3D printed fiber-optic nanomechanical bioprobe
Source abstract
Abstract
Abstract Ultrasensitive nanomechanical instruments, e.g. atomic force microscopy (AFM), can be used to perform delicate biomechanical measurements and reveal the complex mechanical environment of biological processes. However, these instruments are limited because of their size and complex feedback system. In this study, we demonstrate a miniature fiber optical nanomechanical probe (FONP) that can be used to detect the mechanical properties of single cells and in vivo tissue measurements. A FONP that can operate in air and in liquids was developed by programming a microcantilever probe on the end face of a single-mode fiber using femtosecond laser two-photon polymerization nanolithography. To realize stiffness matching of the FONP and sample, a strategy of customizing the microcantilever’s spring constant according to the sample was proposed based on structure-correlated mechanics. As a proof-of concept, three FONPs with spring constants varying from 0.421 N m−1 to 52.6 N m−1 by more than two orders of magnitude were prepared. The highest microforce sensitivity was 54.5 nm μN−1 and the detection limit was 2.1 nN. The Young’s modulus of heterogeneous soft materials, such as polydimethylsiloxane, muscle tissue of living mice, onion cells, and MCF-7 cells, were successfully measured, which validating the broad applicability of this method. Our strategy provides a universal protocol for directly programming fiber-optic AFMs. Moreover, this method has no special requirements for the size and shape of living biological samples, which is infeasible when using commercial AFMs. FONP has made substantial progress in realizing basic biological discoveries, which may create new biomedical applications that cannot be realized by current AFMs.
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Research context
OpenAlex topics: Force Microscopy Techniques and Applications · Mechanical and Optical Resonators · Cellular Mechanics and Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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