	    Supplement: ,  
	
    
 
    
        
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            Nature Publishing Group
        
 
        
            Publishing Place
            London
        
 
	
        
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        Peer reviewed
    
 
    
        Institute(s)
        Helmholtz AI - FZJ (HAI - FZJ)
    
 
    
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        Grants
        Helmholtz AI projects
European Research Council through the project GENIES (ERC)
European Research Council
    
 
    
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        Erfassungsdatum
        2024-05-08