Quartz Crystal Microbalance Sensors: New
Tools for the Assessment of Organic Threats
to the Quality of Water
Bartolomeo Della Ventura, Marco Mauro, Raffaele Battaglia,
and Raffaele Velotta
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
2 Theory and Modeling of QCM Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
2.1 Sauerbrey’s Equation: Rigid Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
2.2 Sauerbrey’s Mass Sensitivity . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 319
2.3 Kanazawa: Gordon Equation: Quartz Crystal in Contact with a Liquid . . . . . . . . . . . . . 320
2.4 Small Load Approximation: The Electromechanical Model . . . . . . . . . . . . . . . . . . . . . . . . . 321
2.5 Semi-Infinite Viscoelastic Layer Newtonian Liquid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324
2.6 Purely Inertial Layer: Sauerbrey’s Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
2.7 Viscoelastic Layer of Arbitrary Thickness . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . 325
2.8 Viscoelastic Layer in Liquid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
3 QCM Detection Scheme and Electronic Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
3.1 Quartz Oscillators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
3.2 Network or Impedance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
3.3 Functionalization Methods of the QCM Gold Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
3.4 Detection Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
Abstract Water monitoring technologies are widely used for contaminant detection
in a wide variety of water ecology applications such as water treatment plants and
water distribution systems. A tremendous amount of research has been conducted over
the past decades to develop robust and efficient techniques of contaminant detection
with minimum operating cost and energy. Recent developments in spectroscopic
techniques and biosensor approach have improved the detection sensitivities,
B. Della Ventura and R. Velotta (*)
Department of Physics “E. Pancini”, Università di Napoli Federico II, Naples, Italy
e-mail: rvelotta@unina.it
M. Mauro and R. Battaglia
Novaetech S.r.l., Pompei, NA, Italy
Andrea Scozzari, Steve Mounce, Dawei Han, Francesco Soldovieri,
and Dimitri Solomatine (eds.), ICT for Smart Water Systems: Measurements and
Data Science, Hdb Env Chem (2021) 102: 315–342, https://doi.org/10.1007/698_2019_390,
© Springer Nature Switzerland AG 2019, Published online: 26 July 2019
315
Tools for the Assessment of Organic Threats
to the Quality of Water
Bartolomeo Della Ventura, Marco Mauro, Raffaele Battaglia,
and Raffaele Velotta
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
2 Theory and Modeling of QCM Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
2.1 Sauerbrey’s Equation: Rigid Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
2.2 Sauerbrey’s Mass Sensitivity . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 319
2.3 Kanazawa: Gordon Equation: Quartz Crystal in Contact with a Liquid . . . . . . . . . . . . . 320
2.4 Small Load Approximation: The Electromechanical Model . . . . . . . . . . . . . . . . . . . . . . . . . 321
2.5 Semi-Infinite Viscoelastic Layer Newtonian Liquid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324
2.6 Purely Inertial Layer: Sauerbrey’s Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
2.7 Viscoelastic Layer of Arbitrary Thickness . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . 325
2.8 Viscoelastic Layer in Liquid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
3 QCM Detection Scheme and Electronic Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
3.1 Quartz Oscillators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
3.2 Network or Impedance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
3.3 Functionalization Methods of the QCM Gold Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
3.4 Detection Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
Abstract Water monitoring technologies are widely used for contaminant detection
in a wide variety of water ecology applications such as water treatment plants and
water distribution systems. A tremendous amount of research has been conducted over
the past decades to develop robust and efficient techniques of contaminant detection
with minimum operating cost and energy. Recent developments in spectroscopic
techniques and biosensor approach have improved the detection sensitivities,
B. Della Ventura and R. Velotta (*)
Department of Physics “E. Pancini”, Università di Napoli Federico II, Naples, Italy
e-mail: rvelotta@unina.it
M. Mauro and R. Battaglia
Novaetech S.r.l., Pompei, NA, Italy
Andrea Scozzari, Steve Mounce, Dawei Han, Francesco Soldovieri,
and Dimitri Solomatine (eds.), ICT for Smart Water Systems: Measurements and
Data Science, Hdb Env Chem (2021) 102: 315–342, https://doi.org/10.1007/698_2019_390,
© Springer Nature Switzerland AG 2019, Published online: 26 July 2019
315