Department of Metallurgical and Materials Engineering

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Quantitative treatment of microstructure data is the first step in establishing the structure–property linkages using materials informatics. However, the microstructure data are often huge and require dimensionality reduction techniques to use it in a computationally meaningful way. In this paper, we present a simple and unique approach to estimate the intrinsic dimensionality of microstructure data. By using principal component analysis (PCA) and multi-dimensional scaling (MDS), we demonstrate the effects of global and local metrics on various classes of 2D and 3D synthetic two-phase microstructure data on the intrinsic dimensionality (ID). Further, we establish the influence of the phase fraction and the inherent stochastic nature of the microstructure on ID estimation. It is observed that 2-point spatial correlation statistics greatly influence intrinsic dimensionality. A change in the intrinsic dimensionality is observed with an increase in the volume fraction of the phase. Considerable variation is observed in metric values for MDS compared to PCA, with an increase in dimensions. We also provide a reduced-order phase fraction benchmark of intrinsic dimensionality (ID) for high dimensional microstructure data. The presented framework is based on a simple and effective trade-off between property preservation and complexity reduction. © 2021, The Minerals, Metals & Materials Society.

World University root node

Integrating Materials and Manufacturing Innovation

Intrinsic Dimensionality of Microstructure Data

Crystal structure classification of perovskites (ABO3) is done using the Light Gradient Boosting Machine (Light GBM) algorithm. In this work, we have identified features such as electronegativity, ionic radius, valence, and bond lengths of A-O and B-O pairs that enable a priori crystal structure prediction. We have taken 5329 ABO3 perovskites and applied the proposed model to 675 compounds. It successfully categorized the compounds into cubic, tetragonal, orthorhombic, and rhombohedral structures with 80.3% best accuracy using 5-fold cross-validation. Therefore, the model can be used as a preliminary, fast, and inexpensive method to classify perovskites into their respective crystal systems. Feature importance graph and SHapley Additive exPlanations (SHAP) are used in feature ranking and crystal structure prediction. These composition-structure predictions will find applications in ceramic engineering and solid-state chemistry. © 2020 Elsevier B.V.

Computational Materials Science

Crystal structure classification in ABO3 perovskites via machine learning

Conventional water electrolyzers produce H2 and O2 simultaneously. The sluggish water oxidation reaction (WOR) results in low overall energy conversion efficiency; also the production of O2 is not of economic value. Thus, replacing sluggish anodic water oxidation reaction with 5-(Hydroxymethyl) furfural oxidation reaction (HmfOR) has been considered as a more energy-efficient strategy to produce hydrogen. Here, Ni3N incorporated by V2O3 is synthesized via thermal ammonolysis, which shows excellent bifunctional electrocatalytic performance for hydrogen evolution reaction (HER) and HmfOR. The electrocatalyst has strong tolerance to Hmf, which avoids the use of membrane. The Ni3N-V2O3 hybrids shows a low overpotential of 53 mV for HER, and a low overpotential of 230 mV for HmfOR, which is below 140 mV for WOR at 10 mA cm−2. When Ni3N-V2O3 is employed as a bifunctional electrode, it offers a cell voltage of only 1.40 V at 10 mA cm−2 for continuously hydrogen production. © 2021 Elsevier B.V.

Chemical Engineering Journal

Ni3N-V2O3 enables highly efficient 5-(Hydroxymethyl) furfural oxidation enabling membrane free hydrogen production

Spring-back of sheet metal affects the dimensional accuracy of the components after stamping and has to be eliminated or corrected. Advanced high strength steels (AHSS) are prone to spring-back while forming at ambient temperatures. Hot stamping process is an effective technique to reduce the spring-back upon forming. The forming temperatures and the phase transformations associated with quenching are known to influence the spring-back. However, the detailed experimental analysis on spring-back behaviour is limited due to the difficulty in physically simulating the thermal-mechanical conditions prevailing in a typical hot forming process. In the present work, a novel V-bending technique is presented for effectively measuring the spring-back using a thermal-mechanical simulator, Gleeble™. V-bending is carried out by applying a compressive force along the longitudinal plane of AHSS steel strips at forming temperatures. Upon V-bending, samples were cooled at different cooling rates while maintaining the forming loads to simulate die quenching. Results indicate that the spring-back decreases and becomes close to zero (at a cooling rate of 20 K s-1) with an increase in cooling rate for a hot stamping steel. Further, an increase in cooling rate results in spring-forward owing to the formation of an increased volume fraction of martensite. © 2021 The Authors.

