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NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗
NASA NTRS2023

A Community Convention for Ecological Forecasting: Output Files and Metadata Version 1.0

This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

Michael C. Dietze↗
NASA NTRS2024

Experimentally Modeling the Emergence of Prebiotically Plausible Phospholipid Vesicles

The prebiotic emergence of protocells is an important part of any origins of life scenario. Although fatty-acid-based vesicles are well studied, how they transitioned to phospholipid vesicles is uncertain. Herein, we show that cyclic-phospholipids formed from fatty acids and glycerol could have played a role by generating a heterogeneous library of vesicles with diverse morphologies and tolerance to a range of metal ions, temperature, and pH. The cyclic phosphate moiety facilitates the natural emergence of vesicles composed of diacyl-phospholipids to become part of the chemical evolutionary process. Competing emergent properties of the various systems (facilitated by additives) could have led to an early preference of the sn-1,2-acyl-substitution on the glycerol backbone coincidental with extant biology. Thus, cyclic-phospholipids could have played a significant role not only in early prebiotic protocellular chemistry but also in facilitating the chemical evolution of protocells from the structurally simple to the functionally more complex.

Sunil Pulletikurti↗
NASA NTRSDate not supplied

Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders

Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.

quantum machine learning↗
DOE OSTI2021

Strategies for Working Remotely: Responding to Pandemic-Driven Change.

In response to the COVID-19 pandemic, the Exascale Computing Project’s (ECP) Interoperable Design of Extreme-scale Application Software (IDEAS) productivity team launched the panel series Strategies for Working Remotely to facilitate informal, cross-organizational dialog in the absence of face-to-face meetings. In a time of pandemic, organizations increasingly need to reach across perceived boundaries to learn from each other, so that we can move beyond stand-alone silos to more connected multidisciplinary and multiorganizational configurations. The present paper argues that the unplanned transition to remote work, overuse of electronic communication, and need to unlearn habits associated with an overreliance on face-to-face, created unique opportunities to learn from the situation and accelerate cross-institutional cooperation and collaboration through online community dialog facilitated by informal panel discussions. Recommendations for facilitating online panel discussions to foster cross-organizational dialog are provided by applying the Simulation Experience Design Method.

Raybourn, Elaine M.↗
DOE OSTI2021

Functional traits driving species role in the structure of terrestrial vertebrate scavenger networks

Abstract Species assemblages often have a non‐random nested organization, which in vertebrate scavenger (carrion‐consuming) assemblages is thought to be driven by facilitation in competitive environments. However, not all scavenger species play the same role in maintaining assemblage structure, as some species are obligate scavengers (i.e., vultures) and others are facultative, scavenging opportunistically. We used a database with 177 vertebrate scavenger species from 53 assemblages in 22 countries across five continents to identify which functional traits of scavenger species are key to maintaining the scavenging network structure. We used network analyses to relate ten traits hypothesized to affect assemblage structure with the “role” of each species in the scavenging assemblage in which it appeared. We characterized the role of a species in terms of both the proportion of monitored carcasses on which that species scavenged, or scavenging breadth (i.e., the species “normalized degree”), and the role of that species in the nested structure of the assemblage (i.e., the species “paired nested degree”), therefore identifying possible facilitative interactions among species. We found that species with high olfactory acuity, social foragers, and obligate scavengers had the widest scavenging breadth. We also found that social foragers had a large paired nested degree in scavenger assemblages, probably because their presence is easier to detect by other species to signal carcass occurrence. Our study highlights differences in the functional roles of scavenger species and can be used to identify key species for targeted conservation to maintain the ecological function of scavenger assemblages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

