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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Structure-Spin-Transport Anomaly in Quasi-One-Dimensional Ba9Fe3Te15 under High Pressure

Recently, a series of novel compounds Ba3MX5 (M= Fe, Ti, V; X=Se, Te) with hexagonal crystal structures composed of quasi-1-dimensional (1D) magnetic chains has been synthesized by our research team using high-pressure and high-temperature methods. The initial hexagonal phases persist to the maximum achievable pressure, while spin configurations and magnetic interactions may change dramatically as a result of considerable reductions in interchain separations upon pressurization. These compounds therefore offer unique possibilities for studying the evolution of intrinsic electronic structures in quasi-1D magnetic systems. Here we present a systematic investigation of Ba9Fe3Te15, in which the interchain separations between trimerized 1D chains (~10.2 Å) can be effectively modulated by external high pressure. The crystal structure especially along the 1D chains exhibits an abnormal expansion at ~5GPa, which accompanies trimerization entangled anomalous mixed-high-low spin transition. An insulator-metal transition has been observed under high pressure as a result of charge-transfer gap closing. Pressure-induced superconductivity emerges at 26GPa, where the charge-transfer gap fully closes, 3D electronic configuration forms and local spin fully collapses.

Zhang, Jun↗
DOE OSTI2024

Phase-field modeling of stored-energy-driven grain growth with intra-granular variation in dislocation density

Abstract We present a phase-field (PF) model to simulate the microstructure evolution occurring in polycrystalline materials with a variation in the intra-granular dislocation density. The model accounts for two mechanisms that lead to the grain boundary migration: the driving force due to capillarity and that due to the stored energy arising from a spatially varying dislocation density. In addition to the order parameters that distinguish regions occupied by different grains, we introduce dislocation density fields that describe spatial variation of the dislocation density. We assume that the dislocation density decays as a function of the distance the grain boundary has migrated. To demonstrate and parameterize the model, we simulate microstructure evolution in two dimensions, for which the initial microstructure is based on real-time experimental data. Additionally, we applied the model to study the effect of a cyclic heat treatment (CHT) on the microstructure evolution. Specifically, we simulated stored-energy-driven grain growth during three thermal cycles, as well as grain growth without stored energy that serves as a baseline for comparison. We showed that the microstructure evolution proceeded much faster when the stored energy was considered. A non-self-similar evolution was observed in this case, while a nearly self-similar evolution was found when the microstructure evolution is driven solely by capillarity. These results suggest a possible mechanism for the initiation of abnormal grain growth during CHT. Finally, we demonstrate an integrated experimental-computational workflow that utilizes the experimental measurements to inform the PF model and its parameterization, which provides a foundation for the development of future simulation tools capable of quantitative prediction of microstructure evolution during non-isothermal heat treatment.

Materials Science↗
DOE OSTI2025

Runaway electron-induced plasma facing component damagein tokamaks

This Roadmap article addresses the critical and multifaceted challenge of plasma facing component (PFC) damage caused by runaway electrons (REs) in tokamaks, a phenomenon that poses a significant threat to the viability and longevity of future fusion reactors such as ITER and DEMO. The dramatically increased RE production expected in future high-current tokamaks makes it very difficult to avoid or mitigate REs in such devices when a plasma discharge terminates abnormally. Preventing damage from the intense localised heat loads they can cause requires a holistic approach that considers plasma, REs and PFC damage. Despite decades of progress in understanding the physics of REs and the thermomechanical response of PFCs separately, their complex interplay remains poorly understood. This document aims to initiate a coordinated, interdisciplinary approach to bridge this gap by reviewing experimental evidence, advancing diagnostic capabilities, and improving modelling tools across different scales, dimensionalities, and fidelities. Key topics include RE beam formation and transport, damage mechanisms in both brittle and metallic PFCs, and observed effects in major facilities such as JET, DIII-D, WEST and EAST. The Roadmap emphasises the urgency of predictive, high-fidelity modelling validated against well-diagnosed controlled experiments, particularly in the light of recent changes in ITER’s wall material strategy and the growing importance of private sector fusion initiatives. Each section of the Roadmap article is written to provide a concise overview of one area of this multidisciplinary subject, with an assessment of the status, a look at current and future challenges, and a brief summary. The ultimate goal of this initiative is to guide future mitigation strategies and design resilient components that can withstand the intense localised loads imposed by REs, thus ensuring the safe and sustainable operation of the next generation of fusion power plants.

