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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar: SUMO Default vs. Radar-Measured CF model Parameters

In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.

Schrader, Max (ORCID:0000000336737672)↗
DOE OSTI2020

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Radiation exposure induces cross-species temporal metabolic changes that are mitigated in mice by amifostine

Exposure to acute, damaging radiation may occur through a variety of events from cancer therapy and industrial accidents to terrorist attacks and military actions. Our understanding of how to protect individuals and mitigate the effects of radiation injury or Acute Radiation Syndrome (ARS) is still limited. There are only a few Food and Drug Administration-approved therapies for ARS; whereas, amifostine is limited to treating low dose (0.7–6 Gy) radiation poisoning arising from cancer radiotherapy. An early intervention is critical to treat ARS, which necessitates identifying diagnostic biomarkers to quickly characterize radiation exposure. Towards this end, a multiplatform metabolomics study was performed to comprehensively characterize the temporal changes in metabolite levels from mice and non-human primate serum samples following γ-irradiation. The metabolomic signature of amifostine was also evaluated in mice as a model for radioprotection. The NMR and mass spectrometry metabolomics analysis identified 23 dysregulated pathways resulting from the radiation exposure. These metabolomic alterations exhibited distinct trajectories within glucose metabolism, phospholipid biosynthesis, and nucleotide metabolism. A return to baseline levels with amifostine treatment occurred for these pathways within a week of radiation exposure. Together, our data suggests a unique physiological change that is independent of radiation dose or species. Furthermore, a metabolic signature of radioprotection was observed through the use of amifostine prophylaxis of ARS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Aging matrix visualizes complexity of battery aging across hundreds of cycling protocols

To reliably deploy lithium-ion batteries, a fundamental understanding of cycling aging behavior is critical. Battery aging consists of complex and highly coupled phenomena, making it challenging to develop a holistic interpretation. In this work, we generate a diverse battery cycling dataset with a broad range of degradation trajectories, consisting of 359 high energy density commercial Li(Ni,Co,Al)O 2 /graphite + SiO x cylindrical 21 700 cells cycled across 207 unique cycling protocols. We consolidate aging via 16 mechanistic state-of-health (SOH) metrics, including cell-level performance metrics, electrode-specific capacities/state-of-charges (SOCs), and aging trajectory metrics. We develop a framework using interpretable machine learning and explainable features to generate an aging matrix that visually deconvolutes the complex battery degradation behavior. This generalizable data-driven mechanistic framework simplifies the complex interplay between cycling conditions, degradation modes, and SOH, acting as a hypothesis-generation tool to aid battery users in identifying key degradation regimes for further study and experimentation.

25 ENERGY STORAGE↗
DOE OSTI2023

Modeling Future Demand for EV Charging Infrastructure [Slides]

U.S. climate goals for economy-wide net-zero greenhouse gas emissions by 2050 require rapid decarbonization of the light-duty vehicle fleet and plug-in electric vehicles (EVs) are poised to become the preferred technology for achieving this end. Considerable investments in public and private EV charging infrastructure will be needed to support widespread adoption, however, guidance is lacking on when, where, and what types of chargers will be needed. In this PLMA Load Management Dialogue session, researchers from the National Renewable Energy Laboratory (NREL) discuss findings from a recent quantitative assessment of the charging network requirements to support high penetrations of light-duty EVs by 2030. The study produced multiple detailed network growth trajectories at the national, state, and local levels that serve as a guidepost for future planning. In addition, an overview of NREL's publicly accessible EV infrastructure tools and data sets, designed to support planning and decision-making for EV infrastructure stakeholders, is provided.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

