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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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DOE OSTI2019

Impact of Long-Course Neoadjuvant Radiation on Postoperative Low Anterior Resection Syndrome and Quality of Life in Rectal Cancer: Post Hoc Analysis of a Randomized Controlled Trial

Background: Neoadjuvant radiation is recommended for locally advanced rectal cancer, with proven benefit in local control but not in disease-free survival. However, the impact of long-course radiation on postoperative bowel function and quality of life (QOL) remains controversial. This study aimed to investigate the impact of long-course neoadjuvant radiation on bowel function and QOL, and to identify risk factors for severe bowel dysfunction. Methods: Patients who underwent long-course neoadjuvant chemoradiotherapy (nCRT) or chemotherapy (nCT) followed by radical low anterior resection for locally advanced rectal cancer were recruited from the FOWARC randomized controlled trial. Low anterior resection syndrome (LARS) score and European Organisation for Research and Treatment of Cancer (EORTC) C30/CR29 questionnaires were used to assess bowel function and QOL, respectively. Results: Overall, 220 patients responded after a median follow-up of 40.2 months, of whom 119 (54.1%) reported major LARS, 74 (33.6%) reported minor LARS, and 27 (12.3%) reported no LARS. Compared with the nCT group, the nCRT group reported more major LARS (64.4% vs. 38.6%, p < 0.001) and worse QOL. Long-course neoadjuvant radiation (OR 2.20, 95% CI 1.24–3.91; p = 0.007), height of anastomosis (OR 0.74, 95% CI 0.63–0.88; p < 0.001), and diverting ileostomy (OR 2.59, 95% CI 1.27–5.30; p = 0.009) were independent risk factors for major LARS. Conclusions: Long-course neoadjuvant radiation, along with low anastomosis, are likely independent risk factors for postoperative bowel function and QOL. Our findings might have implications for alleviating LARS and improving QOL by informing selection of neoadjuvant treatment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

Higher shares of electricity generation from renewable energy sources and market liberalization is increasing uncertainty in power systems operation. At the same time, operation is becoming more flexible with improved control systems and new technology such as phase shifting transformers (PSTs) and high voltage direct current connections (HVDC). Previous studies have shown that the use of corrective control in response to outages contributes to a reduction in operating cost, while maintaining N-1 security. In this work, we propose a method to extend the use of corrective control of PSTs and HVDCs to react to uncertainty. We characterize the uncertainty as continuous random variables, and define the corrective control actions through affine control policies. This allows us to efficiently model control reactions to a large number of uncertainty sources. The control policies are then included in a chance constrained optimal power flow formulation, which guarantees that the system constraints are enforced with a desired probability. Lastly, by applying an analytical reformulation of the chance constraints, we obtain a second-order cone problem for which we develop an efficient solution algorithm. In a case study for the IEEE 118 bus system, we show that corrective control for uncertainty leads to a decrease in operational cost, while maintaining system security. Further, we demonstrate the scalability of the method by solving the problem for the IEEE 300 bus and the Polish system test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Automatic learning-based beam angle selection for thoracic IMRT

Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose–volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner’s clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload. Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth. Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume coverage and organ at risk sparing and were superior over plans produced with fixed sets of common beam angles. The great majority of the automatic plans (93%) were approved as clinically acceptable by three radiation therapy specialists. Conclusions: The results demonstrated the feasibility of utilizing a learning-based approach for automatic selection of beam angles in thoracic IMRT planning. The proposed method may assist in reducing the manual planning workload, while sustaining plan quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Excitonic effects in oxyhalide scintillating host compounds

Ab-initio calculations based on density functional theory have been performed to study the electronic, optical, mechanical, and vibrational properties of scintillator host compounds YOX (X = F, Cl, Br, and I). Semiempirical dispersion correction schemes are used to find the effect of van der Waals forces on these layered compounds and we found this effect to be negligible except for YOBr. Calculations of phonons and elastic constants showed that all the compounds studied here are both dynamically and mechanically stable. YOF and YOI are found to be indirect band gap insulators while YOCl and YOBr are direct band gap insulators. The band gap is found to decrease as we move from fluorine to iodine, while the calculated refractive index shows the opposite trend. As the band gap decreases on going down the periodic table from YOF to YOI, the luminescence increases. The excitonic binding energy calculated, within the effective mass approximation, is found to be more for YOF than the remaining compounds, suggesting that the excitonic effect to be more in YOF than the other compounds. The optical properties are calculated within the Time-Dependent Density Functional Theory (TDDFT) and compared with results obtained within the random phase approximation. The TDDFT calculations, using the newly developed bootstrap exchange-correlation kernel, showed significant excitonic effects in all the compounds studied here.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Charge collection microscopy of in-situ switchable PRAM line cells in a scanning electron microscope: Technique development and unique observations