Ratynskaia, Svetlana [KTH Royal Inst. of Technolog↗
DOE OSTI2022

Review of the gas breakdown physics and nanomaterial-based ionization gas sensors and their applications

Ionization gas sensors are ubiquitous tools that can monitor desired gases or detect abnormalities in real time to protect the environment of living organisms or to maintain clean and/or safe environment in industries. The sensors’ working principle is based on the fingerprinting of the breakdown voltage of one or more target gases using nanostructured materials. Fundamentally, nanomaterial-based ionization-gas sensors operate within a large framework of gas breakdown physics; signifying that an overall understanding of the gas breakdown mechanism is a crucial factor in the technological development of ionization gas sensors. Moreover, many studies have revealed that physical properties of nanomaterials play decisive roles in the gas breakdown physics and the performance of plasma-based gas sensors. Based on this insight, this review provides a comprehensive description of the foundation of both the gas breakdown physics and the nanomaterial-based ionization-gas-sensor technology, as well as introduces research trends on nanomaterial-based ionization gas sensors. The gas breakdown is reviewed, including the classical Townsend discharge theory and modified Paschen curves; and nanomaterial-based-electrodes proposed to improve the performance of ionization gas sensors are introduced. The secondary electron emission at the electrode surface is the key plasma–surface process that affects the performance of ionization gas sensors. Finally, we present our perspectives on possible future directions.

nanomaterial-based gas breakdown characteristics↗
DOE OSTI2022

Muon track reconstruction in a segmented bolometric array using multi-objective optimization

Recent advances in segmented solid-state detector arrays for rare-event searches have allowed the technology to approach the ton-scale in detector mass and the scale of meters in size. Often focused around searches for neutrinoless double-beta decay or direct dark matter detection, such experiments also have the capability to search for exotic particles that leave track-like signatures across their volume. However, the segmented nature of such detector arrays often sets the spatial resolution and makes the problem of reconstructing track-like paths non-trivial. Here, in this paper, we present an algorithm that improves reconstruction of track-like events in segmented detectors using multi-objective optimization — a computational technique that optimizes more than one cost function at a time without specifying a quantitative weighting between them. Such a technique allows the reconstruction of tracks through a detector and the determination of path-lengths through individual elements. When combined with the reconstructed energy depositions in each element this allows for a calculation of the stopping power of track-like particles and opens the door to searches for particles with abnormal stopping power like monopoles or lightly-ionizing particles (LIPs). Results are presented which evaluate the precision of the reconstruction tools as they currently stand against Monte Carlo generated data. The algorithm is presented in the context of the CUORE experiment, but has applications to other segmented calorimeter detectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Cyclone Fani: the tug-of-war between regional warming and anthropogenic aerosol effects

Before Cyclone Amphan took place in 2020, Cyclone Fani (May 2019) is the strongest pre-monsoon cyclone in the Bay of Bengal (BOB) since 1991, killing 90 people in eastern India and Bangladesh while causing US$1.81 billion of damages. Fani developed during a period of high concentration of anthropogenic aerosols in the BOB with abnormally high sea surface temperature (SST), thereby presenting an opportunity to understand the compound effects of atmospheric aerosols and regional climate warming on a tropical cyclone. A quantitative attribution analysis was conducted using the Weather Research and Forecasting model with chemistry (WRF-Chem) run at the convection-permitting (4 km) grid spacing, accompanied by an ensemble of coarser-resolution simulations to quantify the uncertainty. The removal of post-1990 trends in the tropospheric variables and SST from WRF-Chem's initial conditions (IC) and boundary conditions (BC, including the lateral and lower boundary conditions) resulted in a reduction of cyclone precipitation by about 51% during the 5 d of April 28-May 2. The removal of tropospheric warming shows approximately twice as strong an effect on Fani (39% reduction in precipitation) as that of SST warming (22% reduction). When aerosol's direct and indirect effects were removed from the simulations, i.e., no aerosol influence on radiation and cloud microphysics, Fani initially strengthened but later weakened, as measured by geopotential height and precipitation amounts. These results suggest that aerosol and its interaction with the atmosphere acted to mitigate the strengthening effect of anthropogenic warming on Fani, but was not strong enough to entirely counteract it. Although the ensemble of coarser simulations appears to overestimate Cyclone Fani in terms of precipitation, the direction of the effects is in agreement with that obtained from the 4 km simulations. Given the increasing anthropogenic aerosols in the BOB, future attribution studies using more sophisticated dynamical aerosol models on BOB tropical cyclones are urged.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Projected evolution of droughts and human exposure in the contiguous United States under SSP5-8.5: a regional downscaling perspective

