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Pilania, Ghanshyam

Publications and source records attributed to Pilania, Ghanshyam.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Advanced Coating Compositions and Microstructures to Improve Uptime and Operational Flexibility in Cyclic, Low-Load Thermal Utility Plants

GE, the University of Tennessee, and Oak Ridge National Laboratory collaborated from 2020 to 2023 developing two key technologies for improving the viability of fuel switching and load following in thermal utility plants: a) cost-effective weld overlay compositions for boiler tubing b) cathodic arc coatings that deliver improvements in both erosion resistance and oxidation resistance in high temperature steam for HP turbine blades The team worked through a robust, logical project map to de-risk these two technologies and advance them from TRL 3 to TRL 6. For the cost-effective weld overlay, the team developed a ferritic filler material which was fabricated at a vendor for 18% the average market cost of Inconel 625 wire, had a corrosion rate 3x lower in conditions simulating a biomass-fired superheater and 10x lower in conditions simulating a coal-fired superheater, and was fabricated into prototype overlaid tubing that passed ASME requirements including transverse bending, dye penetrant inspection, and ASTM G-76 evaluation. For the cathodic arc coatings applied to steam turbine blades, the team developed a novel composition that was successfully transferred to a qualified vendor. The vendor was able to produce coated prototypes with 4x the as-deposited erosion resistance and 10.4x the post-steam-exposure erosion resistance of the TiN coating the vendor currently applies on GE steam turbine components, without significantly increasing process cost. These coated prototypes also passed a GE inspection and showed favorable performance in high temperature erosion, nanoindentation, sliding wear, scratch adhesion, and high cycle fatigue testing. If successfully deployed by GE, it is anticipated that the technologies will enable the following: • 25%-50% increase in time between outages for both boilers and HP turbines. • 50% decrease in cost for weld overlay on a per foot basis relative to todays NiCr alloys. • Adequate oxidation resistance and erosion for HP turbine inlet steam at >620°C and >220 bar. • No need for changes in component supply chain or any notable Capital Expenditures. 5 Decreasing component cost, increasing performance, and extending time between outages represent direct value propositions to GE and their customers. For the American consumer, these objectives translate into increased grid reliability (fewer unexpected outages), decreased Levelized Cost of Electricity, and improved environmental health (low-loading/load following to accelerate penetration of renewables). The results also have implications for wear resistant tooling, wire arc additive manufacturing, more durable components for syngas cleanup, and deployment of more efficient thermochemical pathways for carbon negative fuel production

09 BIOMASS FUELS↗
DOE OSTI2023

Biodegradation Studies of Polyhydroxybutyrate and Polyhydroxybutyrate- co -Polyhydroxyvalerate Films in Soil

Due to increased environmental pressures, significant research has focused on finding suitable biodegradable plastics to replace ubiquitous petrochemical-derived polymers. Polyhydroxyalkanoates (PHAs) are a class of polymers that can be synthesized by microorganisms and are biodegradable, making them suitable candidates. The present study looks at the degradation properties of two PHA polymers: polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-polyhydroxyvalerate (PHBV; 8 wt.% valerate), in two different soil conditions: soil fully saturated with water (100% relative humidity, RH) and soil with 40% RH. The degradation was evaluated by observing the changes in appearance, chemical signatures, mechanical properties, and molecular weight of samples. Both PHB and PHBV were degraded completely after two weeks in 100% RH soil conditions and showed significant reductions in mechanical properties after just three days. The samples in 40% RH soil, however, showed minimal changes in mechanical properties, melting temperatures/crystallinity, and molecular weight over six weeks. By observing the degradation behavior for different soil conditions, these results can pave the way for identifying situations where the current use of plastics can be replaced with biodegradable alternatives.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Here, we develop a computational database, website applications (web-apps), and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D) heterostructures. Using density functional theory (DFT) based lattice parameters and electronic band energies for 674 nonmetallic exfoliable 2D materials, we generate 226 779 possible bilayer heterostructures. We classify these heterostructures into type-I, -II, and -III systems according to Anderson’s rule, which is based on the relative band alignments of the noninteracting monolayers. We find that type II is the most common and type III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the Periodic Table of constituent elements. The band alignment data can also be used for identifying photocatalysts and high-work-function 2D metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS 2 /WSe 2 , MoS 2 /h-BN, and MoSe 2 /CrI 3 ), to compare the band-alignment description with the predictions from Anderson’s rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools, and associated data will be distributed through the JARVIS-HETEROSTRUCTURE website. Our analysis, results, and the developed web-apps can be applied to the screening and design applications, such as finding alternative photocatalysts, photodetectors, and high-work-function (WF) 2D-metal contacts.

