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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Performance analysis of photodetectors based on 2D materials and heterostructures

The unprecedented demand for sophisticated, self-powered, compact, ultrafast, cost-effective, and broadband light sensors for a myriad of applications has spurred a lot of research, precipitating in a slew of studies over the last decade. Apart from the photosensing ability of an active element in the light sensor, the device architecture is crucial in terms of photoinduced charge carrier generation and separation. Since the inception of graphene and the subsequent research growth in the atomically thin 2D materials, researchers have developed and adapted different families of 2D materials and device architectures, including single element 2D, 0D/2D, 2D/2D, 1D/2D stacked structures, and so on. Here this review discusses the recent reports on the light-sensing properties of various 2D materials, their heterostructures, and characteristics applicable to the ultraviolet-near infrared (UV-NIR), short-wave IR (SWIR), mid-wave IR (MWIR), long-wave IR (LWIR), and terahertz (THz) spectral ranges. It highlights the novelty of the burgeoning field, the heightened activity at the boundaries of engineering and materials science, particularly in the generation of charge carriers, their separation, and extraction, and the increased understanding of the underpinning science through modern experimental approaches. Devices based on the simultaneous effects of the pyro-phototronic effect (PPE) and the localized surface plasmon resonance (LSPR) effect, the photothermoelectric effect (PTE)-assisted photodetectors (PDs), waveguide-integrated silicon-2D PDs, metal-2D-metal PDs, and organic material PDs are examined rigorously. Theoretical treatment utilizing various computational approaches to investigate 2D materials and heterostructures for photodetection applications is also briefly discussed. At the end, current challenges and solutions to enhance the figures of merit of photodetectors are proposed.

2D transition metal chalcogenides↗
DOE OSTI2020

Harnessing biological applications of quantum materials: opportunities and precautions

Since the discovery of graphene, research on two-dimensional (2D) quantum materials has generated a plethora of novel materials with the potential to significantly change our lives. Owing to their unique quantum, electronic, and excitonic properties, biosensing and bio-imaging are two specific areas where these quantum materials have started to make a large impact. This article provides a holistic overview into the properties, synthesis methods, biological applications, and toxicology studies of commonly studied quantum materials. Recent research into the biological applications of graphene and transition metal dichalcogenides is highlighted and their unique electronic properties are explored. Though tremendous research is generated in discovering new 2D materials, toxicology studies in the area are lacking. Historically, toxicology studies have lagged significantly behind new material discovery often with dire consequences. The product development cycle of polymeric materials is a testament to the negative consequences of having a reactionary approach to toxicology. With 2D materials set to become a widespread part of everyday life in the near future, we comment on the need for a proactive approach to examining and understanding their toxicology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗
DOE OSTI2022

Engineered Living Materials For Sustainability

Recent advances in synthetic biology and materials science have given rise to a new form of materials, namely engineered living materials (ELMs), which are composed of living matter or cell communities embedded in self-regenerating matrices of their own or artificial scaffolds. Like natural materials such as bone, wood, and skin, ELMs, which possess the functional capabilities of living organisms, can grow, self-organize, and self-repair when needed. They also spontaneously perform programmed biological functions upon sensing external cues. Currently, ELMs show promise for green energy production, bioremediation, disease treatment, and fabricating advanced smart materials. This review first introduces the dynamic features of natural living systems and their potential for developing novel materials. We then summarize the recent research progress on living materials and emerging design strategies from both synthetic biology and materials science perspectives. Lastly, we discuss the positive impacts of living materials on promoting sustainability and key future research directions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

FAIRLinked: Data FAIRification Tools for Materials Data Science

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of data from CSV into JSONLDs and vice versa in a way that captures the semantics of the data using MDS-Onto. Lastly, QBWorkflow is a serialization and deserialization workflow that incorporates RDF Data Cube vocabulary, useful for working with multidimensional datasets. By offering these packages, FAIRLinked lowers the barrier of creating FAIR, machine-actionable data for researchers in the materials science community.

FAIR↗
DOE OSTI2021

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Here, artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are research fields where DNN results valorize the data driven approach; especially in cases where conventional methods have failed. In view of the great potential of deep learning for the future of artificial electromagnetic materials research, the status of the field with a focus on recent advances, key limitations, and future directions is reviewed. Strategies, guidance, evaluation, and limits of using deep networks for both forward and inverse AEM problems are presented.

