Chain reaction final report

Chain Reaction was a three-year project funded by the European Commission. Its key aim was to develop Inquiry Based Science Education (IBSE) across twelve partner countries—United Kingdom, Italy, Slovakia, Turkey, Bulgaria, France, Slovenia, Germany, Greece, Ireland, Jordan, Georgia. The key underpinning element of the project was the development and deployment of interactive and engaging professional development for science teacher educators from each participating country. The science teacher educators involved were introduced to ‘tried and tested’ inquiry-themed science resources and worked collaboratively with project members to gain a clear understanding of the philosophy and mechanisms involved in designing and facilitating inquiry in the science classroom. Once fully cognisant in the use of the resources the science teacher educators, from each partner, designed and delivered a dedicated professional development course for participating science teachers. The specific nature of each professional development event varied from partner to partner but was consistent in its aim to develop participating teachers’ confidence and skills in using the resources with their science students. Following the professional development sessions in each country, science teachers were able to deliver a series of inquiry-based sessions

New trends on photoelectrocatalysis (PEC):nanomaterials, wastewater treatment and hydrogen generation

The need for novel water treatment technologies has been recently recognised as concerning contaminants (organics and pathogens) are resilient to standard technologies. Advanced oxidation processes degrade organics and inactivate microorganisms via generated reactive oxygen species (ROS). Among them, heterogeneous photocatalysis may have reduced efficiency due to, fast electron-hole pair recombination in the photoexcited semiconductor and reduced effective surface area of immobilised photocatalysts. To overcome these, the process can be electrically assisted by using an external bias, an electrically conductive support for the photocatalyst connected to a counter electrode, this is known as photoelectrocatalysis (PEC). Compared to photocatalysis, PEC increases the efficiency of the generation of ROS due to the prevention of charge recombination between photogenerated electron-hole pairs thanks the electrical bias applied. This review presents recent trends, challenges, nanomaterials and different water applications of PEC (degradation of organic pollutants, disinfection and generation of hydrogen from wastewater)

Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods

Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs), in which the random nature of the tool and inconsistent manufacturing methods create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool’s life being underutilised due to premature replacement. Acoustic Emission (AE) in conjunction with Machine Learning (ML) models presents a possible on-machine monitoring technique which could be used as a prediction method for DCB wear. Four wear life tests were conducted with a ∅1.3 mm #1000 DCB until failure, in which AE was continuously acquired during grinding passes, followed by surface measurements of the DCB. Three ML model architectures were trained on AE features to predict DCB mean radius, an indicator of overall tool wear. All architectures showed potential of learning from the dataset, with Long Short-Term Memory (LSTM) models performing the best, resulting in prediction error of MSE = 0.559 μm2 after optimisation. Additionally, links between AE kurtosis and the tool’s run-out/form error were identified during an initial review of the data, showing potential for future work to focus on grinding effectiveness as well as overall wear. This paper has shown that AE contains sufficient information to enable on-machine monitoring of DCBs during the grinding process. ML models have been shown to be sufficiently precise in predicting overall DCB wear and have the potential of interpreting grinding condition