Nicholas, T. C. et al. Geometrically frustrated interactions drive structural complexity in amorphous calcium carbonate. Nat. Chem. 16, 36–41 (2024).
Addadi, L., Raz, S. & Weiner, S. Taking advantage of disorder: Amorphous calcium carbonate and its roles in biomineralization. Adv. Mater. 15, 959–970 (2003).
Mergelsberg, S. T. et al. Metastable solubility and local structure of amorphous calcium carbonate (ACC). Geochim. Cosmochim. Acta 289, 196–206 (2020).
Rodriguez-Navarro, C., Kudłacz, K., Cizer, Ö. & Ruiz-Agudo, E. Formation of amorphous calcium carbonate and its transformation into mesostructured calcite. Cryst. Eng. Comm. 17, 58–72 (2015).
Wu, W. et al. Bioinspired stabilization of amorphous calcium carbonate by carboxylated nanocellulose enables mechanically robust, healable, and sensing biocomposites. ACS Nano 17, 6664–6674 (2023).
Otter, L. et al. Growth dynamics and amorphous-to-crystalline phase transformation in natural nacre. Nat. Commun. 14, 2254 (2023).
Goffredo, S. et al. Biomineralization control related to population density under ocean acidification. Nat. Climate Change 4, 593–597 (2014).
De Yoreo, J. J. et al. Crystallization by particle attachment in synthetic, biogenic, and geologic environments. Science 349, aaa6760 (2015).
Kim, J. J. et al. Carbonate coprecipitation for Cd and Zn treatment and evaluation of heavy metal stability under acidic conditions. Environ. Sci. Technol. 57, 3104–3113 (2023).
Poonoosamy, J. et al. The use of microfluidic platforms with raman spectroscopy for investigating the co-precipitation of metals and radionuclides in carbonates. Minerals 13, 636 (2023).
Ait-Mouheb, N. et al. Retention of \(^{226}\)Ra in the sandy Opalinus Clay facies from the Mont Terri rock laboratory, Switzerland. Appl. Geochem. 170, 106048 (2024).
Meldrum, F. C. & Cölfen, H. Controlling mineral morphologies and structures in biological and synthetic systems. Chem. Rev. 108, 4332–4432 (2008).
Gilbert, P. U. et al. Biomineralization: Integrating mechanism and evolutionary history. Sci. Adv. 8, eabl9653 (2022).
Zou, Z. et al. Additives control the stability of amorphous calcium carbonate via two different mechanisms: Surface adsorption versus bulk incorporation. Adv. Funct. Mater. 30, 2000003 (2020).
Raiteri, P. & Gale, J. D. Water is the key to nonclassical nucleation of amorphous calcium carbonate. J. Am. Chem. Soc. 132, 17623–17634 (2010).
Du, H. et al. Amorphous CaCO\(_{3}\): Influence of the formation time on its degree of hydration and stability. J. Am. Chem. Soc. 140, 14289–14299 (2018).
Zou, Z., Xie, J., Macias-Sanchez, E. & Fu, Z. Nonclassical crystallization of amorphous calcium carbonate in the presence of phosphate ions. Crystal Growth & Design 21, 414–423 (2020).
Molnár, Z., Dódony, I. & Pósfai, M. Transformation of amorphous calcium carbonate in the presence of magnesium, phosphate, and mineral surfaces. Geochim. Cosmochim. Acta 345, 90–101 (2023).
Albéric, M. et al. The crystallization of amorphous calcium carbonate is kinetically governed by ion impurities and water. Adv. Sci. 5, 1701000 (2018).
Stephens, C. J., Ladden, S. F., Meldrum, F. C. & Christenson, H. K. Amorphous calcium carbonate is stabilized in confinement. Adv. Funct. Mater. 20, 2108–2115 (2010).
Cavanaugh, J., Whittaker, M. L. & Joester, D. Crystallization kinetics of amorphous calcium carbonate in confinement. Chem. Sci. 10, 5039–5043 (2019).
Meldrum, F. C. & O’Shaughnessy, C. Crystallization in confinement. Adv. Mater. 32, 2001068 (2020).
Yashina, A., Meldrum, F. & Demello, A. Calcium carbonate polymorph control using droplet-based microfluidics. Biomicrofluidics 6, 022001 (2012).
Li, S. & Lian, B. Application of calcium carbonate as a controlled release carrier for therapeutic drugs. Minerals 13, 1136 (2023).
Whittaker, M. L., Sun, W., Duggins, D. O., Ceder, G. & Joester, D. Dynamic barriers to crystallization of calcium barium carbonates. Crystal Growth & Design 21, 4556–4563 (2021).
Poonoosamy, J. et al. A lab on a chip experiment for upscaling diffusivity of evolving porous media. Energies 15, 2160 (2022).
Poonoosamy, J. et al. A lab-on-a-chip approach integrating in-situ characterization and reactive transport modelling diagnostics to unravel (Ba, Sr) SO\(_{4}\) oscillatory zoning. Sci. Rep. 11, 23678 (2021).
