L. Li

Mar 18, 2024 | pubs.aip.org | Materials Laboratory |COMSATS Islamabad |W. Ahmed |L. Li

Bimetallic nanoparticles (NPs) have recently gained great attention in the fields of catalysis, photothermal therapy, biosensing, etc. due to their unique properties.1–3 Depending on the arrangement of the two metals, bimetallic NPs can be classified into either alloys or core-shell structures. Core-shell nanoparticles are usually formed by the successive reduction of metal salts resulting in the heterogeneous arrangement of metals.

Feb 5, 2024 | hard-drive.net | L. Li

DALLAS — A recent scientific study has found that transgender women who compete in women’s esports leagues have an unfair biological advantage in receiving online harassment. “We found no meaningful evidence supporting the idea that transgender women have biological advantages over cisgender women while playing competitive video games,” said head researcher Dr. Melanie Parker.

Nov 2, 2023 | pubs.aip.org | Université de Lorraine |L. Li

Self-assembly of dipolar particles assisted by external magnetic/electric field is relevant in the development of a wide range of applications. The inherent chain formation leads to outstanding magnetic properties,1–3 that can be beneficial to drug delivery,4 microfluidic systems,5 electrophotographic devices,6 data recording,7 etc. Moreover, aggregation of magnetic particles is also known to be a very confinement-sensitive phenomenon.

Aug 4, 2023 | pubs.aip.org | L. Li

To investigate the impact of EDLVO forces on high-viscosity droplet, we initially examined the deformation dynamics of the droplet in a capillary tube in the absence of EDLVO forces. The velocity and deformation of the droplet would gradually reach a steady state during the transient process. Figure 3(a) presents the deformation of droplet at steady state without applying EDLVO forces under different capillary numbers Ca (Ca = μ1u1/σ, where u1 is the average velocity of water).

Jul 17, 2023 | sciencedirect.com | L. Li

IntroductionNeuromorphic computing has been intensively studied in emerging fields, such as artificial intelligence and high-density storage, because it has the capacity to overcome the bottlenecks of von Neumann computer architectures [1], [2], [3]. To utilize neuromorphic computing techniques, it is necessary to develop devices that can simultaneously process and store data.