CO 2 -selective membranes containing amino acid salts for CO 2 /N 2 separation

In this paper, facilitated transport membranes comprising polyvinylamine (PVAm) as fixed carrier and different amino acid salts (AAS) as mobile carriers were synthesized for post-combustion capture. The AAS carriers were prepared by deprotonating alanine (Ala), lysine (Lys), and proline (Pro) with 2-(1-piperazinyl)ethylamine (PZEA). CO 2 separation performances of the membranes with these AAS carriers were compared, and the AAS effectiveness to facilitate transport of CO 2 was in the order of PZEA-Pro > PZEA-Lys > PZEA-Ala. Here, the membrane comprising 35 wt.% PVAm and 65 wt.% PZEA-Pro at 57 °C rendered a promising CO 2 permeance of 936 GPU and a CO 2 /N 2 mixed gas selectivity of 193. When the PVAm was reduced to 15 wt.%, i.e., its 20 wt.% was replaced by the mobile carrier of PZEA-Sar (sarcosinate), the permeance further improved to 947 GPU with a remarkable CO 2 /N 2 selectivity of 210. Moreover, thermal gravimetric analysis showed a good thermal stability of the membrane, and membrane stability testing also gave stable transport performance. Furthermore, spectroscopic ellipsometry analysis exhibited a uniform membrane selective layer and an excellent agreement on the membrane thickness of ca. 170 nm measured independently by SEM. In addition, the membranes presented in this paper surpassed both the Robeson upper bound and the modified upper bound, indicating a great potential for CO 2 capture.

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

New sterically hindered polyvinylamine-containing membranes for CO 2 capture from flue gas

Amine-containing facilitated transport membranes can have both high permeability and selectivity due to the reversible reaction between CO 2 and amino groups for effective carbon capture from flue gas. In this study, we have developed an improved method for the synthesis of high-molecular-weight sterically hindered polyvinylamine (PVAm) as the new fixed-site carrier in thin-film composite membranes for CO 2 capture. Commercially available PVAm was N-monomethylated into poly-N-methyl-N-vinylamine (PVAm-CH 3 ) using the stepwise reductive amination with a highly polar fluorinated alcohol as the solvent to enhance the equilibrium shift to the formation of the imine intermediate. The method prevented over-alkylation, resulting in the increased yield of the target product. PVAm-CH 3 exhibited excellent CO 2 facilitation with a CO 2 permeability of 445.7 Barrer (1 Barrer = 3.349 x 10 –16 mol m m –2 s –1 Pa –1 ) and a CO 2 /N 2 selectivity of 70.3, which are above the Robeson 2008 upper bound. The PVAm-CH 3 solution retained a sufficiently high viscosity after incorporating the aminoacid salt, 2-(1-piperazinyl)ethylamine sarcosinate (PZEA-Sar), as the mobile carrier for the membrane coating on nanoporous polyethersulfone (PES) substrates without any penetration issues. The resultant thin-film composite PVAm-CH3/PZEA-Sar membrane with a thickness of approximately 170 nm exhibited a superior CO 2 performance of 1071 GPU (1 GPU = 3.349 x 10 –10 mol m –2 s –1 Pa –1 ) and a CO 2 /N 2 selectivity of 183 at 57°C and a feed gas pressure of 111.64 kPa (1.5 psig). This PVAm-CH 3 /PZEA-Sar membrane surpassed the latest redefined 2019 CO 2 /N 2 upper bound and outperformed other polymer-based membranes. Density functional theory calculations also demonstrated that PVAm-CH 3 showed a stronger preference, relative to PVAm, toward the more efficient bicarbonate pathway. Thus, the steric hindrance effect of PVAm-CH 3 enhanced the solubility of CO 2 in the polymer matrix and resulted in the higher CO 2 permeance.

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

Structural Design Variations in Beetle Elytra

Abstract Beetles typically use their protective wing coverings or elytra to shield their membranous hindwings from the environment. Elytra in some terrestrial species have evolved a greater protective role capable of shielding the organism from powerful antagonistic predators. The structure–function relationships of these biological composites identify how architectural and chemical variations of the cuticle are tuned to create light‐weight, impact resistant composites. Specifically, the elytral structures of a tree dwelling beetle capable of flight, Trypoxylus dichotomus , and a terrestrial beetle incapable of flight, Phloeodes diabolicus , are compared to understand how their varied environmental needs forged the elytra to facilitate fight or resist fatal predator strikes. Mechanical and microstructural analysis reveals P . diabolicus has a harder, stiffer elytra that incorporate through‐thickness fibers to resist greater mechanical stresses imposed by bending and puncture. Conversely, the elytra of T . dichotomus have a compliant structure with large voids that facilitates localized deformation. Variations in flexural strength and puncture resistance remain attributed to P . diabolicus possessing a thicker cuticle with a greater degree of cross‐linking and an increased amount of endocuticular layers. These findings may provide useful insight into the design and manufacturing of composite materials for use in light‐weight or energy‐absorbing applications.

