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Field-induced orientational switching produces vertically aligned Ti3C2Tx MXene nanosheets

Changjae Lee, Soon Mo Park, Soobin Kim, Yun‐Seok Choi, Geonhyeong Park, Yun Chan Kang, Chong Min Koo, Seon Joon Kim, Dong Ki Yoon, Seon Joon Kim, Dong Ki Yoon

2022Nature Communications65 citationsDOIOpen Access PDF

Abstract

Abstract Controlling the orientation of two-dimensional materials is essential to optimize or tune their functional properties. In particular, aligning MXene, a two-dimensional carbide and/or nitride material, has recently received much attention due to its high conductivity and high-density surface functional group properties that can easily vary based on its arranged directions. However, erecting 2D materials vertically can be challenging, given their thinness of few nanometres. Here, vertical alignment of Ti 3 C 2 T x MXene sheets is achieved by applying an in-plane electric field, which is directly observed using polarised optical microscopy and scanning electron microscopy. The electric field-induced vertical alignment parallel to the applied alternating-current field is demonstrated to be reversible in the absence of a field, back to a random orientation distribution. Interdigitated electrodes with uniaxially aligned MXene nanosheets are demonstrated. These can be further modulated to achieve various patterns using diversified electrode substrates. Anisotropic electrical conductivity is also observed in the uniaxially aligned MXene nanosheet film, which is quite different from the randomly oriented ones. The proposed orientation-controlling technique demonstrates potential for many applications including sensors, membranes, polarisers, and general energy applications.

Topics & Concepts

Materials scienceElectric fieldNanosheetElectrodeNanotechnologyAnisotropyOptoelectronicsOrientation (vector space)ConductivityField (mathematics)OpticsGeometryPhysical chemistryPure mathematicsMathematicsPhysicsQuantum mechanicsChemistryMXene and MAX Phase Materials2D Materials and ApplicationsAdvanced Memory and Neural Computing