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NASA NTRS1925

Relation of Fuel-Air Ratio to Engine Performance

The tests upon which this report is based were made at the Bureau of Standards between October 1919 and May 1923. From these it is concluded that: (1) with gasoline as a fuel, maximum power is obtained with fuel-air mixtures of from 0.07 to 0.08 pound of fuel per pound of air; (2) maximum power is obtained with approximately the same ratio over the range of air pressures and temperatures encountered in flight; (3) nearly minimum specific fuel consumption is secured by decreasing the fuel content of the charge until the power is 95 per cent of its maximum value. Presumably this information is of most direct value to the carburetor engineer. A carburetor should supply the engine with a suitable mixture. This report discusses what mixtures have been found suitable for various engines. It also furnishes the engine designer with a basis for estimating how much greater piston displacement an engine operating with a maximum economy mixture should have than one operating with a maximum power mixture in order for both to be capable of the same power development.

Sparrow, Stanwood W↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

42 - ENGINEERING↗

DOE OSTI2024

2024 Atmospheric Radiation Measurement (ARM) Annual Report

This report provides an overview of the Atmospheric Radiation Measurement (ARM) user facility and a sample of achievements for fiscal year 2024 (FY2024). Strong collaborations between nine DOE national laboratories enable ARM to successfully operate in remote locations around the world. This unique partnership supports the DOE mission to provide for the energy security of the nation. Without the support of the following laboratories, ARM would not be the state-of-the-art facility that it is today. ARM is a multi-laboratory, U.S. Department of Energy (DOE) Office of Science user facility and a key contributor to national and international atmospheric and climate research efforts. ARM offers scientists cutting-edge, ground-based observatories, aerial observation capabilities, and high-performance computing. ARM’s capabilities have enabled more than 30 years of continuous measurements of cloud and aerosol properties and their effects on Earth’s energy balance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Technology Impact and Resource Assessment of Existing and Planned U.S. Biofuel Production: Life Cycle Water Consumption, Water Stress, Land Use, and Criteria Air Pollutants

Biofuels have the potential to strengthen the U.S. energy supply, enhance energy security, and promote economic development. As the United States continues to expand biofuel production, quantifying resource requirements and location-specific constraints is crucial for planning, siting, and technology development to support long-term viability. Accordingly, this work assesses the life cycle resource consumption (water consumption, and land use), water stress and criteria air pollutants associated with expanded U.S. biofuel production over the 2020–2035 period, based on producers’ plans. We perform a bottom-up technology impact assessment and resource assessment by integrating facility-level production statistics with Argonne’s Research and Development (R&D) GREET model and county-level water-stress characterization factors from Available Water Remaining for the United States (AWARE-US) model. Results suggest that by 2035, biofuels could meaningfully contribute to U.S. energy demand, driven primarily by first-generation and waste-based feedstocks with plans for substantial capacity expansion, although cellulosic and e-fuel technologies remain limited. However, growth must be managed to minimize impacts on water resources and land use. These impacts vary by fuel type and facility location, specifically, projected expansion increases water consumption and can elevate water-stress impacts in certain regions like Nebraska, Kansas, Colorado, Idaho, California and North Texas. Direct land use also increases overall, particularly for first-generation feedstocks such as corn and soybeans. These findings underscore the need for continued technological improvements and innovative strategies to manage resource demands as the industry scales and to support complementary deployment within the evolving U.S. energy system.

09 BIOMASS FUELS↗

DOE OSTI2025

Acoustic Observations of the OSIRIS-REx Sample Return Capsule Re-Entry from Wendover Airport

The Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer sample return capsule (SRC) re‐entered the Earth’s atmosphere at hypersonic speeds from interplanetary space on 24 September 2023. The current work reports on 18 ground‐based acoustic sensors deployed at Wendover Airport, the same location that the Genesis and Stardust SRC re‐entries were recorded. Four different sensors (Chaparral Physics, Gem, Wilson Engineering Research and Development [WERD], and RedVox) were deployed in close proximity to compare their performance. All the sensors captured an N‐wave signal associated with the SRC re‐entry shock wave followed by a broadband coda. The Chaparral Physics array served as the high‐fidelity reference measurement. The N‐wave signal had a peak‐to‐peak amplitude of 4.07 Pa with a fundamental frequency of 4.98 Hz from 167.5° measured clockwise from north, nearly perpendicular to the SRC trajectory. In addition, high coherence in the coda was shown to be associated reflections from the surrounding mountains. In general, the more economical sensors (Gem, WERD, and RedVox) produced results that were consistent with these observations and sensor specifications. Beamforming with these single sensors arranged as an array showed agreement with the high‐fidelity array to within a couple of degrees. Furthermore, the current high‐fidelity results were compared with the measurements during the Genesis and Stardust SRC re‐entries. All three entries produced a broadband fundamental peak at a frequency that was inversely related to the SRC diameter as well as evidence of reflections from the surrounding topography.