Rivera, Jesus↗
DOE OSTI2024

Design Principles of Diketopyrrolopyrrole‐Thienopyrrolodione Acceptor 1 –Acceptor 2 Copolymers

Abstract The design principles of acceptor 1 –acceptor 2 copolymers featuring alternating diketopyrrolopyrrole (DPP) and thienopyrrolodione (TPD) moieties are investigated. The investigated series of polymers is obtained by varying the aromatic linker between the two acceptor motifs between thiophene, thiazole, pyridine, and benzene. High electron affinities between 3.96 and 4.42 eV, facilitated by the synergy of the acceptor motifs are determined with optical gaps between 1.37 and 2.02 eV. Grazing incidence wide‐angle X‐ray scattering studies reveal a range of film morphologies after thermal annealing, including face‐on, end‐on and superstructure edge‐on‐like crystallites. Conversely, all materials form thin edge‐on layers on the polymer–air interface, as demonstrated by multi‐elemental near‐edge X‐ray absorption fine‐structure spectroscopy. The benefit of the electron‐deficient linkers thiazole and pyridine is evident: In organic field effect transistors, electron mobilities of up to 4.6 × 10 −2 cm 2 V −1 s −1 are obtained with outstanding on/off current ratios of 5 × 10 5 , facilitated by the absence of detectable hole transport in these materials. Viability for all‐polymer solar cells is assessed in active layer blends with the donor polymer PM6, yielding a maximum average power conversion efficiency of 4.8% and an open circuit voltage above 1 V.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tunable Solid‐State Properties and Anisotropic Charge Mobility in Hydrogen‐Bonded Diketopyrrolopyrrole Polymers via Automated Device Fabrication and Characterization

The optoelectronic properties of semiconducting polymers and device performance rely on a delicate interplay of design and processing conditions. However, screening and optimizing the relationships between these parameters for reliably fabricating organic electronics can be an arduous task requiring significant time and resources. To overcome this challenge, Polybot is developed—a robotic platform within a self‐driving lab that can efficiently produce organic field‐effect transistors (OFETs) from various semiconducting polymers via high‐throughput blade coating deposition. Polybot not only handles the fabrication process but also can conduct characterization tests on the devices and autonomously analyze the data gathered, thus facilitating the rapid acquisition of data on a large scale. This work leverages the capabilities of this platform to investigate the fabrication of OFETs using hydrogen bonding‐containing semiconducting polymers. Through high‐throughput fabrication and characterization, various data trends are analyzed, and large extents of anisotropic charge mobility are observed in devices. The materials are thoroughly characterized to understand the role of processing conditions in solid state and electronic properties of these organic semiconductors. The findings demonstrate the effectiveness of automated fabrication and characterization platforms in uncovering novel structure–property relationships, facilitating refinement of rational chemical design, and processing conditions, ultimately leading to new semiconducting materials.

automated fabrication and characterization↗
DOE OSTI2025

Unveiling Atomistic Mechanisms Governing Additive Manufacturing Processability and Mechanical Behavior of a Refractory Complex Concentrated Alloy

Extending the concept of complex concentrated alloys (CCAs) to the refractory alloys (solidus temperature over 2000 °C) space potentially facilitates the design of lightweight structural alloys with service temperatures that exceed those of Ni and Co‐based alloys. However, the room and elevated temperature tensile properties of the current refractory‐CCAs (R‐CCAs) are inferior to those of the Ni/Co‐based alloys. Furthermore, the manufacturing scalability of R‐CCAs remains challenging, in that cracks are prevalent in all R‐CCAs when processed using near‐net shape manufacturing processes, such as fusion‐based additive manufacturing (F‐BAM). Still, mechanisms governing the poor F‐BAM processability of R‐CCAs remain unexplored. Here, to this end, this work unveils the atomistic mechanisms underlying F‐BAM process‐induced cracking in a NbTiTaMoHfZrC R‐CCA. The implications of light elements’ presence for intrinsic ductility and grain boundary cohesion, and subsequently for F‐BAM processability and mechanical behavior, are revealed. Leveraging the insights, we accomplish what is, to the best of the knowledge, the first instance of crack‐free F‐BAM processing of any R‐CCA. Additionally, the R‐CCA exhibits over 20% tensile ductility and ≈160 MPa tensile yield strength at 1200 °C. In addition to facilitating the design of lightweight R‐CCAs, findings enable scalable manufacturing of these ultra‐high temperature alloys for structural applications.

