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Innovative Network for Training in wAter and Food QUality monitoring using Autonomous SENSors and IntelligEnt Data Gathering and Analysis

Periodic Reporting for period 2 - AQUASENSE (Innovative Network for Training in wAter and Food QUality monitoring using Autonomous SENSors and IntelligEnt Data Gathering and Analysis)

Reporting period: 2020-10-01 to 2023-06-30

Prevention of water pollution is one of the major issues in the global environmental protection system. Water quality directly impacts our lives as drinks or food made from it affect our health. Over the last decade, water quality observing technology has risen to the challenge of scientists to identify and mitigate poor water quality by providing them with cost-effective tools that can take measurements of essential biogeochemical variables autonomously. Yet, despite these options becoming more readily available, there is a gap between the technology and the end-user (including the investigators and technicians that deploy these technologies) due to a collective lack of training, in-depth knowledge, and skilled workers who can meet new and emerging challenges. The traditional method of WQM, such as ‘sample collection at location and analysis in the lab’ causes multiple issues, including sensing data accuracy, processing time and low-frequency data collection. To overcome this, a connected networking sensing system with an advanced sensor deployment method is an important approach.

This AQUASENSE project aims to resolve the challenges in WQM and provide excellent training to 15 early-stage researchers (ESRs) in the field of water quality through autonomous sensors and autonomous deployment and soft skills. The ESRs trained through this project are expected to fill the skill gap and contribute towards strengthening Europe’s human resources and industry competitiveness in the strategic fields of aqua/agriculture and sensing technologies.
The major aim of the AQUASENSE project is to design a smart connected system for water and food quality monitoring. To achieve this, improved methods for data quality, data gathering, and data analysis, to monitor the water and aqua-food quality, are required. This was accomplished through: (a) the development of autonomous multi-sensory patches capable of measuring pH, DO, ammonia, nitrate, dissolved ions, pathogen conductivity, temperature, and pressure; (b) strategies for the intelligent deployment of sensors using autonomous robotic vehicles (ARVs), including under-water robots (UWRs) and unmanned aerial vehicles (UAVs) or drones; and (c) data analysis and prediction including using artificial intelligence (AI) tools . One major obstacle to overcome is the disconnect between data quality, data gathering by autonomous sensors and data analysis. To address this, sensors need to be deployed through buoys, boats, and other means to broaden data coverage in space and time.

As part of this project to design sensors, various materials were prepared, including metal oxides (such as RuO2, ZnO, CeO2, CoO2), conducting polymers, and carbon-based materials. Based on these materials, ESRs have completed the design and development of new sensors which include for pH, ammonia, nitrate, heavy metal ions, pathogens, pesticides, turbidity, and more. In this work, we developed both flexible and non-flexible sensors. To acquire sensor data, new electronic circuits were developed, along with 3D printing technologies for packaging and various chip bonding methods. To deploy sensors, researchers received training in underwater robotic and aerial vehicles. For better data analysis, wireless sensing networking protocols were designed, and new integration technologies were developed. As a joint collaborative effort, the researchers applied the sensors for food and water quality monitoring in real-time.
Compared with the present works in water quality monitoring, the researchers made significant progress in three aspects. NO2- sensors

Sensors and Materials: Various activities were carried out including (i) the synthesis of materials and development of multifunctional sensors for detection of lead, NH3, nitrate, and pH in water samples; (ii) a new methodology for fabricating and functionalizing Ag NP surfaces with polysaccharides using electrochemical deposition technique for Surface Enhanced Raman Spectroscopy (SERS) sensors; (iii) electrochemical sensors for detecting bacteria in water and food; (vi) and development of physical parameters monitoring sensors (pressure, humidity, temperature) on a flexible substrate and assessing the disposability of the prepared sensors.

Electronics and Sensors Deployment: AQUASENSE project implemented new approach in electronics circuit design and deployment which include (i) designing a time domain potentiostatic readout out IC to simultaneously detect and transmit dissolved oxygen (DO) concentration and temperature; (ii) developing conductive structures on various flexible substrates, ink-jet printing of functional layers, UV-curing processes of metal inks, and metal layers on low-temperature substrates as well as bonding and accurate placement of ultra-thin Chips for hybrid integration (iii) developing a USV-towable platform to operate the sensors, (iv) and inferring the concentration of suspended solids within inland waters using multispectral cameras attached to drones, and mapping remote sensing reflectance across an inland lake with a low-cost airborne drone.

Sensors Data Analysis and Applications: Various activities carried out for testing and application. This include (i) formulating the specifications and procedures concerning RuO2-Nf sensors in food samples; (ii) developing electrochemical sensors for detection of copper ions, cobalt ions in water, results from Arduino readout and ascorbic acid in fruits and vegetables, (iii) developing machine learning model to predict the incoming harvesting energy, which enables the receiver node to perform more aggressively when it has a sufficient inflow of incoming harvested energy to improve the performance.

The ESRs were actively involved in various project activities, including sensor design and development, data acquisition, and data analysis. They were also actively disseminating the project through international conferences, workshops, public engagement activities. This includes presenting research findings, demonstrating sensor prototypes, and engaging with stakeholders to promote awareness and understanding of the project goals and outcomes. The ESRs have also been involved in organizing and participating in outreach activities, such as STEM events, to inspire and engage the public in the field of water and food quality monitoring. The major impact of the project are (i) four ESRs defended their PhD thesis (ii) 52 Journal articles were published in high impact journals including Advanced Materials, Progress in Material Science, IEEE Internet of Things Journal, Sensors and Actuator B - Chemical, etc. (iii) Consortium members organised an edited book with 9 chapters from ESRs and from other leading researchers. Book published by IEEE Sensors+ Wiley with title of ‘Sensing Technologies for Real Time Monitoring of Water Quality’ (iv) 17 Peer-reviewed conference proceedings published (v) ESRs received multiple awards including 1st Prize and Audience Award, Falling Walls Lab Warsaw, Enterprise Ireland Innovation Arena Awards - Research Emerging from a 3rd Level Award, WiSe/ YP Big Idea Pitch – APSCON 2023 - Best Innovation Pitch, Pre-Commercialisation Programme 2023 - Best Pitch, UCC research awards - UCC Innovator of the Year (Physical Sciences) and ESR7 awarded a HiSilicon-sponsored tape-out in TSMC 180nm technology node.
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