Phase II Final Report
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61 RADIATION PROTECTION AND DOSIMETRY↗
Engineering topics
Li, Jiang
Publications and source records attributed to Li, Jiang.
Aging Mechanisms of Broad Area ~800 nm Laser Diodes
Here, this work presents a comprehensive study of early aging behavior (<500 hr) in ~800 nm, phosphide-based laser diodes grown by solid-source MBE with different oxygen concentration levels incorporated into the diode epitaxial layers during growth. The data indicate that lasing characteristics prior to aging are degraded by oxygen introduction, but the gradual power degradation rate after the onset of aging is not a strong function of oxygen at these concentration levels. Devices with oxygen concentrations of ~2.5 × 10 15 cm -3 showed significantly longer delay before the onset of aging (incubation time) than devices with less than 1 × 10 15 cm -3 oxygen. Generation-Recombination current and Laser Beam Induced Current measurements indicate that defect densities and aggregation are suppressed at the facets by oxygen, which can explain longer incubation times. Diagnostic data and parametric fits to diode simulation models show that increased cavity optical loss and defect density are primarily responsible for gradual power degradation during aging, rather than changes in nonradiative recombination. Mechanisms are proposed that explain this behavior, based on density functional theory (DFT) simulations and known recombination-enhanced defect generation phenomena.
47 OTHER INSTRUMENTATION↗
Slope Efficiency and Voltage Reduction at High Current Densities in AlInGaAs Diode Lasers
Here, the slope efficiency and drive voltage of broad area AlInGaAs laser diodes near 865 nm is observed to decrease significantly under quasi-CW pulsed operation at currents well above threshold, in a manner that cannot be explained by thermal effects or carrier leakage over heterojunction barriers. Simulations show that the slope efficiency reduction is explicable by increased free carrier absorption in the waveguide region. Empirical formulas are presented to represent these effects in a closed analytic form suitable for use in simulators for diode-pumped laser systems.
47 OTHER INSTRUMENTATION↗
The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning
A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. Further, the MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10%. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11%. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.
42 ENGINEERING↗
Deep Multitask Learning Models for Radiation Estimation at High Energy Accelerator Facility
Controlling the dose of radiation exposure in potential radioactive facilities is critical for ensuring the safety of staff and the public. Here, in this paper, we developed machine learning models to estimate radiation exposure efficiently at the Thomas Jefferson National Accelerator Facility (JLab), aiming to enhance safety in both accelerator facilities and public areas. Multiple sensors were deployed around the three experimental halls at JLab. Data on single-beam currents, energy levels, and radiation values at the sensor locations were collected during accelerator operation. We proposed a multi-task learning model for radiation estimation, utilizing either one-dimensional convolutional neural networks (1-D CNNs) or long short-term memory networks (LSTMs) as the backbone. The proposed model was trained to simultaneously estimate radiation levels at the sensor locations. Experimental results demonstrated that the proposed model with LSTM backbone achieved the best estimation performance, with an average R 2 score of 0.7557 for estimation within the same year and 0.7157 for estimation across different years. These results significantly surpassed those of competing models.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Tuning Active Site Flexibility by Defect Engineering of Graphene Ribbon Edge‐hosted Fe−N 3 Sites
Abstract Nitrogen‐doped, carbon‐supported transition metal catalysts are excellent for several reactions. Structural engineering of M−N x sites to boost catalytic activity is rarely studied. Here, we demonstrate that the structural flexibility of Fe−N 3 site is vital for tuning the electronic structure of Fe atoms and regulating the catalytic transfer hydrogenation (CTH) activity. By introducing carbon defects, we construct Fe−N 3 sites with varying Fe−N bond lengths distinguishable by X‐ray absorption spectroscopy. We investigate the CTH activity by density‐functional theory and microkinetic calculations and reveal that the vertical displacement of the Fe atom out of the plane of the support, induced by the Fe−N 3 distortion, raises the Fe orbital and strengthens binding. We propose that the activity is controlled by the relaxation of the reconstructed site, which is further affected by Fe−N bond length, an excellent activity descriptor. We elucidate the origin of the CTH activity and principles for high‐performing Fe−N−C catalysts by defect engineering.