2-dimensional systems↗
DOE OSTI2022

Metal Oxynitrides for the Electrocatalytic Reduction of Nitrogen to Ammonia

The successful deployment of technologies for the electrocatalytic nitrogen reduction reaction (e-NRR) to synthesize ammonia would enable distributed ammonia production with lower greenhouse gas emissions compared to the Haber–Bosch process. However, electrocatalysts that can readily activate N 2 , promote selective ammonia formation over the competing hydrogen evolution reaction, and maintain stability under reaction conditions are needed to enable this technology. Herein, we give our perspective on metal oxynitrides (A x B y O w N z ) as an emerging and underexplored materials class for e-NRR. Furthermore, we contrast the activity, selectivity, and stability of metal oxynitrides with those of their metal nitride and metal oxide counterparts. We discuss the different possible e-NRR reaction mechanisms on metal oxynitrides, emphasize challenges related to using metal oxynitrides for e-NRR, and provide an outlook for future research. Ultimately, the huge design space of metal oxynitrides is ripe for exploration to find catalyst formulations that overcome some of the limitations of traditional metal oxides and metal nitrides for e-NRR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of lattice strain on magnetism in epitaxial YCrO 3 films

The influence of lattice strain on magnetism is studied for perovskite rare-earth chromite, YCrO 3 (YCO) thin films. A combined theoretical and experimental approach is used to demonstrate the correlation between lattice-strain and magnetic properties of YCO films. The first-principles calculations reveal that the local Cr magnetic moments and the out-of-plane magnetization linearly increase with changing the in-plane lattice strain from compressive to tensile. The experimental results agree with the theory findings showing a strong dependence of magnetization on epitaxial strain in YCO films. These results unveil the lattice-strain-controlled functionalities on perovskite rare-earth chromites and related material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Strong Zeeman splitting in orbital-hybridized valleytronic interfaces

We report stacked heterointerfaces of two-dimensional (2D) materials are well-suited to obtain novel electronic functionality at small length scales, because the intrinsic properties of both materials can be blended by interlayer coupling with remarkable effects on the electronic band structure. In particular, band degeneracy at the K and K' Brillouin zone valleys of 2D materials can be broken by symmetry reductions by applying an external magnetic field, with possible application in valleytronic devices. Nevertheless, this usually requires impractically large external magnetic fields > 100 T. An alternative and more practical route is provided via stacking a non-centrosymmetric layered material on a magnetic substrate to induce valley splitting through the magnetic proximity effect; however, the splitting magnitude can vary sharply between similar interfaces. In this work, first-principle calculations are used to calculate the valley splitting of eight low-strain interfaces with transition metal dichalcogenides stacked on 2D magnetic substrates. It is shown that the interlayer band hybridization plays a major role when the two layers’ bands are closely aligned in energy, with the resulting band repulsion and the total valley splitting being strongly dependent on the Hubbard U correction , showing up to an order of magnitude change. The WSe 2 /CrGeTe 3 interface in particular shows strong interlayer interactions, which can potentially lead to a significantly high valley splitting of 26 meV; however further experimental investigations will be needed to resolve uncertainties in the predicted valley splitting.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strong Purcell enhancement at telecom wavelengths afforded by spinel Fe 3 O 4 nanocrystals with size-tunable plasmonic properties

