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While the fabrication of graded materials by directed energy deposition (DED) has led to accelerated materials discovery, the ability to rapidly explore sufficiently large material composition spaces is limited due to the time-intensive nature of conventional materials characterization techniques. The present study investigates the viability of small punch test (SPT) protocols for rapidly evaluating DED-fabricated alloy mixtures of stainless steel 316L (316L) and Inconel 625 (IN625). The SPT protocols evaluated in this study include both the recently established two-step Bayesian estimation framework as well as the empirical relationships established in prior literature. It is shown that these protocols are capable of reliably and quantitatively tracking the changes in the mechanical properties of the alloy mixtures studied. Enhancement of mechanical properties was observed with the addition of IN625 to 316L, which is attributed to the austenite stabilization in the matrix and the formation of fine δ - Ni3Nb precipitates. It is shown that CALPHAD-based Scheil model simulations predicted the formation of different precipitate phases for each composition. The novel protocols presented in this paper open new avenues for high throughput material explorations for additive manufacturing.

Covid-19 has posed a serious threat to the existence of the human race. Early detection of the virus is vital to effectively containing the virus and treating the patients. Profound testing methods such as the Real-time reverse transcription-polymerase chain reaction (RT-PCR) test and the Rapid Antigen Test (RAT) are being used for detection, but they have their limitations. The need for early detection has led researchers to explore other testing techniques. Deep Neural Network (DNN) models have shown high potential in medical image classification and various models have been built by researchers which exhibit high accuracy for the task of Covid-19 detection using chest X-ray images. However, it is proven that DNNs are inherently susceptible to adversarial inputs, which can compromise the results of the models. In this paper, the adversarial robustness of such Covid-19 classifiers is evaluated by performing common adversarial attacks, which include the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Using these attacks, it is found that the accuracy of the models for Covid-19 samples decreases drastically. In the medical domain, adversarial training is the most widely explored technique to defend against adversarial attacks. However, using this technique requires replacing the original model and retraining it by including adversarial samples. Another defensive technique, High-Level Representation Guided Denoiser (HGD), overcomes this limitation by employing an adversarial filter which is also transferable across models. Moreover, the HGD architecture, being suitable for high-resolution images, makes it a good candidate for medical image applications. In this paper, the HGD architecture has been evaluated as a potential defensive technique for the task of medical image analysis. Experiments carried out show an increased accuracy of up to 82% in the white box setting. However, in the black box setting, the defense completely fails to defend against adversarial samples.

Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

Recent works have demonstrated the viability of convolutional neural networks (CNN) for capturing the highly non-linear microstructure-property linkages in high contrast composite material systems. In this work, we develop a new CNN architecture that utilizes a drastically reduced number of trainable parameters for building these linkages, compared to the benchmarks in current literature. This is accomplished by creating CNN architectures that completely avoid the use of fully connected layers, while using the 2-point spatial correlations of the microstructure as the input to the CNN. In addition to increased robustness (because of the much smaller number of trainable parameters), the CNN models developed in this work facilitate the construction of property closures at very low computational cost. This is because it allows for easy exploration of the space of valid 2-point spatial correlations, which is known to be a convex hull. Consequently, one can generate new sets of valid 2-point spatial correlations from previously available valid sets of 2-point spatial correlations, simply as convex combinations. This work demonstrates the significant benefits of utilizing 2-point spatial correlations as the input to the CNN, in place of the voxelated discrete microstructures used in current benchmarks.

Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.

A series of small molecules containing aminopropyltriethoxysilyl-substituted imines and amides were synthesized so that they could potentially be incorporated into self-assembled monolayers (SAMs) on metal oxide surfaces. Simple one-step imine preparations and two-step amide preparations are reported here.

