Engineering topics

Fan, Rong

Publications and source records attributed to Fan, Rong.

3 records

DOE OSTI2021

Flow-regulated growth of nanotubes

A method for growing nanotubes via flow-regulated microfluidic electrochemical anodization, includes providing a microfluidic device having a fluid inlet; a fluid outlet; and a fluidic microchannel connecting the fluid inlet and outlet, wherein the microchannel includes a Pt cathode and a Ti anode separated by an electrical insulator; providing an electrolyte fluid flow through the microchannel; and providing an electrical current across the anode and cathode sufficient to cause electrochemical anodization growth of TiO2 nanotubes in the microchannel on a surface of the anode.

Wan, Jiandi↗

DOE OSTI2020

Achieving large uniform tensile elasticity in microfabricated diamond

Diamond is not only the hardest material in nature, but is also an extreme electronic material with an ultrawide bandgap, exceptional carrier mobilities, and thermal conductivity. Straining diamond can push such extreme figures of merit for device applications. We microfabricated single-crystalline diamond bridge structures with ~1 micrometer length by ~100 nanometer width and achieved sample-wide uniform elastic strains under uniaxial tensile loading along the [100], [101], and [111] directions at room temperature. We also demonstrated deep elastic straining of diamond microbridge arrays. The ultralarge, highly controllable elastic strains can fundamentally change the bulk band structures of diamond, including a substantial calculated bandgap reduction as much as ~2 electron volts. Our demonstration highlights the immense application potential of deep elastic strain engineering for photonics, electronics, and quantum information technologies.

Dang, Chaoqun↗

DOE OSTI2019

Short-term traffic state prediction from latent structures: Accuracy vs. efficiency

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.

99 GENERAL AND MISCELLANEOUS↗