NREL Price Series Developed for the ARPA-E FLECCS Program

The price data for four regions (CAISO, ERCOT, MISO-W, and PJM-W) are developed using the ReEDS to PLEXOS conversion as described in (Gagnon et al. 2020). The reference ReEDS case chosen is based on the 2020 Standard Scenario Mid-case, which uses the 2020 ReEDS model version (Cole et al. 2020; Ho et al. 2021). All ReEDS model inputs use 2020 Standard Scenarios Mid-case assumptions except for CO2 prices, which are implemented as linearly increasing CO2 price trajectories beginning at $0/tCO2 in 2020 and ending at either $100/tCO2 or $150/tCO2 in 2035 to dive capacity expansion towards a low-carbon system that could support CCS deployment. However, these scenarios also prohibit CCS deployment in this time frame so that resulting price data are not influenced by the deployment and operation of CCS itself. Implementing the ReEDS to PLEXOS conversion tool, PLEXOS is then simulated using the 2035 ReEDS infrastructure for both CO2 price scenarios, with the following model version and setup: PLEXOS Version: 8.2 Solver: Xpress-MP 35.01.01 Mixed integer optimization relative gap 1% System configuration: • Total number of nodes: 134 (consistent with ReEDS balancing areas) • Line losses enforced using piecewise linear approximation • Energy dump was enabled Price data is aggregated to the ISO/RTO level using load-weighted averages. References: Cole, Wesley, Sean Corcoran, Nathaniel Gates, Daniel Mai, Trieu, and Paritosh Das. 2020. “2020 Standard Scenarios Report: A U.S. Electricity Sector Outlook.” NREL/TP-6A20-77442. Golden, CO: National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy21osti/77442.pdf. Gagnon, Pieter, Will Frazier, Elaine Hale, and Wesley Cole. 2020. “Cambium Documentation: Version 2020.” NREL/TP-6A20-78239. National Renewable Energy Lab. (NREL), Golden, CO (United States). https://doi.org/10.2172/1734551. Ho, Jonathan, Jonathon Becker, Maxwell Brown, Patrick Brown, Ilya (ORCID:0000000284917814) Chernyakhovskiy, Stuart Cohen, Wesley (ORCID:000000029194065X) Cole, et al. 2021. “Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020.” NREL/TP-6A20-78195. Golden, CO: National Renewable Energy Laboratory. https://doi.org/10.2172/1788425.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Search for Magnetic Accretion in SW Sextantis Systems

SW Sextantis systems are nova-like cataclysmic variables that have unusual spectroscopic properties, which are thought to be caused by an accretion geometry having part of the mass flux trajectory out of the orbital plane. Accretion onto a magnetic white dwarf is one of the proposed scenarios for these systems. To verify this possibility, we analyzed photometric and polarimetric time-series data for a sample of six SW Sex stars. We report possible modulated circular polarization in BO Cet, SW Sex, and UU Aqr with periods of 11.1, 41.2, and 25.7 minutes, respectively, and less significant periodicities for V380 Oph at 22 minutes and V442 Oph at 19.4 minutes. We confirm previous results that LS Peg shows variable circular polarization. However, we determine a period of 18.8 minutes, which is different from the earlier reported value. We interpret these periods as the spin periods of the white dwarfs. Our polarimetric results indicate that 15% of the SW Sex systems have direct evidence of magnetic accretion. We also discuss SW Sex objects within the perspective of being magnetic systems, considering the latest findings about the demography, formation, and evolution of cataclysmic variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Minimizing Ground Risk in Cellular-Connected Drone Corridors With mmWave Links

Unmanned Aircraft Systems (UASs) have been receiving significant interest and support from academia, industry, and regulatory bodies over the past decade due to their various use cases. To safely integrate UAS operations into the national airspace, particularly overpopulated regions, the risk posed to ground users, buildings, and vehicles due to unmanned aerial vehicle (UAV) flight should be minimized. This risk can be represented by a numerical metric, which we refer to in this article as the “ground risk.” Many UAS applications also depend on the presence of a reliable wireless communication link between the UAV and a control station for the transmission of UAV position, surveillance video, UAV payload commands, and other mission-related data. Such wireless communication requirements also need to be considered in the design of UAS operations. In this article, we consider both these aspects and study the design of nonintersecting trajectories for UAS operations to minimize ground risk, subject to constraints on the wireless signal strength and geometry of the trajectory, specified in terms of: 1) an enclosing cylinder within which the trajectory must lie and 2) an integrated angular change along the UAV's trajectory. The performance of a computationally expensive optimal algorithm is compared with that of a computationally faster heuristic approach within the dense urban environment of Manhattan, NY, USA. Performance evaluation using ray-tracing simulations shows that the heuristic approach performs close to the optimal algorithm at a reduced computation cost. In conclusion, this research can be utilized to make UAS operations safe and reliable and accelerate their adoption.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Climate Change Will Increase Biomass Proportion of Global Forest Carbon Stocks Under an SSP5–8.5 Climate Trajectory

Abstract A large amount of carbon is stored in global forests. However, the fraction of carbon stored as plant biomass versus soil organic carbon (SOC) varies among forest types, and potential changes over the 21st century are uncertain. Here, we used extensive data derived from inventories and remote sensing and Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine the current and 21st century dynamics in the proportion of biomass and SOC across global forests. We found that precipitation, elevation, soil, and wildfire were the primary controls of these differences in carbon pools. Under the SSP5–8.5 climate scenario, CMIP6 models project that the ratio of biomass to ecosystem carbon in global forests will increase across the 21st century, with the largest increases in boreal forests (95 ± 37%) compared to moist tropical forests (16 ± 15%). Changes in forest carbon pools resulting in greater biomass fraction will affect disturbance, and ecosystem carbon and energy balances, all of which interact with the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Carbon cycling across ecosystem succession in a north temperate forest: Controls and management implications

