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Barium sulphate, a chemical compound with the formula BaSO₄, is widely recognized for its numerous applications in various industries, particularly in the field of medicine, paints, plastics, and as a component in drilling fluids. One of the distinguishing features of barium sulphate is its striking physical property its color. Understanding the color of barium sulphate not only helps in identifying the compound during handling but also plays a significant role in its applications and quality assessment.


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Moreover, TiO2 can also improve the mechanical properties of plastics. It increases the stiffness and strength of the material, making it more resistant to impact and deformation. This is particularly beneficial in applications where plastics are subjected to stress or pressure, such as automotive parts and construction materials. By incorporating TiO2 into their formulations, manufacturers can create stronger and more durable plastic products without sacrificing their lightweight nature.

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In conclusion, the wholesale classification of calcium carbonate is an important aspect of the calcium carbonate industry that helps categorize and differentiate the different grades of calcium carbonate based on their quality and intended use. Whether it is for pharmaceutical, food, industrial, or agricultural applications, there is a suitable grade of calcium carbonate available to meet the specific requirements of each industry. This classification system ensures that the right grade of calcium carbonate is used for the right application, ultimately leading to better quality products and improved performance.

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The realization of neuromorphic resistive memory in TiO2 thin films (Strukov et al., 2008) marked an important milestone in the search for bio-inspired technologies (Chua and Kang, 1976). Many research proposals urged a focus on memristivity as the common feature of two electrical models: (i) electromigration of point defects in titanium oxide systems (Baiatu et al., 1990; Jameson et al., 2007) and (ii) voltage-gated ionic channels in the membranes of biological neurons (Hodgkin and Huxley, 1952). In this regard, memristors functionally mimic the synaptic plasticity of biological neurons, and thus can be implemented in artificial and hybrid neural networks. This includes a new paradigm of future computing systems (Zidan, 2018) and biocompatible electronics such as biointerfaces and biohybrid systems (Chiolerio et al., 2017).

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