HackerPapers

arXiv:1908.00537physics.flu-dyn

How to make a giant bubble

Stephen Frazier, Xinyi Jiang, Justin C. Burton

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

Using mixtures of soap, water, and long chain polymers, free-floating soap bubbles can be formed with volumes approaching 100 m$^3$. Here we investigate how such thin films are created and maintained over time. We show how the extensional rheology is the most important factor in creating the bubble, and how polydispersity in molecular weight of the solvated polymers leads to better performance at lower concentrations. Additionally, using IR absorption, we measure soap film thickness profiles and film lifetimes. Although the initial thickness mostly depends on the choice of detergent, polymers can dramatically increase film lifetime at high molecular weights and high concentrations, although such high concentrations can inhibit the initial film formation. Thus, the ideal concentration of polymer additives for making giant bubbles requires a robust viscoelastic rheology during extension, and is aided by long film lifetimes during gravitational drainage and evaporation.

arXiv comments
10 pages, 8 figures
Journal reference
Phys. Rev. Fluids 5, 013304 (2020)
DOI
doi:10.1103/PhysRevFluids.5.013304
Subjects
physics.flu-dynFluid Dynamics cond-mat.softSoft Condensed Matter

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Feb 2020 to Feb 2020.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. How to Make a Giant Bubble 1 comment
    • Non-paywall source: https://arxiv.org/abs/1908.00537 The article title is somewhat misleading. The authors have only investigated "the properties of surfactant and polymer solutions commonly used to make giant bubbles with surface areas approaching 100 m^2", and they don't…

      b215826View on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.