An imaging method has been developed based on charge collection in a scanning electron microscope (SEM) that allows discrimination between the amorphous and crystalline states of Phase-change Random Access Memory (PRAM) line cells. During imaging, the cells are electrically connected and can be switched between the states and the resistance can be measured. This allows for electrical characterization of the line cells in-situ in the SEM. Details on sample and measurement system requirements are provided which turned out to be crucial for the successful development of this method. Results show that the amorphous or crystalline state of the line cells can be readily discerned, but the spatial resolution is relatively poor. Nevertheless, it is still possible to estimate the length of the amorphous mark, and also for the first time, we could directly observe the shift of the amorphous mark from one side of the line cell to the other side when the polarity of the applied (50 ns) RESET pulse was reversed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

WE-AB-209-05: Development of an Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System

Purpose: To enable near-real-time (<20sec) and interactive planning without compromising quality for whole breast RT treatment planning using tangential fields. Methods: Whole breast RT plans from 20 patients treated with single energy (SE, 6MV, 10 patients) or mixed energy (ME, 6/15MV, 10 patients) were randomly selected for model training. Additional 20 cases were used as validation cohort. The planning process for a new case consists of three fully automated steps:1. Energy Selection. A classification model automatically selects energy level. To build the energy selection model, principle component analysis (PCA) was applied to the digital reconstructed radiographs (DRRs) of training cases to extract anatomy-energy relationship.2. Fluence Estimation. Once energy is selected, a random forest (RF) model generates the initial fluence. This model summarizes the relationship between patient anatomy’s shape based features and the output fluence. 3. Fluence Fine-tuning. This step balances the overall dose contribution throughout the whole breast tissue by automatically selecting reference points and applying centrality correction. Fine-tuning works at beamlet-level until the dose distribution meets clinical objectives. Prior to finalization, physicians can also make patient-specific trade-offs between target coverage and high-dose volumes.The proposed method was validated by comparing auto-plans with manually generated clinical-plans using Wilcoxon Signed-Rank test. Results: In 19/20 cases the model suggested the same energy combination as clinical-plans. The target volume coverage V100% was 78.1±4.7% for auto-plans, and 79.3±4.8% for clinical-plans (p=0.12). Volumes receiving 105% Rx were 69.2±78.0cc for auto-plans compared to 83.9±87.2cc for clinical-plans (p=0.13). The mean V10Gy, V20Gy of the ipsilateral lung was 24.4±6.7%, 18.6±6.0% for auto plans and 24.6±6.7%, 18.9±6.1% for clinical-plans (p=0.04, <0.001). Total computational time for auto-plans was < 20s. Conclusion: We developed an automated method that generates breast radiotherapy plans with accurate energy selection, similar target volume coverage, reduced hotspot volumes, and significant reduction in planning time, allowing for near-real-time planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Comparison of the results of refractometric measurements in the process of diffusion, obtained by means of the backgroundoriented schlieren method and the holographic interferometry method

The results of the study of interdiffusion of two liquids, obtained using the holographic recording scheme with a nonstationary reference wave with the frequency linearly varying in space and time are compared with the results of correlation processing of digital photographs, made with a random background screen. The spatio-temporal behaviour of the signal in four basic representations ('space – temporal frequency', 'space – time', 'spatial frequency – temporal frequency' and 'spatial frequency – time') is found in the holographic experiment and calculated (in the appropriate coordinates) based on the background-oriented schlieren method. Practical coincidence of the results of the correlation analysis and the holographic double-exposure interferometry is demonstrated. (interferometry)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Scaling Input Distributions for Probabilistic Models - 19472

It is important to address scaling issues when building models to support risk-based decisions for radioactive waste disposal sites. Currently, probabilistic risk assessment and probabilistic performance assessment models are run for multiple realizations where each realization uses one value from each input distribution over the entire modeled time. The input distributions represent the current state of knowledge in the mean of the input variable over the spatial region of interest and over the time period of the model run. This paper focuses on the time factor in input distribution scaling because it presents more serious conceptual and distributional challenges than the spatial factor. One of the challenges in temporal scaling is the effect of non-linearity on propagation of distribution uncertainty through to uncertainty in results. Radioactive waste disposal models tend to have both linear and non-linear components in their models. Dose assessment calculations are typically represented by linear models, while contaminant transport models have linear and non-linear components. In principle, temporal scaling of input parameter distributions for a linear model is straightforward, but scaling of input distributions for non-linear models is not. This paper uses examples to investigate the effects of input distribution temporal scaling on simple linear and non-linear models. Different scaling scenarios were investigated by comparing three analysis tools: analytical mathematical representations, statistical software programming, and GoldSim Monte Carlo simulation software. R statistical software is an open source statistical programming language often used for statistical analyses and graphical representation of data. GoldSim is a robust probabilistic dynamic modeling platform used in performance assessment modeling of radioactive waste sites. A simple linear model, a quadratic model and a multiplicative model were the cases explored for each of these tools. For the three analysis tools considered and with each of the scaling cases, 1,000 realizations were implemented from distributions of the random variables of interest. This is a sufficient number of realizations for simple models to capture the model space. An annual temporal resolution of a 100-year simulation period was chosen for all models and analysis tools. The effects of temporal scaling were demonstrated by investigating two different options for time scaling. Option 1 was set up so that the input distributions represented the expected value on an annual time scale, and a new value from that distribution was chosen every year (at every time step which equaled a year) for 100 years. In Option 2, the input distributions were scaled to represent the expected mean value of the input variable over the 100 years of simulation, and one value from that distribution was chosen for the entire elapsed time. Results show agreement across the three analysis tools. As expected, the linear models have identical results for the two different scaling Options, and the non-linear models diverge in the results for different time scale implementation. The implications of this work point to the need for careful consideration of input distribution temporal scaling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Diffusion and stochastic island generation in the magnetic field line random walk

