Tag Archive for: AWS Suite

Publication on AWSensors technology

Methods for Calibrating the Electrochemical Quartz Crystal Microbalance: Frequency to Mass and Compensation for Viscous Load

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Authors: Claes-Olof A. Olsson, Anna Neus Igual-Muñoz and Stefano Mischler

Journal: Chemosensors (2023)

 

Abstract

The main output from an Electrochemical Quartz Crystal Microbalance is a frequency shift. This note describes how to separate the mass- and viscous load contributions to this shift by a calibration procedure. The mass calibration is made by electroplating from a copper sulfate solution in ethanol/water with 100% current efficiency. An estimate of viscous load is obtained by measuring the energy dissipation and is related to frequency change using the Kanazawa–Gordon equation. Two approaches are discussed: either by performing calibration experiments in a series of water–glycerol mixtures or by following oscillations in frequency and dissipation by collecting data during the stabilization phase of the experiment.

 

You may read the full paper here.

Publication on AWSensors technology

Effect of Noise on Determining Ultrathin-Film Parameters from QCM-D Data with the Viscoelastic Model

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Authors: Diethelm Johannsmann, Arne Langhoff, Christian Leppin, Ilya Reviakine, and Anna M. C. Maan

Journal: Sensors (2023)

 

Abstract

Quartz crystal microbalance with dissipation monitoring (QCMD) is a well-established technique for studying soft films. It can provide gravimetric as well as nongravimetric information about a film, such as its thickness and mechanical properties. The interpretation of sets of overtone-normalized frequency shifts, Δf/n, and overtone-normalized shifts in half-bandwidth, ΔΓ/n, provided by QCMD relies on a model that, in general, contains five independent parameters that are needed to describe film thickness and frequency-dependent viscoelastic properties. Here, we examine how noise inherent in experimental data affects the determination of these parameters. There are certain conditions where noise prevents the reliable determination of film thickness and the loss tangent. On the other hand, we show that there are conditions where it is possible to determine all five parameters. We relate these conditions to the mathematical properties of the model in terms of simple conceptual diagrams that can help users understand the model’s behavior. Finally, we present new open source software for QCMD data analysis written in Python, PyQTM.

 

You may read the full paper here.

new analysis software

New analysis software

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December 28th 2016

A new full-featured analysis software is already available for AWS-A20 platforms. User will be able to make QCM experiments with or without electrochemistry in a very simple, fast and intuitive way because AWS Suite software has been specifically designed to ensure an optimal user experience.

It allows effortless management of multiple, different devices, including AWS A20 system, AWS F20 fluidics module and Bio-Logic potentiostats/galvanostats, from a single interface view. Furthermore, the software allows remote access to devices by IP address within the network.

Better data management

Good data management is crucial to ensure quality research. Therefore, new features have been implemented as a solution to match this need. AWS Suite organizes all the data in a simple file system based in the creation and management of projects. The software guides the user in creating and configuring QCM and eQCM experiments, as well as in the application of modeling tools for data analysis.

User-friendly displays and modelling tool

For a better user experience, improved graphic displays containing more graphic tools, automatic annotations and simple computations are featured, in addition to an electronic notebook available in every experiment to keep records of additional information within the experiment file.

Easy access to experiment configuration, data and visualization

AWS Suite allows the user to revisit experiment configuration, data and visualization in a straightforward manner, as well as to import the configuration of previous experiments to quickly reproduce them.

AWSensors developers team has made a huge effort to understand the workflow and needs of users so that the use of the new program is really intuitive, simple and fast.