Engineering topics

Li, Mingwei

Publications and source records attributed to Li, Mingwei.

3 records

DOE OSTI2023

pH‐ and Redox‐Responsive Pickering Emulsions Based on Cellulose Nanocrystal Surfactants

Abstract Nanoparticle surfactants (NPSs), formed by using dynamic interactions between nanoparticles and complementary ligands at the liquid‐liquid interface, have emerged as “smart emulsifiers” with attributes of high emulsification efficiency, long‐term stability, and on‐demand emulsification/demulsification capabilities. However, only pH‐responsiveness can be adopted for the assembly of reported NPSs formed by electrostatic interactions. Here, we propose an alternative design strategy, by taking advantage of the ferrocenium (Fc + ) sulfate ion pair, to develop a new type of cellulose nanocrystal (CNC) surfactant. The Fc + groups are sensitive to pH, redox reagents and voltage, imparting the CNC surfactants and derived Pickering emulsions with multi‐stimuli‐responsiveness, and showing promising applications in controllable delivery, release, and biphasic biocatalysis.

Yang, Yang↗

DOE OSTI2023

pH- and Redox-Responsive Pickering Emulsions Based on Cellulose Nanocrystal Surfactants

Nanoparticle surfactants (NPSs), formed by using dynamic interactions between nanoparticles and complementary ligands at the liquid-liquid interface, have emerged as “smart emulsifiers” with attributes of high emulsification efficiency, long-term stability, and on-demand emulsification/demulsification capabilities. However, only pH-responsiveness can be adopted for the assembly of reported NPSs formed by electrostatic interactions. Here, we propose an alternative design strategy, by taking advantage of the ferrocenium (Fc + ) sulfate ion pair, to develop a new type of cellulose nanocrystal (CNC) surfactant. In conclusion, the Fc + groups are sensitive to pH, redox reagents and voltage, imparting the CNC surfactants and derived Pickering emulsions with multi-stimuli-responsiveness, and showing promising applications in controllable delivery, release, and biphasic biocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.

97 MATHEMATICS AND COMPUTING↗