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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

On the computation of moments in the Super-Transition-Arrays model for radiative opacity calculations

In the Super-Transition-Array statistical method for the computation of radiative opacity of hot dense matter, the moments of the absorption or emission features involve partition functions with reduced degeneracies, occurring through the calculation of averages of products of subshell populations. Here, in the present work, we discuss several aspects of the computation of such peculiar partition functions, insisting on the precautions that must be taken in order to avoid numerical difficulties. In a previous work, we derived a formula for supershell partition functions, which takes the form of a functional of the distribution of energies within the supershell and allows for fast and accurate computations, truncating the number of terms in the expansion. The latter involves coefficients for which we obtained a recursion relation and an explicit formula. We show that such an expansion can be combined with the recurrence relation for shifted partition functions. We also propose, neglecting the effect of fine structure as a first step, a positive-definite formula for the Super-Transition-Array moments of any order, providing an insight into the asymmetry and sharpness of the latter. The corresponding formulas are free of alternating sums. Several ways to speed up the calculations are also presented.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Systematic Review of Radiation Therapy Toxicity Reporting in Randomized Controlled Trials of Rectal Cancer: A Comparison of Patient-Reported Outcomes and Clinician Toxicity Reporting

The use of multimodal treatments for rectal cancer has improved cancer-related outcomes but makes monitoring toxicity challenging. Optimizing future radiation therapy regimens requires collection and publication of detailed toxicity data. This review evaluated the quality of toxicity information provided in randomized controlled trials (RCTs) of radiation therapy in rectal cancer and focused on the difference between clinician-reported and patient-reported toxicity. Medline, EMBASE, and the Cochrane Library were searched (January 1995-July 2013) for RCTs reporting late toxicity in patients treated with regimens including preoperative (chemo)radiation therapy. Data on toxicity measures and information on toxicity reported were extracted using Quantitative Analyses of Normal Tissue Effects in the Clinic recommendations. International Society for Quality of Life Research standards on patient-reported outcomes (PROs) were used to evaluate the quality of patient-reported toxicity. Twenty-one RCT publications met inclusion criteria out of 4144 articles screened. All PRO studies reported higher rates of toxicity symptoms than clinician-reported studies and reported on a wider range and milder symptoms. No clinician-reported study published data on sexual dysfunction. Of the clinician-reported studies, 55% grouped toxicity data related to an organ system together (eg “Bowel”), and 45% presented data only on more-severe (grade ≥3) toxicity. In comparison, all toxicity grades were reported in 79% of PRO publications, and all studies (100%) presented individual symptom toxicity data (eg bowel urgency). However, PRO reporting quality was variable. Only 43% of PRO studies presented baseline data, 28% did not use any psychometrically validated instruments, and only 29% of studies described statistical methods for managing missing data. Analysis of these trials highlights the lack of reporting standards for adverse events and reveals the differences between clinician and patient reporting of toxicity. Recommendations for improving the quality of adverse event data collection are provided, with the aim of improving critical appraisal of outcomes for future studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Validating a Local Failure Risk Stratification for Use in Prospective Studies of Adjuvant Radiation Therapy for Bladder Cancer

Purpose: To inform prospective trials of adjuvant radiation therapy (adj-RT) for bladder cancer after radical cystectomy, a locoregional failure (LF) risk stratification was proposed. This stratification was developed and validated using surgical databases that may not reflect the outcomes expected in prospective trials. Our purpose was to assess sources of bias that may affect the stratification model's validity or alter the LF risk estimates for each subgroup: time bias due to evolving surgical techniques; trial accrual bias due to inclusion of patients who would be ineligible for adj-RT trials because of early disease progression, death, or loss to follow-up shortly after cystectomy; bias due to different statistical methods to estimate LF; and subgrouping bias due to different definitions of the LF subgroups. Methods and Materials: The LF risk stratification was developed using a single-institution cohort (n=442, 1990-2008) and the multi-institutional SWOG 8710 cohort (n=264, 1987-1998) treated with radical cystectomy with or without chemotherapy. We evaluated the sensitivity of the stratification to sources of bias using Fine-Gray regression and Kaplan-Meier analyses. Results: Year of radical cystectomy was not associated with LF risk on univariate or multivariate analysis after controlling for risk group. By use of more stringent inclusion criteria, 26 SWOG patients (10%) and 60 patients from the single-institution cohort (14%) were excluded. Analysis of the remaining patients confirmed 3 subgroups with significantly different LF risks with 3-year rates of 7%, 17%, and 36%, respectively (P<.01), nearly identical to the rates without correcting for trial accrual bias. Kaplan-Meier techniques estimated higher subgroup LF rates than competing risk analysis. The subgroup definitions used in the NRG-GU001 adj-RT trial were validated. Conclusions: These sources of bias did not invalidate the LF risk stratification or substantially change the model's LF estimates.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Multigrid deflation for Lattice QCD