Refractory alloys↗
DOE OSTI2022

Additive‐Free, Low‐Temperature Crystallization of Stable α‐FAPbI 3 Perovskite

Abstract Formamidinium lead triiodide (FAPbI 3 ) is attractive for photovoltaic devices due to its optimal bandgap at around 1.45 eV and improved thermal stability compared with methylammonium‐based perovskites. Crystallization of phase‐pure α‐FAPbI 3 conventionally requires high‐temperature thermal annealing at 150 °C whilst the obtained α‐FAPbI 3 is metastable at room temperature. Here, aerosol‐assisted crystallization (AAC) is reported, which converts yellow δ‐FAPbI 3 into black α‐FAPbI 3 at only 100 °C using precursor solutions containing only lead iodide and formamidinium iodide with no chemical additives. The obtained α‐FAPbI 3 exhibits remarkably enhanced stability compared to the 150 °C annealed counterparts, in combination with improvements in film crystallinity and photoluminescence yield. Using X‐ray diffraction, X‐ray scattering, and density functional theory simulation, it is identified that relaxation of residual tensile strains, achieved through the lower annealing temperature and post‐crystallization crystal growth during AAC, is the key factor that facilitates the formation of phase‐stable α‐FAPbI 3 . This overcomes the strain‐induced lattice expansion that is known to cause the metastability of α‐FAPbI 3 . Accordingly, pure FAPbI 3 p–i–n solar cells are reported, facilitated by the low‐temperature (≤100 °C) AAC processing, which demonstrates increases of both power conversion efficiency and operational stability compared to devices fabricated using 150 °C annealed films.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solid‐State Prealkylation of Electrode Architectures (SPEAR): Direct Control of Prelithiation Levels in Silicon Anodes and Electrochemical Cycling

The Solid-state Prealkylation of Electrode ARchitectures (SPEAR) is different than traditional electrochemical prealkylation processes. Through SPEAR, alkylation is driven by solid-state diffusion without the simultaneous SEI formation concomitant with polarization. Here, we investigate the prelithiation of 80 wt. % Si-based anodes to varying amounts (up to Li 1.38 Si) to understand the trade-off between improved Li capacity and expansion-induced stress. Through dilatometry, we found that solid-state lithiation led to filling of the electrode pores through silicon expansion. This swelling changed the SEI formation process and accessibility of the silicon compared to an electrochemically lithiated electrode. Indeed, optimal prelithiation to Li 0.82 Si increases the initial C/3 cycling capacity post-SEI formation up to 43%, consistent with deeper Si activation through the electrode bulk. Prelithiation and cycling cells prelithiated beyond Li 0.82 Si results in a state of charge (SOC) close to 100% which facilitates parasitic degradation mechanisms and volume expansion of the Si electrode. The results demonstrate a pathway to modify silicon activation/SEI formation to enable high-energy electrodes.

Musgrove, Amanda L. [Oak Ridge National Laboratory↗
DOE OSTI2020

Tracking the Influence of Thermal Expansion and Oxygen Vacancies on the Thermal Stability of Ni-Rich Layered Cathode Materials

