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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 OSTI2022

The Preprocessing of Galaxies in the Early Stages of Cluster Formation in Abell 1882 at z = 0.139

A rare opportunity to distinguish between internal and environmental effects on galaxy evolution is afforded by "SuperGroups," systems that are rich and massive, but include several comparably rich substructures, surrounded by filaments. We present here a multiwavelength photometric and spectroscopic study of the galaxy population in the SuperGroup Abell 1882 (A1882) at z = 0.139, combining new data from the MMT and Hectospec with archival results from the Galaxy And Mass Assembly survey, the Sloan Digital Sky Survey, the Nasa/IPAC Extragalactic Database, the Gemini Multi-Object Spectrograph, and the Galaxy Evolution Explorer. These provide spectroscopic classifications for 526 member galaxies, across wide ranges of local density and velocity dispersion. We identify three prominent filaments along which galaxies seem to be entering the SuperGroup (mostly in E–W directions). A1882 has a well-populated red sequence, containing most galaxies with stellar mass >10 10.5 M Sun , and a pronounced color–density relation even within its substructures. Thus, galaxy evolution responds to the external environment as strongly in these unrelaxed systems as we find in rich and relaxed clusters. From these data, local density remains the primary factor, with a secondary role for distance from the inferred center of the entire structure's potential well. The effects on star formation, as traced by optical and near-UV colors, depend on galaxy mass. We see changes in lower-mass galaxies (M < 10 10.5 M Sun ) at four times the virial radius of major substructures, while the more massive near-UV Green Valley galaxies show low levels of star formation within two virial radii. The suppression of star formation ("quenching") occurs in the infall regions of these structures even before the galaxies enter the denser group environment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Melanoma Among Non-Hispanic Black Americans

Few studies have examined melanoma incidence and survival rates among non-Hispanic black populations because melanoma risk is lower among this group than among non-Hispanic white populations. However, non-Hispanic black people are often diagnosed with melanoma at later stages, and the predominant histologic types of melanomas that occur in non-Hispanic black people have poorer survival rates than the most common types among non-Hispanic white people. Methods: We used the US Cancer Statistics 2001–2015 Public Use Research Database to examine melanoma incidence and 5-year survival among non-Hispanic black US populations. Results: From 2011 through 2015, the overall incidence of melanoma among non-Hispanic black people was 1.0 per 100,000, and incidence increased with age. Although 63.8% of melanomas in nonHispanic black people were of unspecified histology, the most commonly diagnosed defined histologic type was acral lentiginous melanoma (16.7%). From 2001 through 2014, the relative 5- year melanoma survival rate among non-Hispanic black people was 66.2%. Conclusion: Although incidence of melanoma is relatively rare among nonHispanic black populations, survival rates lag behind rates for non-Hispanic white populations. Improved public education is needed about incidence of acral lentiginous melanoma among nonHispanic black people along with increased awareness among health care providers.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Aging Will Amplify the Heat-Related Mortality Risk Under a Changing Climate: Projection for the Elderly in Beijing, China

An aging population could substantially enhance the burden of heat-related health risks in a warming climate because of their higher susceptibility to extreme heat health effects. Here, we project heatrelated mortality for adults 65 years and older in Beijing China across 31 downscaled climate models and 2 representative concentration pathways (RCPs) in the 2020s, 2050s, and 2080s. Under a scenario of medium population and RCP8.5, by the 2080s, Beijing is projected to experience 14,401 heat-related deaths per year for elderly individuals, which is a 264.9% increase compared with the 1980s. These impacts could be moderated through adaptation. In the 2080s, even with the 30% and 50% adaptation rate assumed in our study, the increase in heat-related death is approximately 7.4 times and 1.3 times larger than in the 1980s respectively under a scenario of high population and RCP8.5. These findings could assist countries in establishing public health intervention policies for the dual problems of climate change and aging population. Examples could include ensuring facilities with large elderly populations are protected from extreme heat (for example through back-up power supplies and/or passive cooling) and using databases and community networks to ensure the home-bound elderly are safe during extreme heat events.

age factor↗
DOE OSTI2019

Exploring Intentional Driving Volatility in Naturalistic Driving Environment Prior to Involvement in Unsafe Events