Zhang, Z. et al. Investigating the nucleation kinetics of calcium carbonate using a zero-water-loss microfluidic chip. Crystal Growth & Design 20, 2787–2795 (2020).
Poonoosamy, J. et al. A radiochemical lab-on-a-chip paired with computer vision to unlock the crystallization kinetics of (Ba, Ra)SO\(_{4}\). Sci. Rep. 14 (2024).
Lönartz, M., Yang, Y., Deissmann, G., Bosbach, D. & Poonoosamy, J. Capturing the dynamic processes of porosity clogging. Water Resources Research 59 (2023).
Boyd, V. et al. Influence of Mg\(^{2+}\) on CaCO\(_{3}\) precipitation during subsurface reactive transport in a homogeneous silicon-etched pore network. Geochim. Cosmochim. Acta 135, 321–335 (2014).
Yoon, H., Chojnicki, K. N. & Martinez, M. J. Pore-scale analysis of calcium carbonate precipitation and dissolution kinetics in a microfluidic device. Environ. Sci. Technol. 53, 14233–14242 (2019).
Deng, H., Fitts, J. P., Tappero, R. V., Kim, J. J. & Peters, C. A. Acid erosion of carbonate fractures and accessibility of arsenic-bearing minerals: In operando synchrotron-based microfluidic experiment. Environ. Sci. Technol. 54, 12502–12510 (2020).
Radajewski, D. et al. An innovative data processing method for studying nanoparticle formation in droplet microfluidics using x-rays scattering. Lab on a Chip 21, 4498–4506 (2021).
Poonoosamy, J. et al. Microfluidic investigation of pore-size dependency of barite nucleation. Commun. Chem. 6, 250 (2023).
Poonoosamy, J. et al. Microfluidic flow-through reactor and 3D raman imaging for in situ assessment of mineral reactivity in porous and fractured porous media. Lab Chip 20, 2562–2571 (2020).
Hou, Y., Chen, S., Zheng, Y., Zheng, X. & Lin, J.-M. Droplet-based digital pcr (ddpcr) and its applications. TrAC Trends in Anal. Chem. 158, 116897 (2023).
Chagot, L. et al. Surfactant-laden droplet size prediction in a flow-focusing microchannel: A data-driven approach. Lab Chip 22, 3848–3859 (2022).
Yiannacou, K., Sharma, V. & Sariola, V. Programmable droplet microfluidics based on machine learning and acoustic manipulation. Langmuir 38, 11557–11564 (2022).
Solanki, S. et al. Machine learning for predicting microfluidic droplet generation properties. Comput. Fluids 247, 105651 (2022).
Lashkaripour, A. et al. Machine learning enables design automation of microfluidic flow-focusing droplet generation. Nat. Commun. 12, 25 (2021).
Riti, J. et al. Combining deep learning and droplet microfluidics for rapid and label-free antimicrobial susceptibility testing of colistin. Biosensors and Bioelectron. 257, 116301 (2024).
Gardner, K. et al. Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets. Lab Chip 22, 4067–4080 (2022).
Dos Santos, E. C., Ładosz, A., Maggioni, G. M., von Rohr, P. R. & Mazzotti, M. Characterization of shapes and volumes of droplets generated in pdms t-junctions to study nucleation. Chem. Eng. Res. Design 138, 444–457 (2018).
Wehrmeister, U., Jacob, D., Soldati, A., Hager, T. & Hofmeister, W. Vaterite in freshwater cultured pearls from china and japan. J. Gemmol. 30, 399 (2007).
Darkins, R. et al. Calcite kinetics for spiral growth and two-dimensional nucleation. Crystal Growth & Design 22, 4431–4436 (2022).
Wang, Y.-W., Kim, Y.-Y., Stephens, C., Meldrum, F. & Christenson, H. In situ study of the precipitation and crystallization of amorphous calcium carbonate (ACC). Crystal Growth & Design 12, 1212–1217 (2012).
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, Part III 18, 234–241 (Springer, 2015).
Saini, M. & Susan, S. Tackling class imbalance in computer vision: A contemporary review. Art. Intell. Rev. 56, 1279–1335 (2023).
Zhong, Z. et al. Understanding imbalanced semantic segmentation through neural collapse. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 19550–19560 (2023).
Santoso, R., He, X. & Hoteit, H. Application of machine-learning to construct simulation models from high-resolution fractured formation. In Abu Dhabi International Petroleum Exhibition and Conference, D021S060R004 (SPE, 2019).
Bradski, G. & Kaehler, A. Learning OpenCV: Computer vision with the OpenCV library (“O’Reilly Media, Inc.”, 2008).
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
van der Walt, S. et al. scikit-image: Image processing in Python. PeerJ 2, e453 (2014).
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Whittaker, M. L., Dove, P. M. & Joester, D. Nucleation on surfaces and in confinement. MRS Bull. 41, 388–392 (2016).
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), 801–818 (2018).
Jülich Supercomputing Centre. JURECA: Data Centric and Booster Modules implementing the Modular Supercomputing Architecture at Jülich Supercomputing Centre. JLSRF 7, 1–9 (2021).
1 Comment
u9mrhd