The ever-growing demand for high-energy lithium-ion batteries in portable electronics and electric vehicles has triggered intensive research efforts over the past decade. An efficient strategy to boost the energy and power density of lithium-ion batteries is to increase the Ni content in the cathode materials. However, a higher Ni content in the cathode materials gives rise to safety issues. Herein, thermal expansion and oxygen vacancies are proposed as new critical factors that affect the thermal stability of charged Ni-rich cathode materials based on a systematic synchrotron-based X-ray study of Li 0.33 Ni 0.5+x Co 0.2 Mn 0.3-x O 2 (x = 0, 0.1, 0.2) cathode materials during a heating process. Charged cathode materials with higher Ni contents show larger thermal expansion, which accelerates transition metal migration to the Li layers. Oxygen vacancies are formed and accumulate mainly around Ni ions until the layered-to-spinel phase transition begins. The oxygen vacancies also facilitate transition metal migration to the Li layers. Thermal expansion and the presence of oxygen vacancies decrease the energy barrier for cation migration and facilitate the phase transitions in charged cathode materials during the heating process. These results provide valuable guidance for developing new cathode materials with improved safety characteristics.

25 ENERGY STORAGE↗
DOE OSTI2021

Facile Fabrication of Self‐Assembly Functionalized Polythiophene Hole Transporting Layer for High Performance Perovskite Solar Cells

Abstract Crystallinity and crystal orientation have a predominant impact on a materials’ semiconducting properties, thus it is essential to manipulate the microstructure arrangements for desired semiconducting device performance. Here, ultra‐uniform hole‐transporting material (HTM) by self‐assembling COOH‐functionalized P3HT (P3HT‐COOH) is fabricated, on which near single crystal quality perovskite thin film can be grown. In particular, the self‐assembly approach facilitates the P3HT‐COOH molecules to form an ordered and homogeneous monolayer on top of the indium tin oxide (ITO) electrode facilitate the perovskite crystalline film growth with high quality and preferred orientations. After detailed spectroscopy and device characterizations, it is found that the carboxylic acid anchoring groups can down‐shift the work function and passivate the ITO surface, retarding the interface carrier recombination. As a result, the device made with the self‐assembled HTM show high open‐circuit voltage over 1.10 V and extend the lifetime over 4,300 h when storing at 30% relative humidity. Moreover, the cell works efficiently under much reduced light power, making it useful as power source under dim‐light conditions. The demonstration suggests a new facile way of fabricating monolayer HTM for high efficiency perovskite devices, as well as the interconnecting layer needed for tandem cell.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Bridging Thermal Catalysis and Electrocatalysis: Catalyzing CO 2 Conversion with Carbon‐Based Materials

Abstract Understanding the differences between reactions driven by elevated temperature or electric potential remains challenging, largely due to materials incompatibilities between thermal catalytic and electrocatalytic environments. We show that Ni, N‐doped carbon (NiPACN), an electrocatalyst for the reduction of CO 2 to CO (CO 2 R), can also selectively catalyze thermal CO 2 to CO via the reverse water gas shift (RWGS) representing a direct analogy between catalytic phenomena across the two reaction environments. Advanced characterization techniques reveal that NiPACN likely facilitates RWGS on dispersed Ni sites in agreement with CO 2 R active site studies. Finally, we construct a generalized reaction driving‐force that includes temperature and potential and suggest that NiPACN could facilitate faster kinetics in CO 2 R relative to RWGS due to lower intrinsic barriers. This report motivates further studies that quantitatively link catalytic phenomena across disparate reaction environments.

Koshy, David M.↗
DOE OSTI2021

Bridging Thermal Catalysis and Electrocatalysis: Catalyzing CO 2 Conversion with Carbon-Based Materials

Understanding the differences between reactions driven by elevated temperature or electric potential remains challenging, largely due to materials incompatibilities between thermal catalytic and electrocatalytic environments. In this work, we show that Ni, N-doped carbon (NiPACN), an electrocatalyst for the reduction of CO 2 to CO (CO 2 R), can also selectively catalyze thermal CO 2 to CO via the reverse water gas shift (RWGS) representing a direct analogy between catalytic phenomena across the two reaction environments. Advanced characterization techniques reveal that NiPACN likely facilitates RWGS on dispersed Ni sites in agreement with CO 2 R active site studies. Finally, we construct a generalized reaction driving-force that includes temperature and potential and suggest that NiPACN could facilitate faster kinetics in CO 2 R relative to RWGS due to lower intrinsic barriers. This report motivates further studies that quantitatively link catalytic phenomena across disparate reaction environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