The increasingly unpredictable and extreme weather patterns under a warming climate underscore the urgency of accurate regional assessments of future drought risk. This study evaluates the projected drought evolution in the contiguous United States under the high-emission shared socioeconomic pathway 5–8.5 climate scenario for the coming decades. Using a multi-model ensemble of six Coupled Model Intercomparison Project Phase 6 global climate models combined with dynamical downscaling techniques, we analyzed near-term (2020–2039) and mid-term (2040–2059) drought patterns using the self-calibrating palmer drought severity index (ScPDSI), the standardized precipitation index (SPI-12), and the Standardized Precipitation-Evapotranspiration Index (SPEI-12). Results reveal a widespread increase in abnormally dry (D0) and moderate drought (D1) conditions, particularly in urban areas, while severe (D2), extreme (D3), and exceptional (D4) droughts are expected to become less common in many regions. Meanwhile, persistent and intensifying droughts are projected in the western and southwestern U.S., driven by long-term soil moisture deficits. The ScPDSI projects that 1.1 million urban residents will be affected by D0 conditions in 2050, while SPI-12 suggests a decrease in the total affected populations after 2040. ScPDSI indicates prolonged droughts in the West, and SPI-12 captures transient variability. Although the total drought-exposed population is expected to decrease, urban areas will continue to bear a greater burden, particularly for mild droughts (D0, D1). These findings highlight a shift toward more frequent mild droughts, fewer severe droughts, and persistent drying in the Southwest, emphasizing the need for region-specific adaptation strategies.

CMIP6↗
DOE OSTI2024

PIP-II cryoplant non thermal cycling updates

The Proton Improvement Plan-II (PIP-II) is a crucial upgrade to the Fermilab accelerator complex, featuring a new 800-MeV Superconducting Radio-Frequency (SRF) linear accelerator (LINAC) with 23 cryomodules operating at 2 K. The LINAC thermohydraulic conditions are satisfied by the cryogenic subsystems: Cryogenic Distribution System (CDS), a helium refrigerator cold box (CB), a warm compression station (WCS) and a helium recovery system (RSYS). The accelerator has a strict requirement of non-thermal cycling of the LINAC cryomodules during planned and unplanned subsystem outages. This paper presents an integrated reliability analysis of the PIPII cryoplant. The study evaluates both normal and abnormal operating modes, with a focus on identifying integrated scenarios that put subsystems components under stress. The conclusions of this study will help build redundancy to mitigate LINAC thermal cycling risks during planned and unplanned subsystem outages.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Plasma phosphorylated tau217 strongly associates with memory deficits in the Alzheimer’s disease spectrum

Abstract Plasma phosphorylated tau (p-tau) biomarkers open unprecedented opportunities for identifying carriers of Alzheimer’s disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment. To address this question, we included 103 cognitively unimpaired elderly and 40 cognitively impaired, amyloid-β-positive individuals from the TRIAD cohort, in addition to 336 cognitively unimpaired and 216 cognitively impaired, amyloid-β-positive older adults from the BioFINDER-2 cohort. Participants had tau PET scans, amyloid PET scans or amyloid CSF, p-tau217, p-tau181 and p-tau231 blood measures, structural T1-MRI and cognitive assessments. In this cross-sectional study, we used regression models and correlation analyses to assess the relationship between plasma biomarkers and cognitive scores. Furthermore, we applied receiver operating characteristic curves to assess cognitive impairment across plasma biomarkers. Finally, we categorized participants into amyloid (A), p-tau (T1) and tau PET (T2) positive (+) or negative (−) profiles and ran non-parametric comparisons to assess differences across cognitive domains. We found that plasma p-tau217 was more associated with cognitive performance than p-tau181 and p-tau231 and that this relationship was particularly strong for memory scores (TRIAD: βp-tau217 = −0.53, βp-tau181 = −0.35 and βp-tau231 = −0.24; BioFINDER-2: βp-tau217 = −0.52, βp-tau181 = −0.24 and βp-tau231 = −0.29). Associations in amyloid-β-positive participants resembled these results, but other cognitive scores also showed strong associations in cognitively impaired individuals. Moreover, plasma p-tau217 outperformed plasma p-tau181 and plasma p-tau231 in identifying memory impairment (area under the curve values for TRIAD: p-tau217 = 0.86, p-tau181 = 0.77 and p-tau231 = 0.75; and for BioFINDER-2: p-tau217 = 0.86, p-tau181 = 0.76 and p-tau231 = 0.81) and in identifying executive function impairment only in the BioFINDER-2 cohort (p-tau217 = 0.82, p-tau181 = 0.76 and p-tau231 = 0.76). Lastly, we showed that subtle memory deficits were present in A+T1+T2− participants for plasma p-tau217 (P = 0.007) and plasma p-tau181 (P = 0.01) in the TRIAD cohort and for all biomarkers across cognitive domains in A+T1+T2− and A+T1+T2− individuals (P < 0.001 in all) in the BioFINDER-2 cohort. The A+T1+T2− individuals showed cognitive deficits in both cohorts (P < 0.001 in all). Together, our results suggest that plasma p-tau217 stands out as a biomarker capable of identifying memory deficits attributable to Alzheimer’s disease and that memory impairment certainly occurs in amyloid-β- and plasma p-tau-positive individuals who have no significant amounts of tau in the neocortex.