14 SOLAR ENERGY↗
DOE OSTI2023

A framework for materials informatics education through workshops

The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings. Here, in this article, we discuss the importance of materials informatics education via the lens of these workshops, including details such as learning and implementing specific algorithms, the crucial nuts and bolts of ML, and using competitions to increase interest and participation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hybrid Metal Halides: Advancing Optoelectronic Materials (Final Technical Report)

Hybrid metal halides, particularly main group halide perovskites, are a unique class of materials that offer exceptional optoelectronic properties along with a remarkable materials design space. Initial research on this class of materials was driven by the ability of the prototype hybrid 3D perovskite-structured compound methylammonium lead iodide to function as the active layer in thin film solar cells. It is now known that this class of materials comprise a large family with tunable band gaps, relatively high charge carrier mobilities in both single crystal and polycrystalline forms, and relatively low concentrations of (deleterious) electrically active states within the band gap. Beyond applications in solar cells, the potential of these materials has been extended to emitters in light emitting diodes, and active components of radiation detectors. Complementing new functionality, the design space for hybrid metal halides continues to increase with discovery of new structural motifs. In this project, the combination of organic and inorganic functionalities has been employed to open pathways to the design and synthesis new, functional hybrid metal halides. Beyond the simple perovskites, Ruddlesden-Popper and Dion-Jacobsen compounds, and other variants, such as the newly advanced “hollow” perovskites have been studied. The materials have provided routes to understanding the unique electronic properties of hybrid metal halides because of their natural quantum well structures as well as other means of controlling band gaps and band dispersions. Understanding these materials, including the design rules for their formation, their structures and compositions in bulk and in thin films form, and the role of local (non-crystallographic) structure has an important aspect of this endeavor. The goal of advancing new materials and new fundamental understanding within this deceptively simple, yet fascinating class of compounds, so richly endowed with interesting and useful functionality, has been fulfilled.

36 MATERIALS SCIENCE↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2022

Water Dynamics in Cancer Cells: Lessons from Quasielastic Neutron Scattering

The severity of the cancer statistics around the globe and the complexity involving the behavior of cancer cells inevitably calls for contributions from multidisciplinary areas of research. As such, materials science became a powerful asset to support biological research in comprehending the macro and microscopic behavior of cancer cells and untangling factors that may contribute to their progression or remission. The contributions of cellular water dynamics in this process have always been debated and, in recent years, experimental works performed with Quasielastic neutron scattering (QENS) brought new perspectives to these discussions. In this review, we address these works and highlight the value of QENS in comprehending the role played by water molecules in tumor cells and their response to external agents, particularly chemotherapy drugs. In addition, this paper provides an overview of QENS intended for scientists with different backgrounds and comments on the possibilities to be explored with the next-generation spectrometers under construction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Highly Enhanced Curie Temperature in Ga-Implanted Fe3GeTe2 van der Waals Material

Among many efforts in the research of van der Waals (vdW) magnetic materials, increasing the Curie temperature above room temperature has been at the center of research in developing spintronics technology using vdW materials. Here an effective and reliable method of increasing the Curie temperature of ferromagnetic Fe3GeTe2 vdW materials by Ga implantation is reported. It is found that implanting Ga into Fe3GeTe2 by the amount of 10-3 Ga Å-3 could greatly enhance the Fe3GeTe2 Curie temperature by almost 100%. Spatially resolved microdiffraction and element-resolved X-ray absorption spectroscopy show little changes in the Fe3GeTe2 crystal structure and Fe valence state. In addition, the Ga implantation changes the Fe3GeTe2 magnetization from out-of-plane direction at low temperature to in-plane direction at high temperature. The result opens a new opportunity for tailoring the magnetic properties of vdW materials beyond room temperature.

Yang, M↗
DOE OSTI2020

The Role of Electron Localization in Covalency and Electrochemical Properties of Lithium-Ion Battery Cathode Materials

Following the fundamental research conducted by J. B. Goodenough, the important role of electron localization induced by elemental substitution is studied. The size and electron negativity of host and substituting ions are two important factors in tuning material properties such as local structure and transition metal (TM) oxygen covalency. However, another factor, electron localization, which is widely studied in catalyst research but largely overlooked for battery materials, deserves systematic studies. A combined investigation using synchrotronbased X-ray spectroscopy and theoretical calculations is carried out on the Li-Co-Mn-O model system in which the substituting cation Mn 4+ , with its 3d 3 electronic structure, is used as a promoter for electron localization. Results indicate that electron localization greatly influences the Co - O bond by making it less covalent, which increases the delithiation voltage. It is also found that during charge/discharge, electron localization tends to make TM K-edge X-ray absorption near edge spectroscopy (XANES) spectra show a more “rigid shift” behavior while electron delocalization makes the XANES exhibit a “shape change.” It clearly explains why the K-edge XANES data of some TM oxides show no “rigid shift” while the nominal valence states changed. This work highlights the importance of electron localization with guidance for XANES interpretation.