Despite decades of progress, much remains unknown about successional trajectories of carbon (C) cycling in north temperate forests. Drivers and mechanisms of these changes, including the role of different types of disturbances, are particularly elusive. To address this gap, we synthesized decades of data from experimental chronosequences and long-term monitoring at a well-studied, regionally representative field site in northern Michigan, USA. Our study provides a comprehensive assessment of changes in above- and belowground ecosystem components over two centuries of succession, links temporal dynamics in C pools and fluxes with underlying drivers, and offers several conceptual insights to the field of forest ecology. Our first advance shows how temporal dynamics in some ecosystem components are consistent across severe disturbances that reset succession and partial disturbances that slightly modify it: both of these disturbance types increase soil N availability, alter fungal community composition, and alter growth and competitive interactions between short-lived pioneer and longer-lived tree taxa. Further, these changes in turn affect soil C stocks, respiratory emissions, and other belowground processes. Second, we show that some other ecosystem components have effects on C cycling that are not consistent over the course of succession. For example, canopy structure does not influence C uptake early in succession but becomes important as stands develop, and the importance of individual structural properties changes over the course of two centuries of stand development. Third, we show that in recent decades, climate change is masking or overriding the influence of community composition on C uptake, while respiratory emissions are sensitive to both climatic and compositional change. In synthesis, we emphasize that time is not a driver of C cycling; it is a dimension within which ecosystem drivers such as canopy structure, tree and microbial community composition change. Changes in those drivers, not in forest age, are what control forest C trajectories, and those changes can happen quickly or slowly, through natural processes or deliberate intervention. Stemming from this view and a whole-ecosystem perspective on forest succession, we offer management applications from this work and assess its broader relevance to understanding long-term change in other north temperate forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in ν μ CC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Atmospheric pion, kaon, and muon fluxes for sub-orbital experiments

Cosmic rays interacting with the Earth's atmosphere generate extensive air showers, which produce Cherenkov, fluorescence and radio emissions. These emissions are key signatures for detection by ground-based, sub-orbital, and satellite-based telescopes aiming to study high energy cosmic ray and neutrino events. However, detectors operating at ground and balloon altitudes are also exposed to a background of atmospheric charged particles, primarily pions, kaons, and muons, that can mimic or obscure the signals from astrophysical sources. In this work, we use coupled cascade equations to calculate the atmospheric pion, kaon and muon fluxes reaching detectors at various altitudes. Our analysis focuses on energies above 10 GeV, where the influence of the Earth's magnetic field on particle trajectories is minimal. We provide angular and energy-resolved flux estimates and discuss their relevance as background for extensive air shower detection. Furthermore, our results are potentially relevant for interpreting data from current and future balloon-borne experiments such as EUSO-SPB2 and for refining trigger and veto strategies in Cherenkov and fluorescence telescopes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Probing two-path electron quantum interference in strong-field ionization with time-correlation filtering

We report attosecond dynamics in strong-field tunnel ionization are encoded in intricate holographic patterns in the photoelectron momentum distributions. These patterns show the interference between two or more superposed quantum electron trajectories, which are defined by their ionization times and subsequent evolution in the laser field. We determine the ionization time separation between interfering pairs of electron orbits by performing a differential Fourier analysis on the measured momentum spectrum. We identify electron holograms formed by trajectory pairs whose ionization times are separated by less than a single quarter cycle, between a quarter cycle and half cycle, between a half cycle and three fourths of a cycle, and a full cycle apart. We compare our experimental results to the predictions of the Coulomb quantum orbit strong-field approximation (CQSFA) with significant success. We also time-filter the CQSFA trajectory calculations to demonstrate the validity of the technique on spectra with known time correlations. As a general analysis technique, the filter can be applied to all energy- and angularly resolved data sets to recover time correlations between interfering electron pathways, providing an important tool to analyze any strong-field ionization spectra. Moreover, it is independent of theory and can be applied directly to experiments, without the need of a direct comparison with orbit-based theoretical methods.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.

Ye, Fei↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
DOE OSTI2022

High-speed silicon microresonator modulators with high optical modulation amplitude (OMA) at input powers >10 mW

A high-speed silicon photonic microdisc modulator is used with more than 10 mW optical power in the bus waveguide, extending the optical power handling regime used with compact silicon resonant modulators at 1550 nm. We present an experimental study of the wavelength tuning range and biasing path required to shift the resonant frequency to the optimal point versus on chip power. We measure the optical modulation amplitude (OMA) along different biasing trajectories of the microdisc under active modulation and demonstrate an OMA of 4.1 mW with 13.5 mW optical power in the bus waveguide at 20 Gbit/s non-return to zero (NRZ) data modulation.

Wang, Xiaoxi↗