Context: Fine needle aspiration (FNA) plays a crucial role in the evaluation of patients with thyroid lesions. The Bethesda system for reporting thyroid cytopathology (TBSRTC) was designed with a mission to standardize the process of diagnosis and management of thyroid lesions by FNA cytology (FNAC). Aim: We aim to see the benefits of adopting TBSRTC, seek the cytological pitfalls in the diagnosis of thyroid FNAC, and identify the spectrum of thyroid lesions in our setup. Settings and Design: This is a hospital-based cross-sectional study conducted from June 2009 to June 2014 of all thyroid FNACs with available histopathology reports. Cases were designated a specific diagnostic category according to TBSRTC. Materials and Methods: A total of 109 cases were included in the study. Sixty-eight cases had been reported without using TBSRTC and were reviewed and reclassified according to TBSRTC seeking the common reasons for interpretative errors. Statistical Analysis Used: Data were analyzed using SPSS ver. 11.5. Results: In both pre- and post-TBSRTC era, benign neoplasms constituted the major bulk. After the use of TBSRTC, there was increased ability to look for follicular neoplasms, improvement in making definitive diagnosis of the cases, decline in the suspicious category, and an improvement in diagnostic accuracy, and we were in line with the implied risk outlined by TBSRTC in most of the cases except the nondiagnostic or unsatisfactory category. Conclusion: Application of TBSRTC results in uniformity in reporting among pathologists and better interdisciplinary communication and patient management.

Recent investigations have highlighted the multi-resolution and high throughput characteristics of the spherical indentation experimental and analysis protocols. In the present work, we further demonstrate the capabilities of these protocols for reliably extracting indentation stress-strain (ISS) responses from the microscale constituents as well as the bulk scale of dual phase materials exhibiting bimodal microstructures. Specifically, we focus on bimodal microstructures produced in an α–β Ti6242 sample. Combining the multi-resolution indentation responses with microstructural statistics gathered from the segmentation of back-scattered electron images from the scanning electron microscope allowed for a critical experimental evaluation of the commonly utilized Rule of Mixtures based composite model for the elastic stiffness and plastic yield strength of the sample. The indentation and image analyses protocols described in this paper offer novel research avenues for the systematic development and critical experimental validation of composite material models.

A series of small molecules containing polar aromatic substituents and alkynes have been synthesized. One–pot preparations of polar aromatic molecules containing an alkynyl imine and alkynyl amide are reported. A one-pot preparation of a catechol containing an alkynyl amine was also attempted but in our hands it proved much better to synthesize this target molecule via a three step synthesis which we also report here.

This paper explores the viability and benefits of building a computationally low-cost surrogate model relating the mechanical performance of micro-springs to crystallographic texture and single-crystal elastic constants. This task is accomplished by leveraging advances in (i) computationally-expensive finite element (FE) models that explicitly incorporates the constitutive response of individual grains in to simulate the overall mechanical response of the micro-spring, (ii) efficient parametrization of the extremely large space of textures using Fourier basis (i.e., generalized spherical harmonics), (iii) sequential design of FE simulations to maximize model fidelity while also minimizing the overall computational expense incurred in generating the data, and (iv) the use of Gaussian process models for incorporating uncertainty quantification into the development of the desired surrogate model. The strategy implemented in this work proved to be remarkably efficient in producing the desired surrogate model. The utility of the surrogate model established in this work is demonstrated with a case study addressing an inverse solution for the specific crystallographic texture that maximizes the micro-spring performance for a selected material.

This paper presents a generalized framework for the digital generation of composite microstructures using filter-based approaches that can devise and utilize a wide variety of cost functions reflecting the desired targets on geometrical and statistical measures. The use of filter-based approaches leads to remarkable computational advantages compared to the conventional approaches used currently for microstructure generation. The framework provides a highly modular and flexible approach to generate stochastic ensembles of microstructures meeting user-defined microstructural characteristics. The proposed framework is illustrated in this paper through selected case studies.

Abstract We discuss and demonstrate the application of recently developed spherical nanoindentation stress-strain protocols in characterizing the mechanical behavior of tungsten polycrystalline samples with ion-irradiated surfaces. It is demonstrated that a simple variation of the indenter size (radius) can provide valuable insights into heterogeneous characteristics of the radiation-induced-damage zone. We have also studied the effect of irradiation for the different grain orientations in the same sample.