The cross-field diffusion of field lines in stochastic magnetic fields described by the 2D+slab model is studied using a semi-analytic statistical approach, the decorrelation trajectory method. We show that field line trapping and the associated stochastic magnetic islands strongly influence the diffusion coefficients, leading to dependences on the parameters that are different from the quasilinear and Bohm regimes. A strong amplification of the diffusion is produced by a small slab field in the presence of trapping. The diffusion regimes are determined and the corresponding physical processes are identified.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods

Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate. We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric. Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.

59 BASIC BIOLOGICAL SCIENCES↗
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↗
DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

SU-E-T-248: Near Real-Time Analysis of Radiation Delivery and Imaging, Accuracy to Ensure Patient Safety

Purpose: To develop and optimize an effective software method for comparing planned to delivered control point machine parameters for all VARIAN TrueBeam treatments so as to permit (1) assessment of a large patient pool throughout their treatment course to quantify treatment technique specific systematic and random uncertainty of observables, (2) quantify the site specific daily imaging shifts required for target alignment, and (3) define tolerance levels for mechanical parameters and imaging parameters based on statistical analysis data gathered, and the dosimetric impact of variations. Methods: Treatment and imaging log files were directly compared to plan parameters for Eclipse and Pinnacle planned treatments via 3D, IMRT, control point, RapidArc, and electrons. Each control point from all beams/arcs (7984) for all fractions (1940) of all patients treated over six months were analyzed. At each control point gantry angle, collimator angle, couch angle, jaw positions, MLC positions, MU were compared. Additionally per-treatment isocenter shifts were calculated. Results were analyzed as a whole in treatment type subsets: IMRT, 3D, RapidArc; and in treatment site subsets: brain, chest/mediastinum, esophagus, H and N, lung, pelvis, prostate. Results: Daily imaging isocenter shifts from initial external tattoo alignment were dependent on the treatment site with < 0.5 cm translational shifts for H and N, Brain, and lung SBRT, while pelvis, esophagus shifts were ∼1 cm. Mechanical delivery parameters were within tolerance levels for all sub-beams. The largest variations were for RapidArc plans: gantry angle 0.11±0.12,collimator angle 0.00±0.00, jaw positions 0.48±0.26, MLC leaf positions 0.66±0.08, MU 0.14±0.34. Conclusion: Per-control point validation reveals deviations between planned and delivered parameters. If used in a near real-time error checking system, patient safety can be improved by equipping the treatment delivery system with additional forcing functions which by-pass human error avenues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The Effect of aRandom Transverse Field on the Ferroelectric Properties of aNanowire with Core/Shell Morphology (FN(C/S)M)

In this paper, we have used the effective field theory (EFT) based on the probability distribution method (PDM) to study the hysteresis behavior of a ferroelectric nanowire with core/shell morphology (FN(C/S)M) in a random electric field (REF). This system is studied by using the transverse Ising model (TIM). We have studied the effects of the temperature, the random transverse field (RTF), the surface shell coupling, and the concentration on the hysteresis loops, the coercive field, and the remanent polarization of the nanowire. A number of characteristic behaviors have been found, such as the existence of the relaxor character for appropriate values of the system parameters. The remanent polarization and the coercive field, as a function of the temperature, are examined.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Phase 3 Trial of Domiciliary Humidification to Mitigate Acute Mucosal Toxicity During Radiation Therapy for Head-and-Neck Cancer: First Report of Trans Tasman Radiation Oncology Group (TROG) 07.03 RadioHUM Study

