4.6 Article

Sorption-induced static mode nanomechanical sensing with viscoelastic receptor layers for multistep injection-purge cycles

期刊

JOURNAL OF APPLIED PHYSICS
卷 129, 期 12, 页码 -

出版社

AIP Publishing
DOI: 10.1063/5.0039045

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资金

  1. JST CREST [JPMJCR1665]
  2. MEXT, Japan [18H04168]
  3. Public/Private R&D Investment Strategic Expansion Program (PRISM), Cabinet Office, Japan
  4. Izumi Science and Technology Foundation [2020-J-070]
  5. ICYS, NIMS
  6. CFSN, NIMS
  7. World Premier International Research Center Initiative (WPI) on Materials Nanoarchitectonics (MANA), NIMS
  8. [20K20554]

向作者/读者索取更多资源

Nanomechanical sensors and arrays attract attention for detecting, distinguishing, and identifying target analytes. An analytical model of viscoelastic material-based nanomechanical sensing is derived by extending the theoretical model and solving differential equations with recurrence relations. The model accurately predicts entire signal responses by extracting viscoelastic properties of materials and concentrations of analytes.
Nanomechanical sensors and their arrays have been attracting significant attention for detecting, distinguishing, and identifying target analytes. In the static mode operation, sensing signals are obtained by a concentration-dependent sorption-induced mechanical strain/stress. The analytical models for the static mode nanomechanical sensing with viscoelastic receptor layers have been proposed, while they are not formulated for practical conditions, such as multistep injection-purge cycles. Here, we derive an analytical model of viscoelastic material-based nanomechanical sensing by extending the theoretical model via solving differential equations with recurrence relations. The presented model is capable of reproducing the transient behaviors observed in the experimental signal responses with multistep injection-purge cycles, including drifts and/or changes in the baseline. Moreover, this model can be utilized for extracting viscoelastic properties of the receptor material/analyte pairs as well as the concentrations of analytes accurately by fitting a couple of injection-purge curves obtained from the experimental data. The parameters of the model that best fit the data can be used for predicting the entire signal response. (c) 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.1063/5.0039045

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