Yang, Piaoping↗
Tuning Active Site Flexibility by Defect Engineering of Graphene Ribbon Edge–hosted Fe–N3 Sites
Nitrogen-doped, carbon-supported transition metal catalysts are excellent for several reactions. Structural engineering of metal-Nx sites to boost catalytic activity is rarely studied. Here, we demonstrate that the structural flexibility of Fe-N3 site is vital for tuning the electronic structure of Fe atoms and regulating the catalytic transfer hydrogenation (CTH) activity. By introducing carbon defects, we construct Fe-N3 sites with varying Fe-N bond lengths distinguishable by X-ray absorption spectroscopy. We investigate the CTH activity by density functional theory and microkinetic calculations and reveal that the vertical displacement of the Fe atom out of the plane of the support, induced by the Fe-N3 distortion, raises the Fe [[EQUATION]]orbital and strengthens binding. We propose that the activity is controlled by the relaxation of the reconstructed site, which is further affected by Fe-N bond length, an excellent activity descriptor. Furthermore, we elucidate the origin of the CTH activity and principles for high-performing Fe-N-C catalysts by defect engineering.
09 BIOMASS FUELS↗
Highly active, ultra-low loading single-atom iron catalysts for catalytic transfer hydrogenation
Highly effective and selective noble metal-free catalysts attract significant attention. Here, a single-atom iron catalyst is fabricated by saturated adsorption of trace iron onto zeolitic imidazolate framework-8 (ZIF-8) followed by pyrolysis. Its performance toward catalytic transfer hydrogenation of furfural is comparable to state-of-the-art catalysts and up to four orders higher than other Fe catalysts. Isotopic labeling experiments demonstrate an intermolecular hydride transfer mechanism. First principles simulations, spectroscopic calculations and experiments, and kinetic correlations reveal that the synthesis creates pyrrolic Fe(II)-plN 3 as the active center whose flexibility manifested by being pulled out of the plane, enabled by defects, is crucial for collocating the reagents and allowing the chemistry to proceed. The catalyst catalyzes chemoselectively several substrates and possesses a unique trait whereby the chemistry is hindered for more acidic substrates than the hydrogen donors. This work paves the way toward noble-metal free single-atom catalysts for important chemical reactions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Nitrogen-doped carbon for selective pseudo-metal-free hydrodeoxygenation of 5-hydroxymethylfurfural to 2,5-dimethylfuran: Importance of trace iron impurity
Heteroatoms-doped carbon materials have recently emerged as effective catalysts for various chemical and electrochemical reactions. The free of metals especially noble metals reduces cost and eliminates issues like sintering or leaching of metals at elevated temperatures in solvents. In this work, selective hydrodeoxygenation (HDO) of 5-hydroxymethylfurfural (HMF) to 2,5-dimethylfuran (DMF) is for the first time achieved over simple nitrogen-doped carbon (N-C) catalysts. Further, at optimal reaction conditions, a 91% yield of DMF is obtained with excellent catalyst stability. Extensive characterization, including extended X-ray absorption fine-structure (EXAFS) and soft X-ray absorption spectroscopy (sXAS), model reactions, basic data science analysis, and DFT calculations suggest that ppm of Fe in particular FeN 3 sites formed in pyrolysis, rather than non-metallic elements, drive key steps such as H 2 activation and deoxygenation of –OH during HMF HDO.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Screening binary alloys for electrochemical CO 2 reduction towards multi-carbon products
Electrochemical reduction of CO 2 (eCO 2 R) to high-value chemicals presents an attractive approach for utilizing CO 2 . Copper (Cu) is presently the only electrocatalyst that fulfills this purpose with notable activity, but selectivity remains a problem. To identify catalysts for eCO 2 R with high selectivity towards multicarbon (C 2(+) ) products, we explore binary systems composed of strongly and weakly CO binding metals alloyed with Cu, Fe, Co, Ni, and Pd. A total number of 142 alloys with two commonly studied configurations, L1 2 and L1 0 , are simulated with density functional theory (DFT). We leverage recent progress in the atomistic understanding of the eCO 2 R mechanism and use the binding energies of CO* and C* as descriptors when screening for C 2(+) selectivity. We evaluate the stability of the binary alloys by analyzing the formation energy of the clean alloy surfaces. Our theoretical screening identifies about 16 Cu-based alloys and 18 non-Cu based alloys with optimal C 2(+) selective properties for eCO 2 R. For the non-Cu based binary alloys, the p-block elements play an important role in tuning the C* and CO* adsorption energies. In terms of stability, most of the Cu-based systems alloyed with metals that exhibit strong CO* binding are unstable. Ni-based alloys are more stable than the Co-based alloys followed by the Fe-based alloys, and all the Pd-based alloys are stable. In general, the L1 0 structural Fe, Co, Ni, and Pd-based alloys are more stable than the corresponding L1 2 alloys. Our approach identifies materials known to have good C 2(+) selectivity, but it also proposes several other promising materials that have not previously been tested for eCO 2 R.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An LSTM Deep Learning Network for ¿Background Radiation Prediction