Purpose: To assess the impact of domicile-based humidification on symptom burden during radiation therapy (RT) for head-and-neck (H and N) cancer. Methods and Materials: From June 2007 through June 2011, 210 patients with H and N cancer receiving RT were randomized to either a control arm or to receive humidification using the Fisher and Paykel Healthcare MR880 humidifier. Humidification commenced on day 1 of RT and continued until Common Terminology Criteria for Adverse Events (CTCAE), version 3.0, clinical mucositis (CMuc) grade ≤1 occurred. Forty-three patients (42%) met a defined benchmark for humidification compliance and contributed to per protocol (PP) analysis. Acute toxicities, hospitalizations, and feeding tube events were recorded prospectively. The McMaster University Head and Neck Radiotherapy Questionnaire (HNRQ) was used for patient-reported outcomes. The primary endpoint was area under the curve (AUC) for CMuc grade ≥2. Results: There were no significant differences in AUC for CMuc ≥2 between the 2 arms. Humidification patients had significantly fewer days in hospital (P=.017). In compliant PP patients, the AUC for CTCAE functional mucositis score (FMuc) ≥2 was significantly reduced (P=.009), and the proportion who never required a feeding tube was significantly greater (P=.04). HNRQ PP analysis estimates also in the direction favoring humidification with less symptom severity, although differences at most time points did not reach significance. Conclusions: TROG 07.03 has provided efficacy signals consistent with a role for humidification in reducing symptom burden from mucositis, but the influence of humidification compliance on the results moderates recommendations regarding its practical utility.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Optimizing Radiation Therapy Quality Assurance in Clinical Trials: A TROG 08.03 RAVES Substudy

Purpose: To explore site- and clinician-level factors associated with protocol violations requiring real-time-review (RTR) resubmission in a multicenter clinical trial to help tailor future quality assurance (QA) protocols. Methods and Materials: RAVES (Radiation Therapy–Adjuvant vs Early Salvage) (Trans-Tasman Radiation Oncology Group 08.03) is a randomized trial comparing adjuvant with early salvage radiation therapy in men with positive surgical margins or pT3 disease after prostatectomy. Quality assurance in RAVES required each clinician and site to submit a credentialing dummy run (DR) and for each patient's radiation therapy plan to undergo external RTR before treatment. Prospectively defined major violations from trial protocol required remedy and resubmission. Site and clinician factors associated with RTR resubmission were examined using hierarchical modeling. Results: Data were collected from 171 consecutive patients, treated by 46 clinicians at 32 hospitals. There were 47 RTR resubmissions (27%) due to 65 major violations. The relative rate of resubmission decreased by 29% per year as the study progressed (odds ratio OR. 0.71, P=.02). The majority of resubmissions were due to contouring violations (39 of 65) and dosimetric violations (22 of 65). For each additional patient accrued, significant decreases in RTR resubmission were seen at both clinician level (OR 0.75, P=.02) and site level (OR 0.72, P=.01). The rate of resubmission due to dosimetric violations was only 1.6% after the first 5 patients. Use of IMRT was associated with lower rates of resubmission compared with 3-dimensional conformal radiation therapy (OR 0.38, P=.05). Conclusion: Several low- and high-risk factors that may assist with tailoring future clinical trial QA were identified. Because the real-time resubmission rate was largely independent of the credentialing exercise, some form of RTR QA is recommended. The greatest benefit from QA was derived early in trial activation and clinician experience.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Phase-Controlled Synthesis of Pd–Sn Nanocrystal Catalysts of Defined Size and Shape

Bimetallic Pd-based nanoparticles (NPs) are of interest as electrocatalysts for formic acid electrooxidation (FAEO) because of their higher initial catalytic activity and CO tolerance when compared to Pt. Intermetallic NPs (i-NPs) with specific geometric and electronic structures generally exhibit superior catalytic activity, selectivity, and durability when compared to their disordered (random alloy) counterparts; however, the colloidal synthesis of i-NPs remains a challenge. Here, a one-pot method was demonstrated as a facile route to obtain monodisperse Pd–Sn NPs with phase control, including intermetallic hexagonal Pd 3 Sn 2 (P6 3 /mmc), intermetallic orthorhombic Pd 2 Sn (Pnma), and alloy cubic Pd 3 Sn (FCC, Fm 3 ¯m) as size-controlled NPs with quasi-spherical shapes. Initial metal precursor ratios and reaction temperature were critical parameters to achieving phase control. Also, slight modifications of synthetic conditions resulted in either Pd 2 Sn nanorhombohedra or nanorods with tunable aspect ratios. A systematic evaluation of the Pd–Sn NPs for FAEO showed that most presented higher specific activities when compared to commercial Pd/C, in which Pd 2 Sn quasi-spheres and nanorhombohedra showed the highest catalytic activity for FAEO. Furthermore, these results highlight the benefits of phase-controlled Pd-based nanocatalysts with defined nanocrystal size and shape, with use of trioctylphospine (TOP) and oleic acid (OA) central to shape and size control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