KC, Real J. [Oklahoma State University, Stillwater↗

DOE OSTI2026

Radiation Effects in Next Generation Used Nuclear Fuel Reprocessing Strategies

With the global community committed to significantly expanding nuclear energy capacity, the development of efficient used nuclear fuel (UNF) management strategies has become more critical than ever. These strategies are vital to fostering the widespread adoption of closed fuel cycles, which are essential for sustainable nuclear energy production and security. Achieving this ambitious goal necessitates a comprehensive understanding of radiation effects on next-generation technologies, as radiolysis can often limit the longevity and performance of these systems. This seminar will provide an overview of next-generation UNF reprocessing strategies, highlighting the latest advancements and innovative approaches in the field. Particular attention will be given to two key areas of recent research: 1. Radiation robustness and performance of advanced sulfur chloride-based chlorination technologies. We will explore the efficacy of sulfur chloride-based chlorination processes in the presence of surrogate cladding materials, specifically aluminum. These processes have shown promise in the dissolution, decontamination, and recovery of cladding materials for reuse. Detailed findings on how the composition and performance of these sulfur chloride solvents respond to radiation exposure will be discussed. 2. Impacts of metal ion complexation and direct dissolution conditions on monoamide-based reprocessing strategies. We will delve into the time-resolved and dose accumulation effects of irradiation on the direct dissolution of voloxidized uranium and rhenium using N,N-di-(2-ethylhexyl) butyramide (DEHBA) or N,N-di-(2-ethylhexyl)isobutyramide (DEHiBA) in pre-equilibrated n-dodecane solvent. The implications of these interactions on dissolution efficiency, radiolytic stability, and overall process performance will be examined. These studies aim to underscore the importance of understanding radiation effects in the development of next-generation UNF reprocessing technologies and the global transition towards more sustainable and efficient nuclear energy systems.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗

DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗

DOE OSTI2025

Scale up production of carbon fibers from petroleum mesophase pitch

This work investigates the scale-up of pitch precursors for the carbon fiber market using mesophase pitch formulated and produced by Advanced Carbon Products Technologies’ (ACPT’s) patented mesophase pitch processing technology. Through use of its patented process, ACPT developed strategic materials by converting petroleum-based pitch into a high-value, low-cost, carbon-rich feedstock for carbon fiber and other materials critical to our national security. The team successfully developed processing criteria for the tailored mesophase pitch material that can be processed further into precursor and carbon fiber. Processability of the mesophase pitch into precursor and carbon fiber was demonstrated at scale. The resulting materials sequester the carbon that would otherwise be burned and released into the atmosphere, making this an environmentally friendly way to produce these materials in the United States.Pitch-based carbon fibers offer a promising pathway toward cost-effective, high-performance materials for structural and high-modulus composite applications; however, adoption has been limited by challenges in precursor processability and scale-up. In this work, tailored isotropic and mesophase petroleum-derived pitch materials were developed and evaluated for precursor and carbon fiber production through a collaborative effort between Oak Ridge National Laboratory (ORNL) and ACPT. Processing conditions were established at ORNL’s Carbon Fiber Technology Facility to enable stable melt-blowing of pitch-based precursors under continuous operation. Mesophase pitch precursor fibers were produced following oxidation and carbonization, corresponding to a diameter shrinkage of approximately 12%–13% and an estimated mass yield of about 75%–80%. Continuous melt-blowing steady-state operation was demonstrated over time, indicating robust process stability. Melt-blowing was achieved at throughput rates of approximately 20 lb/h⁻¹, validating the commercial viability of petroleum-derived pitch feedstocks for fiber production. Further studies are required to tailor carbon fiber microstructure and properties for specific composite applications.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.

