Engineering topics

Yan, Hao

Publications and source records attributed to Yan, Hao.

5 records

DOE OSTI2024

High pressure–derived nonsymmetrical [Cu 2 O] 2+ core for room-temperature methane hydroxylation

Nonsymmetrical oxygen-bridged binuclear copper centers have been proposed and modeled as intermediates and transition states in several C–H oxidation pathways, leading to the postulation that structural dissymmetry enhances the reactivity of the bridging oxygen. However, experimentally characterizing the structure and reactivity of these transient species is remarkably challenging. Here, we report the high-pressure synthesis of a metastable nonsymmetrical dicopper-μ-oxo compound with exceptional reactivity toward the mono-oxygenation of aliphatic C–H bonds. The nonequivalent coordination environment of copper stabilizes localized mixed valency and greatly enhances the hydrogen atom abstraction activity of the bridging oxygen, enabling room-temperature hydroxylation of methane under pressure. These findings highlight the role of dissymmetry in the reactivity of binuclear copper centers and demonstrate precise control of molecular structures by mechanical means.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Thermal-Strain-Enabled Enhanced Emission from UV Laser-Induced Defect Levels near the Surface of Multilayer MoS 2

Monolayer two-dimensional (2D) materials have been intensively studied while research on multilayers is still in its infancy. Here, we induce defects inside bulk MoS 2 through thermal annealing and near the surface of multilayer MoS 2 using 375 nm laser irradiation, and investigate their photoluminescence (PL) and fluorescence lifetime imaging (FLIM). Enhanced emission is limited within a certain MoS 2 thickness. The observed enhanced emission is evidenced by a threshold behavior in super-linear PL intensity increase, strong polarization effects, and increased lifetime of defect peak. The laser power threshold for enhanced emission is much smaller in defects near the surface than that inside the bulk of multilayer MoS 2 . The mechanical strain from a wrinkle of the sample further lowers the laser power threshold for enhanced emission. By exciting with a 639 nm laser that is close to the fundamental gap between the conduction band minimum and the valence band maximum, the lifetime of defect enhanced emission increased by 5 times. Furthermore, one of the competing indirect bandgap emissions disappears, and the defect emission peak dominates the PL spectrum in the wrinkle area with a strain. Furthermore, the discovered principle can be applied to future studies on the integration of enhanced emission and single photon emission involving selectively depopulating the conduction band of the host crystal to defect levels for quantum emitters.

2D materials↗

DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗

DOE OSTI2024

Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility of self-assembling complex lattices or finite-size constructs. However, attempts so far have mostly only been successful in silico and often fail in experiment because of unpredicted traps associated with kinetic slowing down (gelation, glass transition) and competing ordered structures. Theoretical predictions also face the difficulty of encoding the desired interparticle interaction potential with the experimentally available nano- and micrometer-sized particles. To overcome these issues, we combine SAT assembly (a patchy-particle interaction design algorithm based on constrained optimization) with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. In this paper, we use this approach to assemble a pyrochlore three-dimensional lattice, coveted for its promise in the construction of optical metamaterials, and characterize it with small-angle x-ray scattering and scanning electron microscopy visualization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Image-based novel fault detection with deep learning classifiers using hierarchical labels

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Lastly, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.

42 ENGINEERING↗