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Scalable mechanical exfoliation of two-dimensional nanosheets by polymer-assisted dry ball-mill of layered materials and insights from machine learning

  • Jing Zhang (Co-first Author)
  • , Tianshu Zhai (Co-first Author)
  • , Yunrui Yan
  • , Qiyi Fang
  • , Yifeng Liu
  • , Yifan Zhu
  • , Weiran Tu
  • , Chen-Yang Lin
  • , Yuguo Wang
  • , Jun Lou*
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

To fully capitalize on the unique properties of 2D materials, cost-effective techniques for producing high-quality 2D flakes at scale are crucial. In this work, we show that dry ball-milling, a commonly used powder-processing technique, can be effectively and efficiently upgraded into an automated exfoliation technique. It is done by adding polymer as adhesives into a ball mill to mimic the well-known tape exfoliation process, which is known to produce 2D flakes with the highest quality but is limited by its extremely low efficiency on large-scale production. Seventeen types of commonly seen polymers, including both artificial and natural ones, have been examined as additives to dry ball-mill hexagonal boron nitride. A parallel comparison between different additives identifies low-cost natural polymers such as starch as promising dry ball-mill additives to produce ultrathin flakes with the largest aspect ratio. The mechanical, thermal, and surface properties of the polymers are proposed as key features that simultaneously determine the exfoliation efficiency, and their ranking of importance in the mechanical exfoliation process is revealed using a machine learning model. Finally, the potential of the polymer-assisted ball-mill exfoliation method as a universal way to produce ultra-thin 2D nanosheets is also demonstrated. © 2025 Elsevier Ltd
Original languageEnglish
Article number100604
JournalMaterials Today Nano
Volume30
Online published6 Mar 2025
DOIs
Publication statusPublished - Jun 2025
Externally publishedYes

Funding

This work is mainly supported by the NSF I/UCRC Center for Atomically Thin Multifunctional Coatings (ATOMIC) under award # EEC-2113882.

Research Keywords

  • Ball mill
  • Machine learning
  • Mechanical exfoliation
  • Polymer additives

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