Neurosciences & Neurology↗
DOE OSTI2022

Characterization of Classical Sheep Scrapie in White-tailed Deer after Experimental Oronasal Exposure

Abstract Background Classic scrapie is a prion disease of sheep and goats that is associated with accumulation of abnormal prion protein (PrPSc) in the central nervous and lymphoid tissues. Chronic wasting disease (CWD) is the prion disease of cervids. This study was conducted to determine the susceptibility of white-tailed deer (WTD) to the classic scrapie agent. Methods We inoculated WTD (n = 5) by means of a concurrent oral/intranasal exposure with the classic scrapie agent from sheep or oronasally with the classic scrapie agent from goats (n = 6). Results All deer exposed to the agent of classic scrapie from sheep accumulated PrPSc. PrPSc was detected in lymphoid tissues at preclinical time points, and necropsies in deer 28 months after inoculation showed clinical signs, spongiform lesions, and widespread PrPSc in neural and lymphoid tissues. Western blots on samples from the brainstem, cerebellum, and lymph nodes of scrapie-infected WTD have a molecular profile similar to CWD and distinct from samples from the cerebral cortex, retina, or the original classic scrapie inoculum. There was no evidence of PrPSc in any of the WTD inoculated with classic scrapie prions from goats. Conclusions WTD are susceptible to the agent of classic scrapie from sheep, and differentiation from CWD may be difficult.

Immunology↗
DOE OSTI2023

The Efficacy and Safety of an Intra-articular Dual-Acting Antibacterial Agent (TNP-2092) for Implant Infection–Associated Methicillin-Resistant Staphylococcus aureus

Abstract Owing to the presence of microbial biofilm on the implant, the eradication of biofilm-associated infections poses a challenge for antibiotic therapies. The study aimed to investigate the efficacy and safety of the novel antibiotic agent TNP-2092 in the context of implant infections. In vivo, rats with periprosthetic joint infection (PJI) treated with antibiotics showed an increase in body weight and decrease in swelling, temperature, and width of knee, compared with the control group. Meanwhile, inflammatory markers in synovium and serum were decreased in the TNP-2092 group, consistent with the pathological results. Moreover, TNP-2092 was effective in eliminating bacteria and disruption biofilm formation, and further alleviated the abnormal bone absorption and reactive bone changes around the prosthesis. In conclusion, intra-articular injection of TNP-2092 is safe and effective in treating knee PJI in a rat model. The study provides a foundation for the future utilization of TNP-2092 in the management of implant-related infections.

Dai, Tianyu↗
DOE OSTI2023

Galactosylation of xyloglucan is essential for the stabilization of the actin cytoskeleton and endomembrane system through the proper assembly of cell walls