25 ENERGY STORAGE↗
DOE OSTI2023

Advanced Thermoluminescence Spectroscopy as a Research Tool for Semiconductor and Photonic Materials: A Review and Perspective

Thermoluminescence (TL) or thermally stimulated luminescence (TSL) spectroscopy is based on liberating charge carriers from traps in the bandgap by providing enough thermal energy to overcome the potential barrier of the traps. It provides a powerful tool to measure the positions of the localized states/traps in the bandgap. Despite that, its applications in semiconductors are very limited. Herein, the basics of TL spectroscopy and the recent advances in the technique with focus on cryogenic thermally stimulated photoemission spectroscopy (C‐TSPS) which extends TL measurements to cryogenic regime and allows the detection of very low concentrations of shallow and deep localized states is discussed. One goal herein is to introduce the reader to the use of TL and C‐TSPS in the characterization of semiconductors, explaining how it can be applied and demonstrating its advantages as a powerful tool for measuring shallow donor/acceptor ionization energies in semiconductors and as a method for characterizing compensating defects. The article also discusses interesting potential applications of C‐TSPS in new research areas such as corrosion and formation of oxide layers on metal surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗
DOE OSTI2024

A review on machine learning-guided design of energy materials

Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Unveiling 3D Morphology of Multiscale Micro-Nanosilver Sintering for Advanced Electronics Manufacturing by Ptychographic X-ray Nanotomography

The sintering processing–structure–property relationship of a multiscale silver materials is investigated: microparticles with nanofeatures, particularly on their three-dimensional (3D) morphology. The target application is to replace conventional lead-based solders in advanced electronic manufacturing. Unlike lead-based solders, silver powders are suited to satisfy increasingly demanding mechanical, electrical, and thermal requirements, meanwhile being free of health effect. Sintering the material at a low temperature and without applied pressure are desirable conditions, which results in a preferred use of silver nanoparticles, as nanofeatures have higher driving force to sinter with the decrease in particle size. However, nanosized powders present potential health/environmental effects. To address the trade-off between the benefits and shortcomings of nano-versus microparticles, this work studies a novel multiscale silver paste, namely micron-sized powders with nanosized features. To get quantitative 3D visualization of micro- and nanoscale features, ptychographic X-ray computed nanotomography is applied. The correlations between conditions (thermal aging, pressure, and substrate metallization), mechanical properties, and morphological parameters are established. Finally, using novel 3D X-ray nanoimaging technique, it is demonstrated that one can design multiscale materials while balancing complex demands required in advanced electronics manufacturing and research directions in materials design and characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Advances in Multimodal Characterization of Structural Materials

The myriad detectors and instruments now available for materials characterization provide researchers with an ever-growing suite of tools to probe material behavior. Progress in the development of instrumentation and workflows that enable the collection, and leverage the potential, of various data modalities have provided novel insights into material behavior. Using data across multiple length scales, or performing complementary analyses of in situ and ex situ data, can help reveal a more complete picture of dynamic processes or material structure. However, the accurate combination, or fusion, of these disparate data modalities presents new challenges. Differences in resolution, as well as the varying length scales at which physical phenomena are exploited to generate these data, necessitate novel approaches to accurately interpret and combine these data. Furthermore, the papers within this special topic focus on the collection and fusion of multimodal data to better understand structural materials. From new frameworks and workflows for data segmentation and analysis, process monitoring, enhancing simulations, or interrogating mechanical response, these papers reveal the potential benefits of utilizing multimodal data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution

Abstract Researchers in ecology and evolutionary biology are increasingly dependent on computational code to conduct research. Hence, the use of efficient methods to share, reproduce, and collaborate on code as well as document research is fundamental. GitHub is an online, cloud‐based service that can help researchers track, organize, discuss, share, and collaborate on software and other materials related to research production, including data, code for analyses, and protocols. Despite these benefits, the use of GitHub in ecology and evolution is not widespread. To help researchers in ecology and evolution adopt useful features from GitHub to improve their research workflows, we review 12 practical ways to use the platform. We outline features ranging from low to high technical difficulty, including storing code, managing projects, coding collaboratively, conducting peer review, writing a manuscript, and using automated and continuous integration to streamline analyses. Given that members of a research team may have different technical skills and responsibilities, we describe how the optimal use of GitHub features may vary among members of a research collaboration. As more ecologists and evolutionary biologists establish their workflows using GitHub, the field can continue to push the boundaries of collaborative, transparent, and open research.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