Computing the trace of the inverse of large matrices is typically addressed through statistical methods. Deflating out the lowest eigenvectors or singular vectors of the matrix reduces the variance of the trace estimator. This work summarizes our efforts to reduce the computational cost of computing the deflation space while achieving the desired variance reduction for Lattice QCD applications. Previous efforts computed the lower part of the singular spectrum of the Dirac operator by using an eigensolver preconditioned with a multigrid linear system solver. Despite the improvement in performance in those applications, as the problem size grows the runtime and storage demands of this approach will eventually dominate the stochastic estimation part of the computation. In this work, we propose to compute the deflation space in one of the following two ways. First, by using an inexact eigensolver on the Hermitian, but maximally indefinite, operator. Second, by exploiting the fact that the multigrid prolongator for this operator is rich in components toward the lower part of the singular spectrum. We show experimentally that the inexact eigensolver can approximate the lower part of the spectrum even for ill-conditioned operators. Also, the deflation based on the multigrid prolongator is more efficient to compute and apply, and, despite its limited ability to approximate the fine level spectrum, it obtains similar variance reduction on the trace estimator as deflating with approximate eigenvectors from the fine level operator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging interpolation models and error bounds for verifiable scientific machine learning

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. Here, in this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Non-destructive analysis of swelling in the EMPIrE fuel test

The European Mini-Plate Irradiation Experiment (EMPIrE) was designed to support the development and testing of a coated uranium-molybdenum (U-Mo) dispersion fuel for the conversion of select high-performance research reactors (HPRRs) to utilize low-enriched uranium (LEU). To aid in the development of the coated fuel form, the EMPIrE test included several plate designs and irradiated them in the Idaho National Laboratory (INL) Advanced Test Reactor (ATR) at a high meat power density (~21 kW/cm 3 ) and to high fuel particle fission densities (~6.4 × 10 21 fissions/cm 3 ). These conditions mimic the bounding conditions of the BR-2 reactor in Belgium, where a concurrent irradiation experiment was performed, and exceed those previously explored in dispersion U-Mo fuel plates. A local fuel swelling analysis, as determined through high-fidelity, post-irradiation mini-plate profilometry, was used along with statistical methods to non-destructively evaluate the overall performance and separate the effects of convoluted fabrication variables. While some effects observed with this non-destructive analysis were subtle, others had more significant, and possibly competing, effects on the fuel swelling behavior. In closing, these observations will be examined further with destructive examinations to more fully assess them as the fuel design is developed and qualified.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

The effect of finish rolling temperature and tempering on the microstructure, mechanical properties and dislocation density of direct-quenched steel

Highlights: • The effect of thermomechanical treatment and tempering on direct-quenched steel has been studied. • Dislocation density of direct-quenched and tempered martensite is evaluated using both TEM and Williamson-Hall method. • Statistical analysis has been performed to study the effect of different factors on toughness of direct-quenched martensite. - Abstract: A unique batch tempering treatment for industrial scale direct-quenched steel coils has been studied using laboratory simulations. The tempering treatment was non-isothermal with slow heating to 570 °C and slow cooling to simulate the tempering of large steel coils. The paper presents the effect of finishing rolling temperature (FRT) relative to the non-recrystallization temperature (T{sub NR}) and the effect of long time tempering on the microstructure, dislocation density and mechanical properties of direct-quenched coiled strips. Conditioning austenite below the recrystallization stop temperature resulted in a finer effective grain size distribution, which correlated strongly with the impact toughness of the final product. Furthermore low finish rolling temperature resulted in partially ferritic microstructures while higher finishing rolling temperatures led to mixtures of bainite and martensite. Dislocation densities determined with TEM and XRD showed somewhat different trends regarding the effect of tempering: intra-lath dislocation density, as measured with TEM, showed a statistically significant drop in only one case, while XRD analysis indicated a drop in all cases. Furthermore, no significant correlation between finishing rolling temperature and dislocation density existed in XRD studies. The XRD results indicate that the decrease in dislocation density corresponds to about 100 MPa lower dislocation strengthening. However, precipitation hardening and potential internal micro stress relief compensates this as yield strength remains unchanged or even increases during tempering.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application

Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIF total ) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIF obs ) through radiative transfer modeling. However, the current satellite SIF obs and thus SIF total are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, in this work, we proposed an algorithm to generate both global high-resolution SIF total (HSIF total ) and high-resolution SIF obs (HSIF obs ) at 1 km from low-resolution SIF obs (LSIF obs ) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIF total using the Orbiting Carbon Observatory-2 SIF (R 2 = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIF total and GPP. We also compared HSIF total to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIF total was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIF total product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Formation of 1 H -Phenalene (C 13 H 10 ) in the Taurus Molecular Cloud via Methylidyne Addition-Cyclization-Aromatization (MACA)