Fulltext

Journal of Materials Research and Technology

A novel method for the spring-back analysis of a hot stamping steel

The flow stress reduction during plastic deformation superposed with electric current, commonly referred as 'electroplasticity' has been actively researched over the past few decades. While the existence of an electron-dislocation interaction, independent of Joule heating is established, the exact rate controlling mechanism of the observed behaviour lacks consensus. Understanding the governing mechanism is complex due to the combined effect of Joule heating and electron-dislocation interaction. The present work attempts to establish the electroplastic mechanism in AA 6063 alloy and its nanocomposites. The role of microstructure on the electron interaction is investigated by preparing four distinct microstructure from the base alloy. All the samples were subjected to constant amplitude direct current during plastic deformation. The Joule heating effect is decoupled using the experimentally measured temperature history. The potential electroplastic mechanism for the alloy is elucidated by analysing the trend of flow stress reduction with strain and strain rate. It is inferred that micro Joule heating and electron wind effect cannot completely explain the observed electroplastic behaviour in AA 6063. The SiC particles in nano-composites suppressed the electroplastic effect. The observed mechanical behaviour under electric current is in agreement with the trend predicted assuming magnetic depinning mechanism. The reduction of dislocation density quantified using X-ray diffraction is found to concur with the inferred mechanism. © 2021 The Author(s).

Department of Mechanical Engineering

Microstructure dependent electroplastic effect in AA 6063 alloy and its nanocomposites

The dielectric behavior of spark plasma sintered SiC(O)/HfCxN1-x nanocomposites synthesized through polymer derived ceramic route was investigated in the frequency range of 1 kHz to 1 MHz at room temperature. The nanostructural features revealed HfCxN1-x nanocrystals encapsulated in a nanometric thin layer of carbon dispersed uniformly in a SiC(O) matrix with segregated free carbon. The nanocomposites exhibited colossal permittivity values in the order of 103 at 1 kHz which reduced to 646 at 1 MHz. The interfacial polarization mechanism existing between complex nanostructural interfaces and the percolation of HfCxN1-x nanocrystals are believed to be responsible for the high permittivity values observed in the measured frequency range. The AC conductivity exemplified a frequency independent behavior at lower frequencies while at higher frequencies, the conductivity exhibited frequency dependence, indicating the existence of hopping type mechanism. © 2020 Elsevier B.V.

Materials Chemistry and Physics

Low frequency dielectric behavior and AC conductivity of polymer derived SiC(O)/HfCxN1-x ceramic nanocomposites

TiNb2O7/carbon nanocomposites synthesized through a simple, surfactant assisted precursor route is reported as a promising alternative anode material for lithium-ion batteries (LIBs). The carbon component of the nanocomposites is derived from an inexpensive and sustainable keratin rich biological source. The reinforcement of carbon in TiNb2O7 facilitated the formation of non-stoichiometric (Ti0.712Nb0.288)O2 crystalline phase, in addition to the stoichiometric TiNb2O7 phase. It also yielded a high specific surface area ( 90 ​m2 ​g−1) and reduced crystallite size ( 4 ​nm). Electrochemical results exemplified high reversible capacity of 356 mAh g−1 at 0.1 ​C and remarkable rate capability of 26 mAh g−1 at ultra-high current rate of 32C. TiNb2O7/carbon nanocomposites also demonstrated remarkable cyclic stability with large capacity retention of 85% even after 50 cycles at 1 ​C. The experimental data attests the potential of TiNb2O7/keratin derived carbon nanocomposites as economically and environmentally viable promising anode material for LIBs. © 2021 The Authors

Open Ceramics

TiNb2O7-Keratin derived carbon nanocomposites as novel anode materials for high-capacity lithium-ion batteries

Hydroxyapatite ((Ca10(PO4)6(OH)2, HAp) based chromatography matrix has attracted great interest in the field of protein separation. However, researchers have been trying to combat the growing costs associated with the HAp matrix. In the present investigation, we utilized a cheap biological waste material, viz. eggshells, for the development of hydroxyapatite (HAp) resins and evaluated them for protein purification. Initially, the calcite of the eggshell carbonate was converted into metastable vaterite microspheres. The HAp microspheres (ECHAp) were then prepared from eggshell carbonate microspheres using a novel dissolution-precipitation process. Synthetic source calcium carbonate was also used to prepare HAp microspheres (CHAp) for comparison. The purity and morphology of the apatite microspheres were characterized using X-ray diffraction (XRD) method, Fourier Transform Infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and laser diffraction particle analysis. Although both the apatites have similar morphology, the ECHAp has a larger surface area of 33.8 m2 g−1 compared to CHAp of 17.27 m2 g−1 by surface area analysis method. A commercial HAp matrix (CHT) with similar properties was also studied for comparison. All the apatite microspheres were found to have a similar protein binding capacity for bovine serum albumin (BSA). But ECHAp showed better protein separation for BSA and lysozyme mixture compared to CHAp and CHT matrices. The ECHAp matrix was also found to be highly stable over 20 purification cycles. Hence, the eggshell waste seems to have the potential for HAp matrix by a novel carbonate route with ease of preparation and also an economical packing material for chromatographic purification of biomolecules. © 2021 Elsevier Ltd and Techna Group S.r.l.

Department Of Biotechnology

Ceramics International

Eggshell derived hydroxyapatite microspheres for chromatographic applications by a novel dissolution - precipitation method

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications. © 2021, International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature.