A systematic study on the structural and optical properties of vertically aligned zinc oxide nanorods grown by high pressure assisted pulsed laser deposition technique

In this study, we synthesize high quality vertically aligned ZnO (VAZO) nanorods on silicon, sapphire, and indium tin oxide (ITO) substrates by using pulsed laser deposition (PLD) technique at high growth pressure (0.3 Torr). Systematic changes in structural and optical properties of VAZO nanorods are studied by varying the substrate temperature (500–600 °C) and number of pulsed laser shots during the deposition. ZnO nanoparticles deposited at high pressure act as nucleation sites, eliminating requirement of catalyst to fabricate VAZO nanorods. Two sharp ZnO peaks with high intensity correspond to the (0002) and (0004) planes in X-ray diffraction pattern confirm the growth of ZnO nanorods, oriented along the c-axis. Scanning Electron Microscopy (SEM) images indicate a regular arrangement of vertically aligned hexagonal closed pack nano-structures of ZnO. The vertical alignment of ZnO nanorods is also supported by the presence of E2 (high) and A1 (LO) modes in Raman spectra. We can tune the diameter of VAZO nanorods by changing growth temperature and annealing environments. Photoluminescence spectroscopy illustrates reduction in defect level peak intensities with increase in diameter of VAZO nanorods. This study signifies that high pressure PLD technique can be used more efficiently for controlled and efficient growth of VAZO nanorods on different substrates

Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. In this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies

Mechanistic Insight into the Attachment of Fullerene Derivatives on Crystal Faces of Methylammonium Lead Iodide Based Perovskites

Recent studies suggest that electron transport layers (ETLs) comprising [6,6]-phenyl-C61-butyric acid methyl ester (PCBM), employed in planar perovskite solar cells, reduce hysteresis by passivating the deep trap states, thereby underscoring the importance of interfacial structures. To gain physical insights into the PCBM–perovskite interfaces during solution processing, we performed molecular dynamics simulations of PCBMs solvated in chlorobenzene near (110) and (100) perovskite surfaces. Our results indicate strong orientational preferences of deposited PCBMs with the strongest associations between the carbonyl oxygen atom of PCBM and the terminating Pb and H atoms of (110) and (100) faces of perovskite, respectively. The phenyl moiety shows weak associations with the (100) perovskite surface enabling two-pronged anchoring that might facilitate charge transfer. In-plane ordering of PCBMs on perovskite surfaces indicates that a more densely packed monolayer is formed on the (110) surface compared to that on the (100) surface and might lead to more efficient electron transport

Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys

More than $270 billion is spent on combatting corrosion annually in the USA alone. As such, we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our focus is on a non-traditional class of alloys called multi-principal element alloys (MPEAs). Given the vast search space due to the variety of compositions and descriptors to be considered, and based upon existing corrosion data for MPEAs, we demonstrate descriptor optimization to predict corrosion resistance of any given MPEA. Our ML model with descriptor optimization predicts the corrosion resistance of a given MPEA in the presence of an aqueous environment by down selecting two environmental descriptors (pH of the medium and halide concentration), one chemical composition descriptor (atomic % of element with minimum reduction potential), and two atomic descriptors (difference in lattice constant (Δa) and average reduction potential). Our findings show that, while it is possible to down select corrosion-resistant MPEAs by using ML from a large search space, a larger dataset and higher quality data are needed to accurately predict the corrosion rate of MPEAs. This study shows both the promise and the perils of ML when applied to a complex chemical phenomenon like corrosion of alloys.This article is published as Roy, Ankit, M. F. N. Taufique, Hrishabh Khakurel, Ram Devanathan, Duane D. Johnson, and Ganesh Balasubramanian. "Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys." npj Materials Degradation 6, no. 1 (2022): 1-10. DOI: 10.1038/s41529-021-00208-y. Copyright 2022 Battelle Memorial Institute. Attribution 4.0 International (CC BY 4.0). Posted with permission. DOE Contract Number(s): AC05-76RL01830; AC02-07CH11358

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.This article is published as Khakurel, Hrishabh, M. F. N. Taufique, Ankit Roy, Ganesh Balasubramanian, Gaoyuan Ouyang, Jun Cui, Duane D. Johnson, and Ram Devanathan. "Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys." Scientific Reports 11, no. 1 (2021): 1-10. DOI: 10.1038/s41598-021-96507-0. Copyright 2021 Battelle Memorial Institute. Attribution 4.0 International (CC BY 4.0). Posted with permission. DOE Contract Number(s): AC02-07CH11358; AC05-76RL01830

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that na\"ive pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization

Simulation Studies to Quantify the Impacts of Point Defects: an Investigation of Cs2agbibr6 Perovskite Solar Devices Utilizing Zno and Cu2o As the Charge Transport Layers

In this investigation, we have applied SCAPS and wxAMPS to simulate defects and probe a photovoltaic device utilizing Cs2AgBiBr6 as the active photovoltaic layer and ZnO and Cu2O as the electron transport layer (ETL) and hole transport layer (HTL) respectively. At the Cs2AgBiBr6 bulk we find that with increasing defect density, each defect level has increasing impact on all device performance parameters. At a given defect density however, we find that that deeper defects have more profound impacts on Jsc and FF, and minimal effects on Voc. Specific to the Cs2AgBiBr6 structure, we have investigated VAg (shallow defect), VBi (deep defect) and Bri (quasi-deep defect). Our results provide insight into the growth conditions of Cs2AgBiBr6, with a need to have both Br-poor and Bi-rich conditions, and a preference for the latter over the former to suppress the deeper defect. Exploring the performance kinetics at the ZnO/Cs2AgBiBr6 and Cs2AgBiBr6/Cu2O interfaces due to defect type, location and density, we showcase a remarkably stable behavior in both Voc and Jsc across both interfaces. We attribute this to much higher charge mobilities in the ZnO and Cu2O compared to the Cs2AgBiBr6 layer combined with similar defect densities across the layers, leading to effective charge extraction and minimal charge recombination