This paper presents a two-step Bayesian framework for the estimation of the intrinsic single crystal elastic stiffness parameters from the measurements of spherical indentation stress-strain responses in multiple individual grains of a polycrystalline sample, whose crystal lattice orientations have been measured using electron back-scattered diffraction technique. The first step requires the establishment of the functional dependence of the indentation elastic modulus given the lattice orientation and the intrinsic single crystal elastic stiffness parameters. Previous efforts for this step required a large database of computationally expensive finite element (FE) simulations in order to establish this function with adequate accuracy. In this paper, it is shown that the introduction of a Bayesian framework can greatly reduce the number of simulations necessary to establish this function, while introducing practically useful measures of uncertainty which can guide the selection of specific additional simulations that are expected to best improve the predictive accuracy of the function. The second step involves a Markov Chain Monte-Carlo (MCMC) sampling of the distribution of possible values for the single crystal elastic stiffness parameters based on a given set of experimentally measured elastic indentation moduli in individual grains of different lattice orientations. This second step is accomplished by calibrating the available experimental data to the function established in the first step. This novel framework is presented and demonstrated in this paper for an as-cast cubic polycrystalline Fe-3% Si sample and a hexagonal polycrystalline commercially pure (CP-Ti) titanium sample.

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

Standardized mechanical tests have become one of the central bottlenecks in the efficient and cost-effective exploration of the process space in advanced manufacturing processes such as additive manufacturing (AM). This paper presents a critical comparison of the relative advantages and disadvantages between emergent high-throughput mechanical test protocols, specifically, spherical microindentation and small punch test, and standardized tension tests. The critical comparison considers the effectiveness and viability of the testing protocols to rapidly screen stress-strain data and mechanical properties of candidate AM-processed metal specimens. These comparisons were performed on samples of Inconel 718 produced by selective laser melting (SLM). Modulus, yield strength, and ultimate tensile strength were evaluated, and the combination of high-throughput mechanical test protocols displayed results consistent with standard tension tests. This study shows that high-throughput mechanical test protocols can successfully produce reliable stress-strain data using significantly smaller material volume and reduced labor compared to the standardized tension tests.

Compositionally graded cylinders of Ti–Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of α–β microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress–strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17–1.78 μm, and beta content in the range of 20–83 vol.%. The estimated values of the Young’s moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97–130 GPa and 828–1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables.

Introduction: Polycystic Ovarian Disease (PCOD) is the most common hormonal disorder in women. PCOD is associated with an increase in subclinical atherosclerotic disease and endothelial dysfunction. The altered endothelial function and early endothelial damage can be assessed by Urinary Albumin Excretion (UAE), a marker of an atherogenic background. Dyslipidaemia is a very common metabolic abnormality in women with PCOD due to elevated androgen level and insulin resistance. As evidenced from previous studies PCOD patients are at an increased cardiovascular risk compared with the age matched controls. Aim: To evaluate the prevalence of microalbuminuria and dyslipidaemia in premenopausal PCOD patients compared to normal premenopausal women. Materials and Methods: The present study was carried out at Department of Biochemistry, Aarupadai Veedu Medical College and Hospital Puducherry, India. A total of 40 diagnosed PCOD patients (according to Rotterdam criteria) of premenopausal age (21 to 42 years) and 40 age and sex matched controls (22 to 43 years) without PCOD were included in the study. All subjects had undergone measurement of height, weight and Blood Pressure (BP), and detailed systemic examination. Fasting plasma glucose, serum cholesterol, Triglycerides (TG), and High Density Lipoprotein-Cholesterol (HDLc) were estimated by using commercially available kits in automated Chemistry Analyser (ChemWell). Low Density Lipoprotein-Cholesterol (LDLc) and Very Low Density Lipoprotein-Cholesterol (VLDLc) were calculated by Friedwald’s equation. Urine microalbumin was estimated by Latex turbidimetry method using semi Autoanalyser BIOTRON BTR830. Urine creatinine was estimated by commercial kit in Autoanalyser. Albumin-Creatinine Ratio (ACR) value more than 30 mg/g was taken as microalbuminuria positive. Student’s t-test and SPSS version 16.0 were used for statistical analysis. Results: Out of 40 PCOD patients 21 patients were having microalbuminuria and out of 40 controls only two were having microalbuminuria. Routine biochemical investigations registered a significant rise of fasting plasma glucose, TG, TC, LDLc and VLDLc in PCOD patients, incomparison with controls (p=0.02 for LDLc and p