A framework for using real data with distributed low cost sensors

Currently, due to the widespread use of computers and the internet, students are trading libraries for the World Wide Web and laboratories with simulation programs. In most courses, simulators are made available to students and can be used to proof theoretical results or to test a developing hardware/product. Although this is an interesting solution: low cost, easy and fast way to perform some courses work, it has indeed major disadvantages. As everything is currently being done with/in a computer, the students are loosing the “feel” of the real values of the magnitudes. For instance in engineering studies, and mainly in the first years, students need to learn electronics, algorithmic, mathematics and physics. All of these areas can use numerical analysis software, simulation software or spreadsheets and in the majority of the cases data used is either simulated or random numbers, but real data could be used instead. For example, if a course uses numerical analysis software and needs a dataset, the students can learn to manipulate arrays. Also, when using the spreadsheets to build graphics, instead of using a random table, students could use a real dataset based, for instance, in the room temperature and its variation across the day. In this work we present a framework which uses a simple interface allowing it to be used by different courses where the computers are the teaching/learning process in order to give a more realistic feeling to students by using real data. A framework is proposed based on a set of low cost sensors for different physical magnitudes, e.g. temperature, light, wind speed, which are connected to a central server, that the students have access with an Ethernet protocol or are connected directly to the student computer/laptop. These sensors use the communication ports available such as: serial ports, parallel ports, Ethernet or Universal Serial Bus (USB). Since a central server is used, the students are encouraged to use sensor values results in their different courses and consequently in different types of software such as: numerical analysis tools, spreadsheets or simply inside any programming language when a dataset is needed. In order to do this, small pieces of hardware were developed containing at least one sensor using different types of computer communication. As long as the sensors are attached in a server connected to the internet, these tools can also be shared between different schools. This allows sensors that aren't available in a determined school to be used by getting the values from other places that are sharing them. Another remark is that students in the more advanced years and (theoretically) more know how, can use the courses that have some affinities with electronic development to build new sensor pieces and expand the framework further. The final solution provided is very interesting, low cost, simple to develop, allowing flexibility of resources by using the same materials in several courses bringing real world data into the students computer works.

Damage assessment of fibre reinforced laminates

The use of solid wastes and natural fibres to the production of innovative composites is now a matter of environmental need. A set of hybrid composites constituted of waste rubber particles and sugarcane bagasse fibres into a thermoset composite material are drilled to determine the effect of rubber particle addition and size, sugarcane fibre addition and length and fibre chemical treatment on damage extension and related mechanical properties. Damage extension is determined by enhanced radiography for further damage measurement – diameter and areas – and correlated with mechanical test results — bearing test. The results demonstrated significant effect of the rubber and fibre additions on mechanical properties of the composites.

MnFe2O4@CNT-N as novel electrochemical nanosensor for determination of caffeine, acetaminophen and ascorbic acid

For the first time, a glassy carbon electrode (GCE) modified with novel N-doped carbon nanotubes (CNT-N) functionalized with MnFe2O4 nanoparticles (MnFe2O4@CNT-N) has been prepared and applied for the electrochemical determination of caffeine (CF), acetaminophen (AC) and ascorbic acid (AA). The electrochemical behaviour of CF, AC and AA on the bare GCE, CNT-N/GCE and MnFe2O4@CNT-N/GCE were carefully investigated using cyclic voltammetry (CV) and square-wave voltammetry (SWV). Compared to bare GCE and CNT-N modified electrode, the MnFe2O4@CNT-N modified electrode can remarkably improve the electrocatalytic activity towards the oxidation of CF, AC and AA with an increase in the anodic peak currents of 52%, 50% and 55%, respectively. Also, the SWV anodic peaks of these molecules could be distinguished from each other at the MnFe2O4@CNT-N modified electrode with enhanced oxidation currents. The linear response ranges for the square wave voltammetric determination of CF, AC and AA were 1.0 × 10−6 to 1.1 × 10−3 mol dm−3, 1.0 × 10−6 to 1.0 × 10−3 mol dm−3 and 2.0 × 10−6 to 1.0 × 10−4 mol dm−3 with detection limit (S/N = 3) of 0.83 × 10−6, 0.83 × 10−6 and 1.8 × 10−6 mol dm−3, respectively. The sensitivity values at the MnFe2O4@CNT-N/GCE for the individual determination of AC, AA and CF and in the presence of the other molecules showed that the quantification of AA and CF show no interferences from the other molecules; however, AA and CF interfered in the determination of AC, with the latter molecule showing the strongest interference. Nevertheless, the obtained results show that MnFe2O4@CNT-N composite material acted as an efficient electrochemical sensor towards the selected biomolecules.

Molecularly imprinted sensor for voltammetric detection of norfloxacin

In this work, a norfloxacin selective modified glassy carbon electrode (GCE) based on a molecularly imprinted polymer (MIP) as electrochemical sensor was developed. A suspension of multi-walled carbon nanotubes (MWCNTs) was deposited on the electrode surface. Subsequently, a molecularly imprinted film was prepared by electropolymerization, via cyclic voltammetry of pyrrole (PPy) in the presence of norfloxacin (NFX) as the template molecule. A control electrode (NIP) was also prepared. Scanning electron microscopy (SEM) and cyclic voltammetry in a ferrocyanide solution were performed for morphological and electrochemical characterisation, respectively. Several experimental parameters were studied and optimised. For quantification purposes the MIP/MWCNT/GCE was immersed in NFX solutions for 10 min, and the detection was performed in voltammetric cell by square wave voltammetry. The proposed sensor presented a linear behaviour, between peak current intensity and logarithmic concentration of NFX between 1 × 10−7 and 8 × 10−6 M. The obtained results presented good precision, with a repeatability of 4.3% and reproducibility of 9% and the detection limit was 4.6 × 10−8 M (S/N = 3). The developed sensor displayed good selectivity and operational lifetime, is simple to fabricate and easy to operate and was successfully applied to the analysis of NFX in urine samples.