Determination of appropriate background radiation is important in any measurement application. Environmental radiation monitors and monitors used to assess dose to individuals outside of controlled areas are particularly susceptible to changes in readings due to fluctuations in the environmental conditions. These fluctuations (e.g. radon progeny concentrations) lead to changes in the detector response in the actual radiation environment, and they need to be taken into account when extracting the net operational doses. Work has been ongoing to apply advances in Deep Learning and Artificial Intelligence to account for changes in detector responses based on environmental parameters; in particular, a Long-Short Term Memory (LSTM) Deep Learning architecture has been utilized to incorporate time-series data into a prediction model. In this work, we present the current status of the project to predict radiation measurements based on meteorological conditions and air packet trajectories extracted using the National Oceanic and Atmospheric Administration's (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model.
Degtiarenko, Pavel↗
Unraveling Electronic Trends in O* and OH* Surface Adsorption in the MO 2 Transition-Metal Oxide Series
Understanding the bond strength of O* and OH* intermediates to metal-oxide surfaces is key to predicting the catalytic activity in oxygen-based electrochemistry. Here, we uncover highly non-linear trends in O* and OH* adsorption energies across the 3d, 4d, and 5d series of MO 2 transition-metal (TM) oxide surfaces computed within Hubbard- U corrected density functional theory (DFT + U ). Investigating the electronic structure with crystal orbital Hamiltonian populations (COHP) of the relevant metal–oxygen bonds reveals that the spin-dependent coupling strength between metal-d and oxygen-2p atomic orbitals together with the extent of filling of bonding and anti-bonding orbitals are the primary contributors to the adsorption energy. Importantly, we show that the integrated COHP obtained purely from bulk calculations is a highly accurate descriptor for surface adsorption energetics that captures trends across the group 5–12 TM oxide series within 0.19–0.36 eV. Our results suggest a pathway to prediction of adsorption energies for an arbitrary metal–ligand catalyst system.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Recent Advances in the Photocatalytic Conversion of Biomass-Derived Furanic Compounds
Not provided.
Chemistry↗
Tuning electrochemically driven surface transformation in atomically flat LaNiO 3 thin films for enhanced water electrolysis
Structure–activity relationships built on descriptors of bulk and bulk-terminated surfaces are the basis for the rational design of electrocatalysts. However, electrochemically driven surface transformations complicate the identification of such descriptors. In this work, we demonstrate how the as-prepared surface composition of (001)-terminated LaNiO 3 epitaxial thin films dictates the surface transformation and the electrocatalytic activity for the oxygen evolution reaction. Specifically, the Ni termination (in the as-prepared state) is considerably more active than the La termination, with overpotential differences of up to 150 mV. A combined electrochemical, spectroscopic and density-functional theory investigation suggests that this activity trend originates from a thermodynamically stable, disordered NiO 2 surface layer that forms during the operation of Ni-terminated surfaces, which is kinetically inaccessible when starting with a La termination. Our work thus demonstrates the tunability of surface transformation pathways by modifying a single atomic layer at the surface and that active surface phases only develop for select as-synthesized surface terminations.
36 MATERIALS SCIENCE↗
Sharding-Enabled Blockchain for Software-Defined Internet of Unmanned Vehicles in the Battlefield
Not provided.
Computer Science↗
An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification
Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).
54 ENVIRONMENTAL SCIENCES↗
Deep Learning for Land Cover Classification Using Only a Few Bands
There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.
58 GEOSCIENCES↗