The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as event-based volatility, can be a leading indicator of safety. The research issue is characterizing volatility in instantaneous driving decisions in longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and near-crash events. A rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the SHRP 2, a test set of 9,593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. Twenty-four aggregate and segmented driving volatility measures based on acceleration and vehicular jerk are proposed, capturing variations in intentional and unintentional extreme instantaneous driving decisions. Given the possibility of unobserved heterogeneity/omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. Importantly, intentional volatility in longitudinal negative jerk has more negative consequences than intentional volatility in positive vehicular jerk. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic driving decisions. The study demonstrates the value of big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

Wali, Behram↗
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
DOE OSTI2017

The International Database of Efficient Appliances (IDEA): A new tool to support appliance energy-efficiency deployment

Appliance energy-efficiency programs are a central component of many countries’ energy-policy portfolios. A major barrier to optimal implementation of these programs is lack of data to determine market baselines, assess the potential for cost-effective energy savings, and track markets over time to evaluate and verify program impacts. To address this gap, we have developed the International Database of Efficient Appliances (IDEA), a suite of software tools that automatically gathers data that is currently dispersed across various online sources and compiles it into a unified repository of information on efficiency, price, and features for a diversity of appliances and devices in markets around the world. In this article we describe the framework and functionality of IDEA, and we demonstrate its power as a resource for research and policy development related to appliance energy efficiency. Using IDEA data for refrigerators in China and India, we assess the potential for cost-effective energy savings within each market by computing robust indicators that can also be easily compared across different appliances and markets. We find that significant cost-effective savings are available on both markets. We discuss implications for the development of future energy-efficiency deployment programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions

Large-scale and openly available material science databases are mainly composed of computer simulation results rather than experimental data. Some examples include the Materials Project, Open Quantum Materials Database, and Open Catalyst 2022. Unfortunately, building large-scale experimental databases remains challenging due to the difficulties in consolidating locally distributed datasets. In this work, focusing on the catalysis literature of CO 2 reduction reactions (CO 2 RRs), we present a machine learning (ML)-based protocol for selecting highly relevant papers and extracting important experimental data. First, we report a document embedding method (Doc2Vec) for collecting papers of greatest relevance to the specific target domain, which yielded 3154 CO 2 RR-related papers from six publishers. Next, we developed named entity recognition (NER) models to extract twelve entities related to material names (catalyst, electrolyte, etc.) and catalytic performance (Faradaic efficiency, current density, etc.). Further, among several tested models, the MatBERT-based approach achieved the highest accuracy, with an average F1-score of 90.4% and an F1-score of 95.2% in a boundary relaxation evaluation scheme. The accurate and accelerated NER-based data extraction from a large volume of catalysis literature enables temporal trend analyses of the CO 2 RR catalysts, products, and performances, revealing the potentially effective material space in CO 2 RRs. While this work demonstrates the effectiveness of our ML-based text mining methods for specifically CO 2 RR literature, the methods and approach are applicable to and may be used to accelerate the development of other catalytic chemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

First transmission of electrons and ions through the KATRIN beamline

The Karlsruhe Tritium Neutrino (KATRIN) experiment is a large-scale effort to probe the absolute neutrino mass scale with a sensitivity of 0.2 eV (90% confidence level), via a precise measurement of the endpoint spectrum of tritium β-decay. This work documents several KATRIN commissioning milestones: the complete assembly of the experimental beamline, the successful transmission of electrons from three sources through the beamline to the primary detector, and tests of ion transport and retention. In the First Light commissioning campaign of autumn 2016, photoelectrons were generated at the rear wall and ions were created by a dedicated ion source attached to the rear section; in July 2017, gaseous 83m Kr was injected into the KATRIN source section, and a condensed 83m Kr source was deployed in the transport section. In this paper we describe the technical details of the apparatus and the configuration for each measurement, and give first results on source and system performance. In conclusion, we have successfully achieved transmission from all four sources, established system stability, and characterized many aspects of the apparatus.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler

Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate. We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning

Abstract Motivation Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Results Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM’s native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. Availability and implementation SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Structural Evaluation of Complex Subcomponents Manufactured by Large Scale Extrusion Deposition of Carbon Fiber Reinforced ABS