Developments in the field of nanoplasmonics have the potential to advance applications from information processing and telecommunications to light-based sensing. Traditionally, nanoscale noble metals such as gold and silver have been used to achieve the targeted enhancements in light-matter interactions that result from the presence of localized surface plasmons (LSPs). However, interest has recently shifted to intrinsically doped semiconductor nanocrystals (NCs) for their ability to display LSP resonances (LSPRs) over a much broader spectral range, including the infrared (IR). Among semiconducting plasmonic NCs, spinel metal oxides (sp-MOs) are an emerging class of materials with distinct advantages in accessing the telecommunications bands in the IR and affording useful environmental stability. In this work, we report the plasmonic properties of Fe 3 O 4 sp-MO NCs, known previously only for their magnetic functionality, and demonstrate their ability to modify the light-emission properties of telecom-emitting quantum dots (QDs). We establish the synthetic conditions for tuning sp-MO NC size, composition and doping characteristics, resulting in unprecedented tunability of electronic behavior and plasmonic response over 450 nm. In particular, with diameter-dependent variations in free-electron concentration across the Fe 3 O 4 NC series, we introduce a strong NC size dependency onto the optical response. In addition, our observation of plasmonics-enhanced decay rates from telecom-emitting QDs reveals Purcell enhancement factors for simple plasmonic-spacer-emitter sandwich structures up to 51-fold, which are comparable to values achieved previously only for emitters in the visible range coupled with conventional noble metal NCs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tuning magnetic and optical properties through strain in epitaxial LaCrO 3 thin films

We report on the effect of epitaxial strain on magnetic and optical properties of perovskite LaCrO 3 (LCO) single crystal thin films. Epitaxial LCO thin films are grown by pulsed laser deposition on proper choice of substrates to impose different strain states. A combined experimental and theoretical approach is used to demonstrate the direct correlation between lattice-strain and functional properties. The magnetization results show that the lattice anisotropy plays a critical role in controlling the magnetic behavior of LCO films. The strain induced tetragonality in the film lattice strongly affects the optical transitions and charge transfer gap in LCO. This study opens possibilities to tailor the functional properties of LCO and related materials by strain engineering in epitaxial growth.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Perovskite oxynitrides as tunable materials for electrocatalytic nitrogen reduction to ammonia

The electrocatalytic nitrogen reduction reaction (ENRR) to ammonia is a potentially sustainable alternative to the Haber-Bosch (HB) process, but low Faradaic efficiencies and rates hinder its implementation. Perovskite oxynitrides are a tunable class of materials with unique chemistry for ENRR. As a result, elucidating structure–property–mechanism relations for perovskite oxynitrides will aid ENRR catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Barriers to carriers: faults and recombination in non-stoichiometric perovskite scintillators

Abstract Tuning the efficiency and speed of charge carrier recombination in inorganic scintillators can potentially improve their performance in diverse applications. Recent work suggests that this maybe be achieved via a two-phase scintillator AB that naturally phase separates into A -rich and B -rich domains. In addition, a favorable electronic structure and band-edge alignment such that the charge carriers are confined or are thermodynamically driven to preferentially accumulate in one of the two domains, might lead to an improved radiative recombination rate. Here, we use density functional theory computations and ab initio molecular dynamics (AIMD), including non-adiabatic molecular dynamics (NAMD) simulations, to examine an alternative phase structure and its potential impact on recombination. Using a model perovskite SrTiO $$_3$$ 3 system with one-, two- and three-dimensional Ruddlesden–Popper (RP) phases, we demonstrate that RP faults induce band structure changes in the material that can act as barriers to carrier transport. Our AIMD/NAMD simulations indicate competing effects of a lower mean free path (potentially enhancing the desired radiative recombination and overall scintillating efficiency) and faster non-radiative recombination (undesired) due to enhanced electron–phonon coupling in the faulted system. Full exploitation of such a rational design approach would require tuning of the effective scintillation efficiency by varying the perovskite chemistry using appropriate arrangements of RP faults in the bulk material. Finally, other effects, such as the tendency of point defects to segregate at the interface, that might affect the overall performance, are briefly discussed. We expect the basic results found here to apply to other nanostructured scintillators. Graphical Abstract

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