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2. Material Hardness Descriptor Derived by Symbolic Regression

Author

Tantardini, Christian, Zakaryan, Hayk A., Han, Zhong-Kang, Altalhi, Tariq, Levchenko, Sergey V., Kvashnin, Alexander G., and Yakobson, Boris I.

Subjects
Condensed Matter - Materials Science, Physics - Applied Physics, Physics - Computational Physics
Abstract

Hardness is a materials' property with implications in several industrial fields, including oil and gas, manufacturing, and others. However, the relationship between this macroscale property and atomic (i.e., microscale) properties is unknown and in the last decade several models have unsuccessfully tried to correlate them in a wide range of chemical space. The understanding of such relationship is of fundamental importance for discovery of harder materials with specific characteristics to be employed in a wide range of fields. In this work, we have found a physical descriptor for Vickers hardness using a symbolic-regression artificial-intelligence approach based on compressed sensing. SISSO (Sure Independence Screening plus Sparsifying Operator) is an artificial-intelligence algorithm used for discovering simple and interpretable predictive models. It performs feature selection from up to billions of candidates obtained from several primary features by applying a set of mathematical operators. The resulting sparse SISSO model accurately describes the target property (i.e., Vickers hardness) with minimal complexity. We have considered the experimental values of hardness for binary, ternary, and quaternary transition-metal borides, carbides, nitrides, carbonitrides, carboborides, and boronitrides of 61 materials, on which the fitting was performed. The found descriptor is a non-linear function of the microscopic properties, with the most significant contribution being from a combination of Voigt-averaged bulk modulus, Poisson's ratio, and Reuss-averaged shear modulus. Results of high-throughput screening of 635 candidate materials using the found descriptor suggest the enhancement of material's hardness through mixing with harder yet metastable structures (e.g., metastable VN, TaN, ReN$_2$, Cr$_3$N$_4$, and ZrB$_6$ all exhibit high hardness).

Published
2023
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33. Preparation of Au/α‐Fe2O3 Electrode for the Detection of Dopamine.

Author

Guanling, Liu, Wu, Ren‐Jang, Fegade, Umesh, Patil, Nilima, Jethave, Ganesh, and Altalhi, Tariq

Subjects
VITAMIN C, CYCLIC voltammetry, DETECTION limit, FERRIC oxide, URIC acid
Abstract

In this study, Au/α‐Fe2O3 sensing material was synthesized and used as the working electrode for dopamine sensing. The Au/α‐Fe2O3 composites were prepared with various loadings (0.5 %, 1 %, and 3 %) to enhance their sensing properties. All three composites were tested for dopamine sensing, with 1 % Au/α‐Fe2O3 showing the best response. The Au/α‐Fe2O3 composites were characterized using XRD, TEM, and SEM to confirm successful doping of Au on the iron oxide surface. The CV plot obtained by plotting Cyclic Voltammetry (CV) vs. dopamine (DA) was linear (R2=0.9872) for DA concentrations ranging from 6.25 μM–200 μM on 1 % Au/α‐Fe2O3. Similarly, the DPV plot between DPV vs. DA was linear (R2=0.9895) for the same DA concentration range on 1 % Au/α‐Fe2O3, with a detection limit of 0.19 μM. Interference and stability experiments were conducted with dopamine (DA), ascorbic acid (AA), and uric acid (UA). The results showed that DA sensing was not affected by the other two substances for up to 14 days. The signal results indicated that the current intensity remained at 90.75 % of the original, proving that this material has good stability. [ABSTRACT FROM AUTHOR]

Published
2024
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34. ZnMn2O4‐BiVO4 as Photocatalyst for Degradation of Gaseous HCHO Using Daylight.

Author

Wua, Peiying, Attarde, Sanjay, Lu, Chia‐Chen, Wu, Ren‐Jang, Fegade, Umesh, Kolate, Sachin, Patil, Nilima, and Altalhi, Tariq

Subjects
MANGANESE catalysts, CARBON dioxide in water, TITANIUM oxides, LIGHT sources, ZINC compounds, ZINC catalysts
Abstract

Formaldehyde (HCHO) is a common indoor air pollutant. Due to its proven carcinogenicity, reducing indoor HCHO levels is a significant issue. In this study, a composite material consisting of zinc manganese oxide and BiVO4 was used as a photocatalyst for HCHO degradation. ZnMn2O4‐BiVO4 has efficient photocatalytic performance, wide application areas, high stability, and low‐cost advantages, making the substance a potentially useful photocatalyst that can be applied in fields such as energy conversion and environmental protection. BiVO4 was prepared by a hydrothermal method. It was further compounded with zinc manganese oxide to form composite as catalysts of different wt % ratios for photodegradation of HCHO using a fluorescent lamp as light source. The samples were characterized by using XRD, UV‐visible spectrometer, TEM, SEM, and photoluminescence spectrometry. The experimental data showed that 5.0 wt % zinc manganese oxide‐BiVO4 as catalyst had the highest HCHO degradation rate. The release of carbon dioxide and water increased the irradiation time. The degradation rate of HCHO was higher than that achieved with commercially available titanium oxide. The highest HCHO degradation rate was 66 % within 4 h. [ABSTRACT FROM AUTHOR]

Published
2024
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