Distributed↗

DOE OSTI2025

Application of Cyber-Informed Engineering for Protecting BESS

This white paper synthesizes an array of crucial grid services provided by BESS technology, assesses its architecture and communications, and presents a case study for analysis against the principles introduced by Cyber-Informed Engineering (CIE). Furthermore, in walking through the analysis, this paper presents a framework to evaluate risks and solutions when considering BESS components. Asset owners and buyers could perform this analysis to assess their BESS product implementations, alternative inverter-based resources (IBR), and energy management systems (EMS). Battery systems fulfill various roles contingent on the unique market demands and the specific challenges presented by regional grid infrastructures. These roles also vary due to the differing utility models for ownership and operation, which are adapted to meet regional and local capabilities and requirements. Concerns have been raised regarding the potential for adversaries to exploit knowledge of battery operational patterns to orchestrate decisive attacks. However, the security of operational data for these systems may not be the primary vulnerability, as much of this information is already well-understood within the community. Applying a modest degree of subject matter expertise can often yield valuable predictions regarding how a battery will respond under certain conditions, such as grid emergencies, high or low-temperature days, Public Safety Power Shutoff (PSPS) events, and outages. The operational characteristics of batteries are well-documented, and their capabilities, including the risks associated with misoperation and the resulting consequences, are published and understood within the industry. CIE practices represent the next step in gaining functional assurance and providing an acceptable level of risk, regardless of whether a battery vendor can support a trusted and validated supply chain. While this issue has exacerbated supply chain challenges, it is not an isolated condition. This foreign supply route is the primary source of BESS for the U.S. market. Significant efforts are underway through the Bipartisan Infrastructure Law (BIL) to change that. Still, strategic short-term operational mitigations are needed to ensure the security of our operational technology (OT) systems, which are enhanced by instilling trust and are separate from vendors implementing CIE principles.

25 ENERGY STORAGE↗

DOE OSTI2025

Capability Building Progression of an Insider Threat Mitigation Program at an International Research Reactor

The nuclear industry recognizes the difficulties involved in developing effective managerial and leadership skills in a highly technical and proficient workforce such as that found in nuclear facilities. Implementing an insider threat mitigation program (ITMP) within the nuclear industry is a complex and ongoing process that demands a comprehensive understanding of human behavior, an organization’s security culture, and rigorous regulatory requirements yet also accounts for facility characteristics, physical security, material flow, and activities involving nuclear material. Given the high-consequence nature of research reactor operations, even minor lapses can lead to safety, security, and reputational risks. An effective ITMP requires a defense-in-depth approach that incorporates behavioral analysis, robust vetting procedures, continuous monitoring, and cross-disciplinary coordination. It must also promote a culture of vigilance and accountability at all levels up to and including executive leadership but be flexible enough to adapt to evolving global threats and technological advances. Insider threat mitigation is not a one-time effort but rather a sustained commitment to excellence in safety and security. Establishing a culture in which personnel proactively report incidents and issues that could affect nuclear safety and security is vital to maintaining a safe and secure operational environment. This document was developed to guide senior management and research reactor organizations in creating comprehensive programs to effectively manage and mitigate insider threat behaviors and actions. It focuses on the key pillars of an effective ITMP, including the national legal framework, security culture, preventive and protective measures, cyber security, and performance evaluation. By using a systematic approach during implementation, facilities can foster environments conducive to insider threat detection and support long-term program sustainability. The document also provides strategies for improving communication across all levels of an organization, helping to eliminate barriers that hinder the development of robust ITMPs and enhance overall security culture. In today’s organizations, the concept of leveraging safety and security culture lessons to facilitate knowledge transfer is rapidly evolving to expedite insider threat management and security culture improvements. This document outlines the rationale for evaluating an ITMP based on national customs, culture, and stakeholders. The elements are all germane to reliability and trustworthiness and relate to security concerns that states may encounter. The document focuses not only on individual perceptions regarding security issues and capability building but also on team building and how to resolve concerns. The implementers of a facility’s ITMP may zero in on indicators of insider threats within their enterprise. This material will benefit organizations when it is applied using a systematic and structured approach as demonstrated throughout the document.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

Fostering Nuclear Security Culture through Effective Leadership: An Operational Perspective