Abstract Xyloglucan, a major hemicellulose, interacts with cellulose and pectin to assemble primary cell walls in plants. Loss of the xyloglucan galactosyltransferase MURUS3 (MUR3) leads to the deficiency of galactosylated xyloglucan and perturbs plant growth. However, it is unclear whether defects in xyloglucan galactosylation influence the synthesis of other wall polysaccharides, cell wall integrity, cytoskeleton behaviour, and endomembrane homeostasis. Here, we found that in mur3-7 etiolated seedlings cellulose was reduced, CELLULOSE SYNTHASE (CESA) genes were down-regulated, the density and mobility of cellulose synthase complexes (CSCs) were decreased, and cellulose microfibrils become discontinuous. Pectin, rhamnogalacturonan II (RGII), and boron contents were reduced in mur3-7 plants, and B-RGII cross-linking was abnormal. Wall porosity and thickness were significantly increased in mur3-7 seedlings. Endomembrane aggregation was also apparent in the mur3-7 mutant. Furthermore, mutant seedlings and their actin filaments were more sensitive to Latrunculin A (LatA) treatment. However, all defects in mur3-7 mutants were substantially restored by exogenous boric acid application. Our study reveals the importance of MUR3-mediated xyloglucan galactosylation for cell wall structural assembly and homeostasis, which is required for the stabilization of the actin cytoskeleton and the endomembrane system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Ex vivo to in vivo model of malignant peripheral nerve sheath tumors for precision oncology

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1). To address the critical need for novel therapeutics in MPNST, we aimed to establish an ex vivo 3D platform that accurately captured the genomic diversity of MPNST and could be utilized in a medium-throughput manner for drug screening studies to be validated in vivo using patient-derived xenografts (PDX). Genomic analysis was performed on all PDX-tumor pairs. Selected PDX were harvested for assembly into 3D microtissues. Based on prior work in our labs, we evaluated drugs (trabectedin, olaparib, and mirdametinib) ex vivo and in vivo. For 3D microtissue studies, cell viability was the endpoint as assessed by Zeiss Axio Observer. For PDX drug studies, tumor volume was measured twice weekly. Bulk RNA sequencing was performed to identify pathways enriched in cells. We developed 13 NF1-associated MPNST-PDX and identified mutations or structural abnormalities in NF1 (100%), SUZ12 (85%), EED (15%), TP53 (15%), CDKN2A (85%), and chromosome 8 gain (77%). We successfully assembled PDX into 3D microtissues, categorized as robust (>90% viability at 48 h), good (>50%), or unusable (<50%). We evaluated drug response to “robust” or “good” microtissues, namely MN-2, JH-2-002, JH-2-079-c, and WU-225. Drug response ex vivo predicted drug response in vivo, and enhanced drug effects were observed in select models. These data support the successful establishment of a novel 3D platform for drug discovery and MPNST biology exploration in a system representative of the human condition.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A highly efficient atomically thin curved PdIr bimetallene electrocatalyst

Abstract The multi-metallene with an ultrahigh surface area has great potential in precise tuning of surface heterogeneous d-electronic correlation by surface strain effect for the distinctive surface electronic structure, which is a brand new class of promising 2D electrocatalyst for sustainable energy device application. However, achieving such an atomically thin multi-metallene still presents a great challenge. Herein, we present a new synthetic method for an atomic-level palladium-iridium (PdIr) bimetallene with an average thickness of only ∼1.0 nm for achieving superior catalysis in the hydrogen evolution reaction (HER) and the formic acid oxidation reaction (FAOR). The curved PdIr bimetallene presents a top-ranked high electrochemical active area of 127.5 ± 10.8 m2 gPd+Ir−1 in the reported noble alloy materials, and exhibits a very low overpotential, ultrahigh activity and improved stability for HER and FAOR. DFT calculation reveals that the PdIr bimetallene herein has a unique lattice tangential strain, which can induce surface distortion while concurrently creating a variety of concave-convex featured micro-active regions formed by variously coordinated Pd sites agglomeration. Such a strong strain effect correlates the abnormal on-site active 4d10-t2g-orbital Coulomb correlation potential and directly elevates orbital-electronegativity exposure within these active regions, resulting in a preeminent barrier-free energetic path for significant enhancement of FAOR and HER catalytic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

Abstract Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Preliminary Thermoluminescent Dosimeter Glow Curve Analysis with Automated Glow Peak Identification for LiF Mg,Ti

When appropriately analyzed, thermoluminescent dosimeter glow curve analysis allows for improved quantification of thermoluminescent material behavior while flagging abnormalities. The mathematical separation of a glow curve into contributions from energetically unique trap states, or glow curve analysis, may be used to remove undesired effects of signal fading for complex materials. A generalized glow curve analysis software for the separation of glow curves is presented in this paper. Written in C ++ , the software uses the first-order kinetics model with automatic peak identification. The automatic identification of peaks is achieved through a unique peak-finding algorithm. Here, the program was performance tested using experimental glow curve data from LiF:Mg,Ti, and comparative results are presented.

47 OTHER INSTRUMENTATION↗