The large scale extrusion deposition process has recently been commercialized in the form of additive manufacturing machines such as the BAAM from Cincinnati Inc., which enables layered production of chopped fiber reinforced thermoplastic polymer systems. The application of this process to the automotive market is being pursued by Local Motors to enable rapid design to market cycles and build on-demand capability for a wide range of vehicle styles from a distributed network of micro-factories. This endeavor will require an understanding of the structural performance of components with complex geometries, where the properties are known to vary as a function of printing conditions, as well as orientation and shape. In order to rapidly build a database of structural performance, a beam specimen with outer dimensions of 914 x 305 x 64 mm3 was selected to be subjected to bending, torsion, and shear loading with full field displacement measurement by digital image correlation (DIC). A total of 25 different specimen configurations were designed including variation of print geometry, reinforcement by foam filling, and overwrapping with advanced composites. This work documents the results of the study and presents the relative performance of the structures in addition to the approach to simulation that is being initiated to serve as a framework for efficient design of structures manufactured in this way. The results are presented as a table of structural efficiency including stiffness and strength as functions of mass for each design. Additional results will also be provided in the form of surface displacements from DIC; future work will utilize this data to better optimize simulations for predicting structural performance of articles manufactured by this process.

Bedsole, Robert↗
DOE OSTI2013

Energy Efficiency and Minimum Standards: a Market Analysis of Recent Changes in Appliance Eenergy Efficiency Standards in the United States

The objective of this paper is to examine the effect of energy efficiency standards on prices and market shares of major household appliances in the United States. The household appliance market is analyzed using point-of-sale (POS) retail data of clothes washers and dryers from NPD Group. Energy efficiency information for each clothes washer model was obtained from the Federal Trade Commission (FTC) appliance energy database. Using these data, we conclude that energy efficiency standards had an overall negative impact on clothes washer price, and products with a mid-low efficiency rating experienced the greatest short run price drop following the January 2004 and 2007 new standard effective dates. In neither instance is there any evidence of an increase in prices of affected appliances. In both cases we analyse the price effects of the standard both relative to dryers as a counterfactual group. Additionally, we show that market shares for clothes washers shifted towards greater efficiency over the course of the period between 2002 and 2011.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Study of Gamma-ray Production from Neutron-induced Activity on Spacecraft for DIORAMA

The Distributed Infrastructure Offering Real{Time Access to Modeling and Analysis (DIORAMA) software provides performance modeling capabilities of the United States Nuclear Detonation Detection System (USNDS) with a focus on the characterization of Space{Based Nuclear Detonation Detection (SNDD) instrument performance [1]. A study was conducted investigating the neutron activation on spacecraft caused by cosmic{ray and trapped proton{induced spallation, and by direct irradiation from hypothetical nuclear detonations as a background component to be incorporated into DIORAMA. The neutron activation background study amasses a database of pre-calculated Monte Carlo data that reflects the delayed gamma-ray production from activated nuclei as a function of incident cosmic or neutron flux, incident energy, material volume, etc. CINDER2008 coupled with MCNPX and the Activation Analysis Tools in the Activation in Accelerator Radiation Environments (AARE) distribution along with a suite of Python scripts are used to calculate and assemble the Monte Carlo database [2{6]. The neutron activation background study improves the fidelity of DIORAMA by providing the neutron-induced activation component to the background of the instruments on-board the spacecraft, and has implications for future instrument design studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1994

Dynamics Explorer 2 observations of equatorial thermospheric winds and temperatures: Local time and longitudinal dependences

We have analyzed a large database of Dynamics Explorer 2 (DE 2) measurements of equatorial (less than + or - 9 degs latitude) thermospheric neutral temperatures and horizontal wind components in the altitude range from 200 to 400 km for quiet and active geomagnetic conditions (Kp less than 3 and Kp greater than 3, respectively). Local time variations and longitudinal dependences on the duskside, dayside, and nightside have been separately examined. The dayside hourly averaged neutral zonal winds are relatively insensitive to variations in geomagnetic activity and appear to show an inverse dependence (lower zonal wind speed for higher Kp) in the midnight-dawn local time sector. The duskside neutral temperatures show a region of enhancement (by approximately 60 K) over the South Atlantic magnetic anomaly. The quiet time zonal winds are weaker than the corresponding active time zonal winds on the dayside near the South Atlantic magnetic anomaly. Enhanced ionization in the region, which increases the ion drag on the neutral gas, is suggested as the causal mechanism for these effects. The DE 2 averaged measurements have also been compared with MSIS-86/HWM90 and VSH models. The data and models are in good general agreement, though, the MSIS-86/HWM90 model predicts stronger dayside zonal winds than are observed under active conditions and the VSH model appears to underestimate the magnitude of the dayside zonal winds for both high and low Kp cases.