Security culture plays a critical role in determining the effectiveness of an organization's security performance, making its significance impossible to overemphasize. It encompasses the collective values, shared perceptions, and habitual actions embraced by all individuals within a nuclear organization—from leadership to frontline staff. When the entire workforce recognizes the reality of potential threats, accepts that security is a shared duty, and integrates security-minded behavior into everyday routines, it fosters an environment where strong security practices are the norm. In such a setting, everyone can take pride and feel reassured in being part of an organization where a strong security culture is deeply embedded. Security culture is based on the broader concept of organizational culture. All organizations—whether families, social clubs, religious institutions, businesses, non-governmental organizations, or governments—possess an underlying culture shaped by core values and beliefs. These values and beliefs influence attitudes and drive behavior throughout the organization. While multiple factors contribute to the development of a strong security culture, leadership plays a particularly pivotal role. In organizations where security culture is well-established, leaders go beyond rhetoric; they demonstrate a genuine commitment to security through their actions. They implement policies and procedures that actively engage all employees, foster open dialogue around security concerns, and encourage teamwork in resolving issues. Furthermore, they reward proactive behavior and ensure that corrective actions are taken promptly. Regular assessments of the organization's security culture allow such leaders to gauge its effectiveness and take strategic steps to strengthen it when necessary. This paper leverages practical, real-world experience to guide leadership and senior management within nuclear organizations through the foundational steps of cultivating a robust, organization-wide culture of nuclear security. It emphasizes the critical importance of early leadership engagement in shaping this culture and outlines a comprehensive approach that includes strategic, tactical, and operational measures. Additionally, it explores methods for fostering a unified vision across all levels of the organization to ensure alignment, commitment, and continuous improvement in nuclear security practices.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗

DOE OSTI2025

Design Choices in Anomaly Detection for Industrial Control Systems: Insights from Gas Pipeline Data

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and naïve imputation—prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensor-decomposition–based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗

DOE OSTI2026

Performance evaluation of neutron noise analysis in detecting special nuclear materials

Reliable and rapid inspection techniques play a vital role in preventing illicit trafficking of special nuclear materials. Active interrogation systems using neutrons produced by portable, high-flux deuterium-deuterium or deuterium-tritium neutron generators are being actively developed as a secondary scanning tool for this purpose. In this study, a neutron noise analysis-based approach for detecting unshielded and shielded special nuclear materials by using a pulsed deuterium-tritium neutron generator was evaluated. Here, this approach analyzes the fluctuation of neutron counts. Its performance was quantified with regard to time-to-detection to achieve a minimum probability of detection of 99% and a probability of false alarm of less than 1% considering various amounts of special nuclear materials and different shielding configurations. It was demonstrated that this approach could detect 17 uranium slugs in 5 s given a neutron generator yield of 8.1 × 10 7 n/s. These slugs could be detected within a reasonable time frame (200 s) when they were shielded by 10.16 cm of high-density polyethylene. The results obtained using the neutron noise analysis approach were compared with those obtained using the commonly used differential die-away analysis technique, a sensitive technique for detecting the presence of fissile materials by utilizing the prompt fission neutrons produced when the source neutrons from a neutron generator are completely diminished. For example, the time to detect 2 unshielded uranium slugs was 2.1 s when using the differential die-away analysis technique; it increased to 93 s for the neutron noise analysis approach. Although the noise analysis-based approach exhibits an overall performance which is not as good as that of differential die-away, neutron noise provides an alternative method for effective detection of special nuclear materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Quantum defects in carbon nanotubes as single-photon sources

Single-photon emitters are essential components of emerging quantum technologies, including secure communication and quantum computing. Single-walled carbon nanotubes (SWCNTs) have emerged as a promising platform for quantum light sources due to their quasi-one-dimensional excitonic host structure and compatibility with telecom photonic systems. Recent advances in deterministic defect engineering—most notably the development of organic color centers (OCCs)—have enabled stable, chemically controllable, and spectrally tunable single-photon emission. OCC-based emitters have demonstrated single-photon purity exceeding 99% and, more recently, room-temperature photon indistinguishability, placing them among the few solid-state systems with quantum-grade performance under ambient conditions. This review surveys progress in the field from three complementary perspectives: chemical synthesis and quantum defect engineering, computational studies of structure-property relationships and excitonic behavior, and experimental investigations of quantum optical properties. We also discuss alternative approaches, including air-suspended SWCNTs and hybrid van der Waals heterostructures, highlighting opportunities and open challenges for scalable integration into quantum photonic platforms.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