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n this review article, a comprehensive meta-analysis based on available literature information has been undertaken to make a relative comparison of total arsenic in rice grain. This involves analyzing the findings of various peer-reviewed studies that examined arsenic-contaminated Asian regions. Also, this article highlights the regional-level human health risks caused by the consumption of arsenic-contaminated rice in the three regions of Asia. Deriving such information at the continental level is of major importance in view of the need for proper monitoring and alleviating serious and continually emerging human health issues in arsenic-contaminated areas. One aim of this paper is to highlight the potential of a viable modeling approach for appraising the danger posed by arsenic in soil-plant-human system. There is an urgent need to fix the safe limit of bioavailable arsenic in soil because total arsenic in soil is not a good index of the arsenic hazard. Our hypothesis is finding out whether the modeling approach can be used in establishing a safe limit of bioavailable arsenic in soils with reference to human health. To achieve the above-mentioned objectives, we have selected reported rice grain arsenic content data from Asian countries following the PRISMA guidelines. Carcinogenic and non-carcinogenic risk was calculated following the US EPA’s guidelines. It emerged that adults in Asian countries are prone to a high risk of cancer due to their consumption of arsenic-contaminated rice. South Asia (SA), South East Asia (SEA), and East Asia (EA) exceeded the US EPA-prescribed safe limit for cancer risk with ~ 100 times higher probability of cancer due to rice consumption. The hazard quotient for the ingestion of arsenic containing rice was 4.526 ± 5.118 for SA, 2.599 ± 0.801 for SEA, and 2.954 ± 2.088 for EA. These figures are all above the permissible limit of HQ of 1. The solubility free ion activity model can predict arsenic transfer from soil to rice grain based on easily measurable soil properties and be used to fix the safe limit of bioavailable arsenic in paddy soils. The methods and findings of this review are expected to be useful for regional-level policymaking and mobilizing resources to alleviate public health issues caused by arsenic. 

The phase-field method is an attractive computational tool for simulating microstructural evolution during phase separation, including solidification and spinodal decomposition. However, the high computational cost associated with solving phase-field equations currently limits our ability to comprehend phase transformations. This article reports a novel phase-field emulator based on the tensor decomposition of the evolving microstructures and their corresponding two-point correlation functions to predict microstructural evolution at arbitrarily small time scales that are otherwise nontrivial to achieve using traditional phase-field approaches. The reported technique is based on obtaining a low-dimensional representation of the microstructures via tensor decomposition, and subsequently, predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once we obtain the microstructure prediction in the low-dimensional space, we employ a hybrid input–output phase-retrieval algorithm to reconstruct the microstructures. As proof of concept, we present the results on microstructure prediction for spinodal decomposition, although the method itself is agnostic of the material parameters. Results show that we are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of 6.78×10^−7) at time scales half of that employed in obtaining training data. Our data-driven microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity. 

Abstract Self-assembly by spinodal decomposition is known to be a viable route for synthesizing nanoscaled interfaces in a variety of materials, including metamaterials. In order to tune the response of these specialized materials to external stimuli, knowledge of processing-nanostructure correlations is required. Such an understanding is more challenging to obtain purely by experimental means due to complexity of multicomponent atomic diffusion mechanisms that govern the nanostructural self-assembly. In this work, we introduce a phase-field modeling approach which is capable of simulating the nanostructural evolution in ternary alloy films that are typically synthesized using physical vapor deposition. Based on an extensive parametric study, we analyze the role of the deposition rate and alloy composition on the nanostructural self-assembly in ternary alloy films. The simulated nanostructures are categorized on the basis of nanostructured morphology and mapped over a compositional space to correlate the processing conditions with the film nanostructures. The morphology maps reveal that while deposition rate governs the nanostructural evolution at around equi-molar compositions, the impact of composition on nanostructuring is more pronounced when the atomic ratios of alloying elements are skewed.