Wu, Q↗
NASA NTRS1999

A Neural Network Aero Design System for Advanced Turbo-Engines

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Sanz, Jose M.↗
NASA NTRS2001

Development of Core Cladding Fabrication Techniques for Phase I Fission Propulsion Systems

Phase I fission propulsion systems focus on safety, timely development, and affordability. Prototype and flight units can be tested at full thrust, using resistance heaters to closely simulate heat from a fission reaction. In Phase I ground testing, one goal is to establish a reliable and affordable manufacturing technique for fabricating a flight-like core. A refractory metal (Mo) has been suggested for the core substrate, primarily due to the existence of a significant database for Mo/LJ02 fuel. The core can be fabricated by bundling Mo tubes with a bonding system that meets preliminary test goals. These criteria include materials compatibility, ability to maintain thermal and structural integrity during 10,000 hours of operation, and fabrication with existing facilities. This paper describes an effort to investigate several fabrication techniques in a cost-effective manner. First, inexpensive materials were tested at low temperatures to determine the relative effectiveness of such techniques as welding, brazing, plating, and vacuum plasma spraying (VPSing). Promising techniques were chosen for further evaluation, including thermal and structural studies, using ceramic tubing at intermediate temperatures. The most desirable technique will be tested on actual Mo tubing at anticipated operating temperatures. This work is being performed by the National Aeronautics & Space Administration (NASA) at George C. Marshall Space Flight Center (MSFC), Los Alamos National Laboratory (LANL), and Advanced Methods & Materials (AMM), Inc.

Salvail, Patrick G.↗
NASA NTRS2010

Widely Tunable Mode-Hop-Free External-Cavity Quantum Cascade Laser

The external-cavity quantum cascade laser (EC-QCL) system is based on an optical configuration of the Littrow type. It is a room-temperature, continuous wave, widely tunable, mode-hop-free, mid-infrared, EC-QCL spectroscopic source. It has a single-mode tuning range of 155 cm(exp -1) (approximately equal to 8% of the center wavelength) with a maximum power of 11.1 mW and 182 cm(exp -1) (approximately equal to 15% of the center wavelength), and a maximum power of 50 mW as demonstrated for 5.3 micron and 8.4 micron EC-QCLs, respectively. This technology is particularly suitable for high-resolution spectroscopic applications, multi-species tracegas detection, and spectroscopic measurements of broadband absorbers. Wavelength tuning of EC-QCL spectroscopic source can be implemented by varying three independent parameters of the laser: (1) the optical length of the gain medium (which, in this case, is equivalent to QCL injection current modulation), (2) the length of the EC (which can be independently varied in the Rice EC-QCL setup), and (3) the angle of beam incidence at the diffraction grating (frequency tuning related directly to angular dispersion of the grating). All three mechanisms of frequency tuning have been demonstrated and are required to obtain a true mode-hop-free laser frequency tuning. The precise frequency tuning characteristics of the EC-QCL output have been characterized using a variety of diagnostic tools available at Rice University (e.g., a monochromator, FTIR spectrometer, and a Fabry-Perot spectrometer). Spectroscopic results were compared with available databases (such as HITRAN, PNNL, EPA, and NIST). These enable precision verification of complete spectral parameters of the EC-QCL, such as wavelength, tuning range, tuning characteristics, and line width. The output power of the EC-QCL is determined by the performance of the QC laser chip, its operating conditions, and parameters of the QC laser cavity such as mirror reflectivity or intracavity losses. In order to maximize the output power, an analysis and optimization of the EC laser parameters has been performed. The parameters of the beam emitted from the gain medium, such as divergence angle, beam profile, and astigmatism, have been investigated. The gain medium has been fully characterized before and after each stage of modification. The main modification steps are coating one facet of the gain chip with a high reflectivity mirror and the other facet with an anti-reflection layer. Then the chip is mounted in the EC-QCL. The optomechanical design has been reviewed and improved to provide for precise collimation of the strongly divergent beam of the QCL and the tuning diffraction grating.

Wysocki, Gerard↗

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