The formation of 1H-phenalene (C 13 H 10 ) in cold molecular clouds, such as the Taurus Molecular Cloud-1 (TMC-1), presents a significant challenge to traditional astrochemical models, which predominantly suggest high-temperature pathways for polycyclic aromatic hydrocarbon (PAH) formation. In this study, we explore computationally the Methylidyne Addition-Cyclization-Aromatization (MACA) mechanism as a viable, barrierless pathway for phenalene synthesis under low-temperature conditions. Through electronic structure calculations and Rice–Ramsperger–Kassel–Marcus (RRKM) statistical methods, we demonstrate that the reaction of 1-vinylnaphthalene (C 10 H 7 C 2 H 3 ) with the methylidyne radical (CH) leads to the formation of 1H-phenalene via a bimolecular reaction, a process that is exoergic and without entrance barrier. The MACA mechanism facilitates the growth of the aromatic carbon backbone via a [5 + 1] ring annulation, providing a new insight into PAH formation in cold molecular clouds. Notably, the MACA mechanism has previously been shown to form indene (C9H8), which was detected in TMC-1 as well, via a [4 + 1] annulation, demonstrating its potential to produce a variety of complex PAHs by addition of a five- and six-membered ring to a benzene moiety via [4 + 1] and [5 + 1] annulation, respectively. As a result, this work highlights the importance of barrierless, exoergic reactions involving MACA in the synthesis of complex aromatic molecules in space, expanding our physicochemical understanding of carbon-rich chemistry in cold molecular clouds.

Aromatic compounds↗
DOE OSTI2016

Hyperspectral Dark Field Optical Microscopy of Single Silver Nanospheres

We record spectrally (400 nm ≤ λ ≤ 675 nm, Δλ < 4.69 nm) and spatially (diffraction-limited, sampled at 85 nm2/pixel) resolved dark field (DF) scattering from single silver nanospheres of 100 nm in diameter. Hyperspectral DF optical microscopy is achieved by coupling a hyperspectral detector to an optical microscope, whereby spectrally resolved diffraction-limited images of hundreds of silver nanoparticles can be recorded in ~30 seconds. We demonstrate how the centers and edges of individual particles can be localized in 2D to within a single pixel (85 nm2), using a statistical method for examining texture based on a co-occurrence matrix. Subsequently, spatial averaging of the spectral response in a 3x3 pixel area around the particle centers affords ample signal-to-noise to resolve the plasmon resonance of a single silver nanosphere. A close inspection of the scattering spectra of 31 different nanospheres reveals that each particle has its unique (i) relative scattering efficiency, and (ii) plasmon resonance maximum and dephasing time. These observations are suggestive of nanometric structural variations over length scales much finer than the spatial resolution attainable using the all-optical technique described herein.

Dark Field Scattering↗
DOE OSTI2024

Source Levels of In‐Cloud Air in Shallow Cumulus: Consistency Between Paluch Diagram and Lagrangian Particle Tracking

Abstract The Paluch diagram is a widely used tool for interpreting aircraft measurements of shallow cumulus clouds. A prior study conducted by Heus et al. (2008,https://doi.org/10.1175/2008jas2572.1) concluded that the source levels of in‐cloud air inferred from the Paluch diagram exhibit biases, sometimes of several hundred meters, in comparison to those derived from Lagrangian particle tracking. In this short study we revisit this comparison. The results indicate that the upper source levels of in‐cloud air determined from the Lagrangian Particle Tracking and the Paluch diagram are consistent, and the choice of statistical methods is crucial. The significance of this research lies in confirming the reliability of the Paluch analysis, enabling its confident application to aircraft data.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Estimating the CO 2 Fertilization Effect on Extratropical Forest Productivity From Flux‐Tower Observations

Abstract The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO 2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO 2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO 2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m −2 yr −1 ppm −1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO 2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO 2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO 2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO 2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO 2 concentrations.

Environmental Sciences & Ecology↗
DOE OSTI2024

Effect of adaptive cruise control on fuel consumption in real-world driving conditions

This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Autonomous platform for solution processing of electronic polymers

The manipulation of electronic polymers’ solid-state properties through processing is crucial in electronics and energy research. Yet, efficiently processing electronic polymer solutions into thin films with specific properties remains a formidable challenge. We introduce Polybot, an artificial intelligence (AI) driven automated material laboratory designed to autonomously explore processing pathways for achieving high-conductivity, low-defect electronic polymers films. Leveraging importance-guided Bayesian optimization, Polybot efficiently navigates a complex 7-dimensional processing space. In particular, the automated workflow and algorithms effectively explore the search space, mitigate biases, employ statistical methods to ensure data repeatability, and concurrently optimize multiple objectives with precision. The experimental campaign yields scale-up fabrication recipes, producing transparent conductive thin films with averaged conductivity exceeding 4500 S/cm. Feature importance analysis and morphological characterizations reveal key design factors. This work signifies a significant step towards transforming the manufacturing of electronic polymers, highlighting the potential of AI-driven automation in material science.

Wang, Chengshi [Argonne National Laboratory (ANL),↗